• Skip to content
  • Accessibility help

Currently viewing BNF . BNF switch to BNFC

Cardiovascular disease risk assessment and prevention

Description of condition.

Cardiovascular disease (CVD) is a term that describes a group of disorders of the heart and blood vessels caused by atherosclerosis and thrombosis, which includes coronary heart disease, stroke, peripheral arterial disease, and aortic disease.

The risk of CVD is greater in men, patients with a family history of CVD, and in certain ethnic backgrounds such as South Asians. CVD risk is also greater in patients aged over 50 years and increases with age; patients aged 85 years and over are at particularly high risk. CVD has several important and potentially modifiable risk factors such as hypertension, abnormal lipids, obesity, diabetes mellitus, and psychosocial factors such as depression, anxiety, and social isolation. Low physical activity, poor diet, smoking, and excessive alcohol intake are also modifiable risk factors.

Aims of treatment

The overall aim of treatment is to prevent the occurrence of a cardiovascular event by reducing modifiable risk factors through lifestyle changes and drug management.

Cardiovascular disease risk assessment

Recommendations on cardiovascular disease (CVD) risk assessment are from the NICE—Cardiovascular disease: risk assessment and reduction, including lipid modification guideline (CG181, 2016) , and SIGN—Risk estimation and the prevention of cardiovascular disease guideline (SIGN 149, 2017) . SIGN 149 uses risk assessment strategies outlined by the Joint British Society (JBS)—Joint British Societies' consensus recommendations for the prevention of cardiovascular disease (2014) . Recommendations where NICE and SIGN differ have been highlighted.

NICE (2016) recommends that for primary prevention of CVD in primary care, a systematic approach be used to identify those who are likely to be at high risk. Patients should be prioritised based on an estimate of their CVD risk using risk factors already recorded in their medical records, before a full formal risk assessment. Priority for a full formal risk assessment should be given to patients with an estimated 10-year risk of 10% or more. Patients aged over 40 years should have their estimate of CVD risk reviewed on an ongoing basis. SIGN (2017) instead recommends that CVD risk assessments are offered at least every 5 years to all patients aged 40 years and over with no history of CVD, familial hypercholesterolaemia, chronic kidney disease or diabetes and who are not receiving treatment to reduce blood pressure or lipids. As well as to patients with a first-degree relative who has premature atherosclerotic CVD or familial dyslipidaemia, regardless of their age. A Strength of recommendation: High

Risk assessment with a calculator is not required in patients who are at increased or high risk of CVD. This includes those with established CVD, chronic kidney disease stage 3 or higher (eGFR <60 mL/minute/1.73 m 2 ), albuminuria, or familial hypercholesterolaemia. In addition to these patients, NICE (2016) does not recommend the use of a risk calculator in patients with other hereditary disorders of lipid metabolism, or type 1 diabetes mellitus. Whereas SIGN (2017) does not recommend the use of a risk calculator in patients with diabetes mellitus aged 40 years and over, and in those aged under 40 years with diabetes mellitus who have either had it for more than 20 years, present with target organ damage (such as proteinuria, albuminuria, proliferative retinopathy, or autonomic neuropathy), or have other significantly elevated cardiovascular risk factors. A Strength of recommendation: High

Risk calculators

Cardiovascular risk assessment calculators are used to predict the approximate likelihood of a cardiovascular event occurring over a given period of time. Standard risk scores may be underestimated in patients with additional risk due to existing conditions or medications that can cause dyslipidaemia (e.g. antipsychotics, corticosteroids, or immunosuppressants). CVD risk may also be underestimated in patients who are already taking antihypertensives or lipid-regulating drugs, or who have recently stopped smoking. Interpretation of risk scores as well as the need for further management of risk factors in those who fall below the CVD risk threshold, should always reflect informed clinical judgement. A Strength of recommendation: High

QRISK ® 2 and JBS3

QRISK ® 2 and JBS3 risk calculators are used to assess CVD risk for patients in England and Wales. Both tools assess cardiovascular risk of coronary heart disease (angina and myocardial infarction), stroke, and transient ischaemic attack. This is based on lipid profile, systolic blood pressure, sex, age, ethnicity, smoking status, BMI, chronic kidney disease (stage 4 or above), diabetes mellitus, atrial fibrillation, treated hypertension, rheumatoid arthritis, social deprivation, or a family history of premature CVD.

The QRISK ® 2 risk calculator ( https://www.qrisk.org/ ) is recommended by NICE clinical guideline 181 , and the JBS3 risk calculator ( http://www.jbs3risk.com/pages/risk_calculator.htm ) is endorsed by the Joint British Society.

QRISK ® 3 is an updated version of the QRISK ® 2 risk calculator. It considers additional risk factors such as chronic kidney disease (stage 3 or above), migraine, corticosteroid use, systemic lupus erythematosus, atypical antipsychotics use, severe mental illness, erectile dysfunction, and a measure of systolic blood pressure variability.

The JBS3 calculator is not only able to estimate short term (10-year) risk, but also lifetime risk of CVD events. Patients with a 10-year risk of CVD of less than 10% may benefit from an assessment of their lifetime risk using the JBS3 tool, and a discussion on the impact of lifestyle interventions and, if necessary, drug therapy.

The ASSIGN cardiovascular risk assessment calculator is tailored to the Scottish population and uses factors such as age, sex, smoking, systolic blood pressure, lipid profile, family history of premature CVD, diabetes mellitus, rheumatoid arthritis, and social deprivation to estimate cardiovascular risk. Other risk factors not included in this CVD risk assessment calculator (such as ethnicity, BMI, atrial fibrillation, psychological wellbeing, and physical inactivity) should also be taken into account when assessing and managing the patient's overall CVD risk. A Strength of recommendation: High

SIGN (2017) recommends that asymptomatic patients without established CVD (or other conditions that are automatically associated with a high CVD risk) should be considered at high risk if they are assessed as having a 20% or more risk of a first cardiovascular event within 10 years. A Strength of recommendation: High

The online calculator tool can be found at http://www.assign-score.com/estimate-the-risk/ . Full details can be found in the SIGN 149 clinical guideline (see Useful resources ).

Cardiovascular disease prevention

Recommendations on cardiovascular disease (CVD) prevention are from the NICE—Cardiovascular disease: risk assessment and reduction, including lipid modification guideline (CG181, 2016) , and SIGN—Risk estimation and the prevention of cardiovascular disease guideline (SIGN 149, 2017) . SIGN 149 uses strategies outlined by the Joint British Society (JBS)—Joint British Societies' consensus recommendations for the prevention of cardiovascular disease (2014) . Recommendations where NICE and SIGN differ have been highlighted.

All patients at any risk of CVD should be advised to make lifestyle modifications that may include beneficial changes to diet (such as increasing fruit and vegetable consumption, reducing saturated fat and dietary salt intake), increasing physical exercise, weight management, reducing alcohol consumption, and Smoking cessation . An annual review should be considered to discuss lifestyle modification, medication adherence and risk factors. The frequency of review may be tailored to the individual. A Strength of recommendation: High

Further preventative measures with drug treatment should be taken in individuals with a high risk of developing CVD (primary prevention), and to prevent recurrence of events in those with established CVD (secondary prevention). A Strength of recommendation: High

Primary prevention

Antiplatelet therapy.

Aspirin is not recommended for primary prevention of CVD due to the limited benefit gained versus risk of side-effects such as bleeding. A Strength of recommendation: High

Antihypertensive therapy

Antihypertensive drug treatment should be offered to patients who are at high risk of CVD and have sustained elevated systolic blood pressure and/or diastolic blood pressure. For further guidance on prescribing antihypertensive drugs in patients without symptomatic CVD and for specific groups at high cardiovascular risk, see Hypertension . A Strength of recommendation: High

Lipid-lowering therapy

A statin is recommended as the lipid-lowering drug of choice for primary prevention of CVD. All modifiable risk factors, comorbidities and secondary causes of dyslipidaemia (e.g. uncontrolled diabetes mellitus, hepatic disease, nephrotic syndrome, excessive alcohol consumption, and hypothyroidism) should be managed before starting treatment with a statin. Factors such as polypharmacy, frailty, and comorbidities should be taken into account before starting statin therapy. A Strength of recommendation: High

NICE (2016) recommends low-dose atorvastatin for patients who have a 10% or greater 10-year risk of developing CVD (using the QRISK2 calculator), and for patients with chronic kidney disease. Low-dose atorvastatin should be considered in all patients with type 1 diabetes mellitus, and be offered to patients with type 1 diabetes who are either aged over 40 years, have had diabetes for more than 10 years, have established nephropathy, or have other CVD risk factors. Patients aged 85 years and over may also benefit from low-dose atorvastatin to reduce their risk of non-fatal myocardial infarction. SIGN (2017) recommends low-dose atorvastatin for patients who are considered to be at high risk of CVD and not on dialysis. A Strength of recommendation: High

Patients taking statins should have an annual medication review to discuss medication adherence, lifestyle modification, CVD risk factors, and non-fasting, non-HDL cholesterol concentration (if testing deemed appropriate). Total cholesterol, HDL-cholesterol, and non-HDL-cholesterol concentrations should be checked 3 months after starting treatment with a high-intensity statin. For statin intensity categorisation, see Dyslipidaemias . A Strength of recommendation: High

Aiming for a reduction in non-HDL-cholesterol concentration of greater than 40% is recommended. If this is not achieved, adherence to drug treatment should be checked and lifestyle modifications optimised. In patients deemed to be at a higher risk due to comorbidities, risk score, or clinical judgement, an increase in the statin dose should be considered. NICE (2016) recommends that the use of higher doses in those with chronic kidney disease with an eGFR less than 30ml/minute/1.73 m2 should be discussed with a renal specialist. A Strength of recommendation: High

Specialist advice should be sought regarding treatment options in patients at high risk of CVD who are intolerant of three different statins. A Strength of recommendation: High

SIGN (2017) recommends that ezetimibe and bile acid sequestrants such as colestyramine and colestipol hydrochloride only be considered for primary prevention in patients with an elevated cardiovascular risk in whom statin therapy is contraindicated, and in patients with familial hypercholesterolaemia. However, NICE (2016) recommends that ezetimibe be considered for primary hypercholesterolaemia as monotherapy when statins are unsuitable or not tolerated, or in combination with a statin for patients in whom the maximum tolerated dose of initial statin therapy fails to adequately control total or non-HDL-cholesterol levels and an alternative statin is being considered. NICE (2016) does not recommend bile acid sequestrants for primary prevention of CVD. A Strength of recommendation: High

Although fibrates are not routinely recommended for primary prevention of CVD, SIGN (2017) recommends that they be considered in patients with a combination of high CVD risk, marked hypertriglyceridaemia and low HDL-cholesterol concentration. A Strength of recommendation: High

For other lipid-lowering therapy options, see SIGN guideline: Risk estimation and the prevention of cardiovascular disease and NICE guideline: Cardiovascular disease: risk assessment and reduction, including lipid modification (see Useful resources ).

Lipid-lowering therapy recommendations from Joint British Societies' consensus (2014), NICE Clinical guideline 181 (2016), and SIGN Clinical guideline 149 (2017) all differ in certain respects for prevention of CVD in patients with diabetes mellitus—see individual guidelines for further details.

For further information on lipid-lowering therapy and familial hypercholesterolaemia, see Dyslipidaemias .

Secondary prevention

Antiplatelet therapy with low-dose daily aspirin should be offered to patients with established atherosclerotic disease. Alternatively, clopidogrel can be considered in patients who are intolerant to aspirin or in whom it is contraindicated. A Strength of recommendation: High

For guidance on the use of antiplatelet drugs in patients with a history of stroke or transient ischaemic attack, see Stroke . For guidance on the use of antiplatelet drugs in patients with acute coronary syndrome, see Acute coronary syndromes .

Antihypertensive drug treatment is recommended in patients with established CVD and sustained elevated systolic blood pressure and/or diastolic blood pressure. For further guidance on prescribing antihypertensive drugs in patients with CVD, see Hypertension . A Strength of recommendation: High

A statin is recommended as the lipid-lowering drug of choice for secondary prevention of CVD. Factors such as polypharmacy, frailty, and comorbidities should be taken into account before starting statin therapy. A Strength of recommendation: High

Treatment with high-dose atorvastatin should be offered to patients with established atherosclerotic CVD. However, a lower dose can be used if the patient is at an increased risk of side-effects or drug interactions. NICE (2016) recommends that low-dose atorvastatin should be offered to patients with established CVD and chronic kidney disease. A Strength of recommendation: High

High-dose simvastatin is generally avoided due to the risk of myopathy, unless the patient has been stable on this regimen for at least one year. A Strength of recommendation: High Furthermore, the MHRA advises that high-dose simvastatin should only be considered for patients who have not achieved their treatment goals with lower doses and have severe hypercholesterolaemia and high risk of cardiovascular complications; benefits should outweigh risks.

Patients taking statins should have an annual medication review to discuss medication adherence, lifestyle modification, CVD risk factors, and non-fasting, non-HDL-cholesterol concentrations (if testing deemed appropriate). Total cholesterol, HDL-cholesterol, and non-HDL-cholesterol concentrations should be checked 3 months after starting treatment with a high-intensity statin. Patients who are stable on a low or medium-intensity statin should discuss the benefits and risks of switching to a high-intensity statin at their next medication review. For statin intensity categorisation, see Dyslipidaemias . A Strength of recommendation: High

Aiming for a reduction in non-HDL-cholesterol concentration of greater than 40% is recommended. A Strength of recommendation: High JBS3 instead recommends a target non-HDL-cholesterol concentration below 2.5 mmol/litre. This level is also recommended in patients with ischaemic stroke or transient ischaemic attack and evidence of artherosclerosis in SIGN and Royal College of Physicians' National Clinical Guideline for Stroke for the UK and Ireland (2023) . A Strength of recommendation: High

If these targets are not achieved, adherence to drug treatment should be checked and lifestyle modifications optimised. In patients judged to be at a higher risk because of comorbidities, risk score, or clinical judgement, an increase in the statin dose (if started on less than maximum atorvastatin dose) should be considered. NICE (2016) recommends that the use of higher doses in those with chronic kidney disease with an eGFR less than 30ml/minute/1.73 m 2 should be discussed with a renal specialist. A Strength of recommendation: High

Specialist advice should be sought regarding treatment options in patients with existing CVD who are intolerant of three different statins. A Strength of recommendation: High

SIGN (2017) recommends that ezetimibe and bile acid sequestrants such as colestyramine and colestipol hydrochloride , can be considered for use in combination with a statin at the maximum tolerated dose if LDL-cholesterol remains inadequately controlled. NICE (2016) recommends ezetimibe for primary hypercholesterolaemia as monotherapy when statins are unsuitable or not tolerated, or in combination with a statin for patients in whom the maximum tolerated dose of initial statin therapy fails to adequately control total or non-HDL-cholesterol levels and an alternative statin is being considered. NICE (2016) does not recommend bile acid sequestrants for the secondary prevention of CVD. A Strength of recommendation: High

Icosapent ethyl is recommended in combination with a statin for patients with established CVD who have a raised fasting triglyceride concentration of 1.7 mmol/litre or above, and a LDL-cholesterol concentration above 1.04 mmol/litre and below or equal to 2.6 mmol/litre. A Strength of recommendation: High

Although fibrates are not routinely recommended for secondary prevention of CVD, SIGN (2017) recommends that they be considered in patients with both marked hypertriglyceridaemia and low HDL-cholesterol concentrations. A Strength of recommendation: High

Psychological risk factors

Psychological treatment should be considered in patients with mood and anxiety disorders and comorbid CVD; complex patients may require referral to mental health services for assessment and delivery of high-intensity or specialist treatments. Selective serotonin re-uptake inhibitors (SSRIs) should be considered for treatment in patients with depression and coronary heart disease. A Strength of recommendation: High For guidance on prescribing of antidepressant drugs, see Antidepressant drugs .

Useful resources

Risk estimation and the prevention of cardiovascular disease. Scottish Intercollegiate Guidelines Network. Clinical guideline 149. June 2017. https://www.sign.ac.uk/our-guidelines/risk-estimation-and-the-prevention-of-cardiovascular-disease/

Cardiovascular disease: risk assessment and reduction, including lipid modification. National Institute for Health and Care Excellence. Clinical guideline CG181. July 2014 (updated September 2016). https://www.nice.org.uk/guidance/cg181

Joint British Societies’ consensus recommendations for the prevention of cardiovascular disease (JBS3). April 2014. http://www.jbs3risk.com/pages/report.htm

Related drugs

  • Atorvastatin
  • Clopidogrel
  • Colestipol hydrochloride
  • Colestyramine
  • Icosapent ethyl
  • Simvastatin

Related treatment summaries

  • Acute coronary syndromes
  • Antidepressant drugs
  • Antiplatelet drugs
  • Diabetic complications
  • Dyslipidaemias
  • Hyperparathyroidism
  • Hypertension
  • Polycystic ovary syndrome
  • Smoking cessation
  • Stable angina
  • Type 2 diabetes

The content on the NICE BNF site (BNF) is the copyright of BMJ Publishing Group Ltd and the Royal Pharmaceutical Society of Great Britain. By using BNF, you agree to the licence set out in the BNF Publications End User Licence Agreement .

  • Skip to navigation
  • Skip to main content

ASSIGN Score – prioritising prevention of cardiovascular disease

  • Estimate the risk
  • Accessibility
  • Authors & Copyright
  • About ASSIGN
  • For Beginners

Introduction

  • Development

Funding & Sponsorship

Assign for professionals.

You are encouraged to look at the For Beginners section as well, as it answers some FAQ s which are not dealt with below. There are also ‘notes’ and ‘further info’ linked to the webpages entitled ‘Estimate the risk’

ASSIGN is a cardiovascular risk score. The score was developed to prioritise the prevention of cardiovascular disease in those currently free of it by identifying those...

Development of the ASSIGN score

The ASSIGN score was developed in the summer of 2006 by Professor Hugh Tunstall-Pedoe and Professor Mark Woodward based at the University of Dundee, Scotland, working with the SIGN (Scottish Intercollegiate Guidelines Network) group on cardiovascular...

The ASSIGN score was developed in consultation with, and under the aegis of the Scottish Intercollegiate...

Scottish Heart Health Extended Cohort (SHHEC)

SHHEC is a large representative cohort of men and women recruited across Scotland in 1984-1987 reinforced with repeated random samples from north...

Scottish Intercollegiate Guidelines Network (SIGN)

This is an internationally respected Scottish national body. It has developed and published, revised, republished (and in some cases withdrawn) numerous...

Estimate the risk of developing cardiovascular disease over ten years using the ASSIGN score, by entering personal details and clicking on calculate.

E-mail us with any comments or questions

  • About the ASSIGN score
  • About use of this website
  • About implementation and policy issues

Site Information

  • Other SHOW websites
  • NHS Scotland
  • Healthier Scotland
  • University of Dundee
  • © Copyright ASSIGN Score 2008. Version 1.5.1 March 2014 incorporates SIMD 2012 and rheumatoid arthritis. (Minor update not affecting calculation algorithm.)
  • Back to top

Log in using your username and password

  • Search More Search for this keyword Advanced search
  • Latest content
  • Current issue
  • Browse by collection
  • BMJ Journals More You are viewing from: Google Indexer

You are here

  • Volume 93, Issue 2
  • Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC)
  • Article Text
  • Article info
  • Citation Tools
  • Rapid Responses
  • Article metrics

Download PDF

  • Mark Woodward 1 ,
  • Peter Brindle 2 ,
  • Hugh Tunstall-Pedoe 1 ,
  • for the SIGN group on risk estimation*
  • 1 Cardiovascular Epidemiology Unit, Institute of Cardiovascular Research, University of Dundee, Ninewells Hospital, Dundee, Scotland, UK
  • 2 Bristol Teaching Primary Care Trust and Department of Social Medicine, University of Bristol, Bristol, UK
  • Correspondence to: Professor Hugh Tunstall-Pedoe Cardiovascular Epidemiology Unit, Institute of Cardiovascular Research, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, Scotland, UK; h.tunstallpedoe{at}dundee.ac.uk

Objective: To improve equity in cardiovascular disease prevention by developing a cardiovascular risk score including social deprivation and family history.

Design: The ASSIGN score was derived from cardiovascular outcomes in the Scottish Heart Health Extended Cohort (SHHEC). It was tested against the Framingham cardiovascular risk score in the same database.

Setting: Random-sample, risk-factor population surveys across Scotland 1984–87 and North Glasgow 1989, 1992 and 1995.

Participants: 6540 men and 6757 women aged 30–74, initially free of cardiovascular disease, ranked for social deprivation by residence postcode using the Scottish Index of Multiple Deprivation (SIMD) and followed for cardiovascular mortality and morbidity through 2005.

Results: Classic risk factors, including cigarette dosage, plus deprivation and family history but not obesity, were significant factors in constructing ASSIGN scores for each sex. ASSIGN scores, lower on average, correlated closely with Framingham values for 10-year cardiovascular risk. Discrimination of risk in the SHHEC population was significantly, but marginally, improved overall by ASSIGN. However, the social gradient in cardiovascular event rates was inadequately reflected by the Framingham score, leaving a large social disparity in future victims not identified as high risk. ASSIGN classified more people with social deprivation and positive family history as high risk, anticipated more of their events, and abolished this gradient.

Conclusion: Conventional cardiovascular scores fail to target social gradients in disease. By including unattributed risk from deprivation, ASSIGN shifts preventive treatment towards the socially deprived. Family history is valuable not least as an approach to ethnic susceptibility. ASSIGN merits further evaluation for clinical use.

  • SHHEC, Scottish Heart Health Extended Cohort
  • SIMD, Scottish Index of Multiple Deprivation
  • cardiovascular disease
  • Scottish Heart Health Extended Cohort
  • socioeconomic status

https://doi.org/10.1136/hrt.2006.108167

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Numerous policies aim to prevent major diseases and reduce disparities resulting from social deprivation. Two recent studies from Scotland 1, 2 complement older observations that classic cardiovascular risk factors inadequately explain social variation in disease. 3 The second study concluded that using the Framingham risk score for coronary disease 4 to assign preventive treatment might lead to relative under-treatment of the socially deprived compared to the socially advantaged in relation to their future disease burden, thus enhancing disparities. 2

Accordingly, it was decided to use the nationally representative database for Scotland from this study, the Scottish Heart Health Extended Cohort (SHHEC), 5 to develop a cardiovascular risk score, ASSIGN. This, in assessing overall cardiovascular risk, would take account of the risk from deprivation which is independent of conventional risk factors. Additionally, we questioned whether risk factors could be better expressed and whether other risk factors, such as obesity and family history, enhanced the score.

Recruitment, risk factor assessment and follow-up

SHHEC includes overlapping studies. The Scottish Heart Health Study 5 recruited random samples of men and women aged 40–59 years across 25 districts of Scotland from 1984 to 1987. The Scottish MONICA Project 6 recruited in Edinburgh and north Glasgow in 1986, north Glasgow again in 1989 and 1995, ages 25–64 and 1992, ages 25–74. Participants completed a questionnaire for a survey clinic where cardiovascular risk factors were measured following WHO MONICA Project rules. 6 Local additions included questions about whether either parent or any siblings had developed heart disease below age 60, and a Minnesota coded electrocardiogram. 7

Participants gave permission for follow-up through routine records. They were flagged for death through the National Health Service Register. The Scottish record linkage scheme listed hospital admissions and deaths from 1981 through 2005. 8, 9

Participants qualified for analysis if they had risk-factor data, permitted follow up, were aged 30–74 years at recruitment and reported neither coronary heart disease nor stroke, and did not have preceding hospital discharge diagnoses of these or transient ischaemic attacks. Endpoints for the ASSIGN score were deaths from cardiovascular causes (ICD-9 codes 390–459, ICD-10 codes I00-I99) or any hospital discharge diagnosis post-recruitment (potentially several per admission) for coronary heart disease (ICD-9 410–414, ICD-10 I20-I25) or cerebrovascular disease (ICD-9 430–438, ICD-10 G45, I60-I69), 10, 11 or for coronary artery interventions (CABG or PTCA). Time to first episode was taken in measuring disease-free survival.

Social deprivation

We used an index of social status based on postcode of residence at recruitment now replacing the Carstairs deprivation score, 12 the Scottish Index of Multiple Deprivation (SIMD). This incorporates numerous components derived from social agencies. 13 It ranges from 0.54 (least) to 87.6 (most deprived), and is also divided into fifths of the Scottish population distribution, SIMD1–5.

Statistical methods

Model development.

We used continuous risk-factor variables where possible. Ten-year disease-free survival was used for calibrating the ASSIGN score, but full follow-up in developing the risk factor model. The Cox proportional hazards model was used to relate survival from cardiovascular disease to the baseline risk factors. 14 For each sex, linearity of effect was shown graphically for the SIMD score, total cholesterol, HDL-cholesterol, systolic blood pressure and cigarettes per day, but not for years since quitting smoking. A significant interaction between sex and SIMD score, persisting after adjustments, suggested separate models for each sex. For each sex, in a multiple regression model, tests proved positive for statistical significance for total cholesterol, HDL-cholesterol, systolic blood pressure, smoking status, cigarettes per day, family history, diabetes, and SIMD score. Body mass index and quitting smoking, both defined in various ways, were not significant in either sex; left ventricular hypertrophy was significant only in women. The final models used the factors identified above as significant in both sexes.

Using the Kaplan-Meier estimate 14 of 10-year survival free from cardiovascular disease, the method of Wilson et al 15 was used to define the 10-year risk of cardiovascular disease, by sex. The result was named the ASSIGN score: ASSessing cardiovascular risk, using SIGN 16 guidelines to assign potential patients to preventive treatment.

Model testing against the Framingham score

The ASSIGN score was compared with the Framingham score for cardiovascular disease. Framingham 10-year cardiovascular risk was defined from the formula given by Anderson et al . 4 Observed events were compared with those expected from the Framingham cardiovascular score across fifths of the SHHEC population ranked by SIMD score (as done previously for coronary disease). 2 The full ASSIGN score was then compared, within sex, to ASSIGN without deprivation and to the Framingham cardiovascular score using the receiver operator characteristics area under the curve, an overall measure of discrimination. 14, 17

The agreement between ASSIGN and Framingham was explored by rank correlations, kappa statistics, and by comparing results of similar thresholds for treatment, and equal-sized high-risk groups.

Finally, the contribution of SIMD as a risk factor was assessed in other cardiovascular endpoints used in other Framingham scores. 4

For testing in practice, the SHHEC ASSIGN score was incorporated into an EXCEL spreadsheet, along with the Framingham cardiovascular score omitting electrocardiographic left ventricular hypertrophy.

Response rates in the different surveys ranged from 65% to 80%, averaging 72%, and were better in affluent than deprived areas. However, these results included Glasgow MONICA so socially deprived areas are overrepresented in SHHEC. Fewer than 100 participants refused follow-up.

Table 1 gives numbers at risk, and mean values and frequencies for risk factors incorporated into the score, including SIMD score and family history. There were 6540 men and 6757 women; mean age at recruitment was 48.8 years. Follow-up at 30th December 2005 ranged from 10 to 21 years. Of 6540 men, 4936 remained disease free and 1604 developed disease, 743 within 10 years. Of 6757 women, 5742 remained disease free and 1015 developed cardiovascular disease, 422 within 10 years. Risk-factors of 10-year disease victims are also shown.

  • View inline

 Means and proportions (SE) of risk factors for the baseline population free of cardiovascular disease, those developing it in the next 10 years, and those placed in the top 20% of the Framingham score, and of the ASSIGN score, by sex (SHHEC age 30–74)

Appendix 1 shows the beta coefficients and formulas used in deriving the ASSIGN score for each sex, with comparable information taken from the cardiovascular risk score for Framingham. 4 A working model comparing the scores is available at www.assign-score.com with additional material.

The rank correlations between Framingham cardiovascular and ASSIGN scores were 0.92 for men and 0.90 for women. The expected 10-year cardiovascular incidence overall for men was 14.4% using ASSIGN and 16.0% using Framingham: the observed incidence was 11.7%. However, risk score distributions are highly skewed. The median ASSIGN score in the SHHEC population is the same as the observed incidence, 11.7%. The Framingham median score was 13.6%. In women the expected 10-year cardiovascular incidence overall for women using ASSIGN was 9.3% and using Framingham 9.6%: that observed was 6.4%. The ASSIGN median was 6.2% and Framingham median 7.1%.

Kappa statistics showed good agreement 14 between the two scores. For predicting a 10-year risk of 20% or more, kappa (95% CI) was 0.68 (0.66 to 0.70) and of 30%, 0.58 (0.55 to 0.60).

When tested against observed events at a 20% fixed cut-off, the overall success rate (those categorised truly as positive or negative) was 83% for ASSIGN and 79% for Framingham; at 30% cut-off it was 96% and 89%. The ASSIGN score receiver operating characteristic area under the curve 14, 17 was significantly (but marginally) higher than the Framingham equivalent in both sexes, an advantage lost if SIMD was left out of the score. For men ASSIGN was 0.727 versus Framingham 0.716 (p = 0.02) and for women 0.765 versus 0.741 (p<0.001).

To make comparisons using similar denominators at risk for both scores and both sexes we took the top scoring 20% for each score in each sex as “high-risk”, about the most that could be so considered. Risk-factor values are shown in table 1. Two thirds of those involved are high-risk to both scores; differences are attributable to the remainder. The results by score using the top 20% cutpoint in terms of fifths of the population distribution (in Scotland) of the SIMD, used in our previous paper, 2 is shown in table 2. As illustrated in the “how-often-that-high” 18 graph of the population frequency distribution of the scores in the SHHEC population (fig 1), the top 20% exceed a Framingham risk score of 24.7% in men and 15.1% in women, and an ASSIGN risk score of 20.8% in men and 13.3% in women. Of the SHHEC events, 46.3% occurred in the top 20% of ASSIGN scores (sexes combined) and 3.4% in the bottom 20%, a ratio of 13.4; for Framingham these values were 45.6%, 4.1% and 11.1.

 Performance of the two risk scores by fifths of the Scottish Index of Deprivation, designating the top 20% for each score as “high risk” in each sex, and combining them (SHHEC population age 30–74)

  • Download figure
  • Open in new tab
  • Download powerpoint

 “How-often-that-high” plots for ASSIGN and Framingham cardiovascular disease scores for men and women, aged 30–74 in the SHHEC cohort.

Table 2 shows that with this change of scores there was little change overall in the common indicators used in assessing the performance of screening tests. ASSIGN performs slightly better, as might be expected, within the population from which it was derived. However, within this overall performance, there are significant changes in the proportions of those in different social groups identified as at high risk, and in the numbers and rates of cardiovascular events anticipated or not within this category. ASSIGN compensates the socially deprived for their excess risk where the Framingham score fails to do so.

Similar Cox regression analyses for other cardiovascular endpoints showed the SIMD score to be equally significant as a risk factor for them.

We have developed a new cardiovascular risk score, ASSIGN, to mitigate potential unfairness in the Framingham and similar risk scores when applied across different social groups in the same population. Nevertheless, with fewer variables than the ASSIGN score, the overall performance of the Framingham score, tested in the population from which ASSIGN was derived, is very similar. However, table 2 shows that like all other cardiovascular scores, performance in both scores is poor in terms of classical ideals of sensitivity and specificity, resulting in many false positives and false negatives. It might be anticipated that the addition of the extra, individually significant, risk factors to the ASSIGN score would improve overall prediction, but it is a common observation that there are rapidly diminishing returns in adding new factors to cardiovascular risk scores after the first small number. Cardiovascular risk scores are imperfect and resist attempts to perfect them, but they are needed for prioritising allocation of preventive treatment fairly to those at highest risk.

The Framingham score might therefore be preferred to ASSIGN, following the principle of parsimony of risk factors. However, the justification for ASSIGN is not greater discrimination, yet to be shown in other populations, but greater fairness. Its added complexity would be hidden in software for computerised data management in primary care. This would contain look-up tables deriving the deprivation score from the postcode of residence of the individuals concerned, as well as consulting or requesting risk factor values, including cigarette consumption and family history. Because we have used continuous variables where possible, this score is more appropriate to electronic calculation than to the colour charts which use risk factor categories. The readout is the same: 10-year risk of cardiovascular disease in the disease free, although again a continuous score rather than a category.

Our earlier analyses in the SHHEC population, 2 like others, suggested that the Framingham coronary risk score overestimated risk. Based on observed/expected event rate ratios, it failed to compensate for social deprivation across SIMD fifths of the population, overestimating risk least in the socially deprived. These findings are repeated here for cardiovascular disease (table 2) with similar results and ratios but larger numbers of events. Social deprivation or socioeconomic status is not only a powerful determinant of coronary and cardiovascular risk but also of chances of reaching hospital alive in a coronary event. 19

For these analyses we used mean Framingham score in the population group concerned to calculate “expected” event rates. After deriving the ASSIGN score, calibrated to 10-year risk in the SHHEC population, we found that it too appeared to overestimate mean risk when it should not have done. We realised then that this paradox was explained by the skewed distribution of risk factor scores. Although the Framingham score does read too high in our population, and it does read higher than ASSIGN, the degree of overestimation is exaggerated using means (see fig 1 and table 2). What happens at the chosen high cutpoints is what matters in practice. This paradox was previously missed both by us and by others. It needs further exploration and discussion elsewhere.

Table 2 shows, using the Framingham score, an undesirable and significant social gradient both in observed/expected ratios, and in the event rate for unanticipated cardiovascular events by SIMD fifth, when using a convenient but artificial criterion for high risk in men and women of the top 20% of risk. These gradients are abolished by the ASSIGN score which redistributes high risk, and potential preventive treatment, towards the most deprived. In its own parent population it has therefore succeeded in its primary objective of social equity. But it needs testing elsewhere.

Apart from social deprivation, the ASSIGN score incorporates a quantitative measure of cigarette smoking where in Framingham it is yes or no. 4, 15 Attempts to characterise ex-smokers were less successful. We recommend classifying them as smokers for the first year and then as non-smokers.

Our third difference from Framingham is the use of family history. The original survey question was about heart disease in parents or siblings below the age of 60. Because younger people may not have parents or siblings who have reached 60, the question has been modified for future use to include premature stroke, and a positive history in several relatives such as uncles, aunts or cousins (see appendix 2). Apart from other advantages of incorporating family history, it may help with ethnic susceptibility. The SHHEC cohort was insufficiently heterogeneous to study risk in ethnic subgroups, but a positive family history was common. Susceptible groups, such as South Asians, could identify their risk through their family’s medical history. A non-threatening question, it avoids labelling people where there could be sensitivity or confusion on the part of the questioner or the questioned. We suspect family history replaced some of the risk associated with social deprivation since they are associated.

We were unable to find a simple adjustment to Framingham scores for social deprivation to make them similar to those from ASSIGN. They read higher on average, and dose of cigarettes and family history complicate a simple one-factor change.

Our comparisons have given us considerable respect for the Framingham score whose coefficients for classic risk factors appear robust. 20, 21 Whether ASSIGN’s marginally better discrimination and its coefficients for deprivation and family history apply elsewhere awaits further testing. Further comparisons will evaluate ASSIGN against Framingham with different cutpoints, age and sex and social distributions, both in other historical populations with equivalent follow-up data, but also in modern populations such as the Scottish Health Survey 22 to assess potential workload and economic consequences of its adoption. It needs installing into computerised databases for pilot testing in primary care.

Whether or not it performs marginally better than the Framingham score overall, ASSIGN addresses the issues of social deprivation and family history. Through greater fairness to disadvantaged, high-risk, minority groups in the population, it should appeal to clinicians and to those responsible for health service strategy.

Acknowledgments

We acknowledge the thousands of volunteer participants who donated their time, their risk factor data and their follow-up data to make this study possible, the work of the NHS Central Register Edinburgh for mortality data, and Information Services of NHS National Services Scotland for morbidity data.

  • ↵ Brindle PM , McConnachie A, Upton MN, et al. The accuracy of the Framingham risk-score in different socioeconomic groups: a prospective study. Br J Gen Pract 2005 ; 55 (520) : 838 –45. OpenUrl Abstract / FREE Full Text
  • ↵ Tunstall-Pedoe H , Woodward M; SIGN group on risk estimation. By neglecting deprivation, cardiovascular risk scoring will exacerbate social gradients in disease. Heart 2006 ; 92 (3) : 307 –10. OpenUrl Abstract / FREE Full Text
  • ↵ Marmot MG , Rose G, Shipley M, et al. Employment grade and coronary heart disease in British civil servants. J Epidemiol Community Health 1978 ; 32 : 244 –9. OpenUrl Abstract / FREE Full Text
  • ↵ Anderson KM , Odell PM, Wilson PW, et al. Cardiovascular disease risk profiles. Am Heart J 1991 ; 121 : 293 –8. OpenUrl CrossRef PubMed Web of Science
  • ↵ Tunstall-Pedoe H , Woodward M, Tavendale R, et al. Comparison of the prediction by 27 different factors of coronary heart disease and death in men and women of the Scottish Heart Health Study: cohort study. BMJ 1997 ; 315 : 722 –9. OpenUrl Abstract / FREE Full Text
  • ↵ Tunstall-Pedoe H , ed, for the WHO MONICA Project. MONICA monograph and multimedia sourcebook . Geneva: World Health Organization, 2003 : 124 .
  • ↵ Prineas RJ , Crow RS, Blackburn H. The Minnesota code manual of electrocardiographic findings: standards and procedures for measurement and classification . Bristol: John Wright, 1982 .
  • ↵ Kendrick S , Clarke J. The Scottish record linkage system. Health Bull (Edinb) 1993 ; 51 : 72 –9.
  • ↵ Kendrick S , The development of record linkage in Scotland: the responsive application of probability matching. Record Linkage Techniques - 1997: Proceedings of an International Workshop and Exposition, Arlington, VA, March 20–21, 1997, http://www.fcsm.gov/working-papers/skendrick.pdf (accessed 12th December 2006).
  • ↵ WHO . International classification of diseases. Manual of the international statistical classification of diseases, injuries and causes of death . 9th rev edn. Geneva: World Health Organization, 1977 .
  • ↵ WHO . International classification of diseases. International statistical classification of diseases and related health problems. 10th rev edn. Geneva: World Health Organization, 1992 .
  • ↵ Carstairs V , Morris R. Deprivation and health in Scotland. Health Bull (Edinb) 1990 ; 48 (4) : 162 –75.
  • ↵ Scottish Executive . The Scottish index of multiple deprivation (SIMD) 2004. http://www.scotland.gov.uk/stats/simd2004/ (accessed 12th December 2006).
  • ↵ Woodward M , Epidemiology: study design and data analysis . 2nd edn. Boca Raton: Chapman and Hall/CRC Press, 2005 .
  • ↵ Wilson PW , D’Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation 1998 ; 97 (18) : 1837 –47. OpenUrl PubMed
  • ↵ SIGN Scottish Intercollegiate Guidelines Network . http://www.sign.ac.uk/ (accessed 12th December 2006).
  • ↵ Altman DG , Bland MJ. Statistical notes. Diagnostic tests 3: receiver operating characteristics curves, BMJ 1994 ; 309 : 188 . OpenUrl FREE Full Text
  • ↵ Tunstall-Pedoe H , Smith WC, Tavendale R. How-often-that-high graphs of serum cholesterol. Findings from the Scottish Heart Health and Scottish MONICA studies. Lancet 1989 ; 1 (8637) : 540 –2. OpenUrl PubMed
  • ↵ Morrison C , Woodward M, Leslie W, et al. Effect of socioeconomic group on incidence of, management of, and survival after myocardial infarction and coronary death: analysis of community coronary event register. BMJ 1997 ; 314 : 541 –6. OpenUrl Abstract / FREE Full Text
  • ↵ Diverse Population Collaborative Group . Prediction of mortality from coronary heart disease among diverse populations: is there a common predictive function? Heart 2002 ; 88 : 222 –8. OpenUrl Abstract / FREE Full Text
  • ↵ Asia Pacific Cohort Studies Collaboration . Cardiovascular risk prediction tools for populations in Asia. J Epidemiol Community Health . (In press).
  • ↵ Bromley C , Sprogston K, Shelton N. eds. The Scottish Health Survey 2003 (4 vols). Edinburgh: The Stationery Office, 2005 .

Supplementary materials

Files in this Data Supplement:

Published Online First 7 November 2006

Funding: Scottish Executive Health Department for this analysis. The Cardiovascular Epidemiology Unit and its studies were funded by a British Heart Foundation Programme grant from 1995 to late 2005, and before that by the Chief Scientist Office of the Scottish Home and Health Department. The opinions expressed in this paper are those of the authors and not the funding bodies.

Competing interests: None declared.

MW planned the analysis of the database, developed the risk score and carried out its critical evaluation, contributing appropriately to the manuscript. PB contributed to the design concept and made critical contributions to the development of the score and the manuscript. HTP planned the study in consultation with the SIGN risk estimation group, obtained the funding, managed and updated the database with staff of the Dundee Unit, is guarantor of the data, assisted in planning the score and in its evaluation, and wrote the paper.

Members of the SIGN (Scottish Intercollegiate Guidelines Network, 28 Thistle Street, Edinburgh EH2 1EN) risk estimation group who helped to refine the study proposal and analyses were: Dr James Grant (chair, principal in general practice, Auchterarder), Dr Moray Nairn (secretary, SIGN Edinburgh), Dr Adrian Brady (consultant cardiologist, Glasgow), Dr Peter Brindle (research and development strategy lead and honorary research fellow, Bristol Teaching Primary Care Trust and Department of Social Medicine, University of Bristol), Mrs Joyce Craig (senior health economist, NHS Quality Improvement Scotland), Dr Alex McConnachie (statistician, Robertson Institute, Glasgow), Mr Adam Redpath (Programme Principal, Information Services, NHS National Services Scotland, Edinburgh), Mr Roger Stableford (patient representative, Falkirk), Professor Hugh Tunstall-Pedoe (cardiovascular epidemiologist, Dundee) and Professor Graham Watt (general practice, Glasgow).

Ethical approval was received from all relevant medical research ethics committees covering the individual populations involved.

Read the full text or download the PDF:

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • BMC Public Health

Logo of bmcph

Evaluation of cardiovascular diseases risk calculators for CVDs prevention and management: scoping review

Mohammed abd elfattah mohammed darwesh badawy.

1 PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam

Sofian Johar

Sokking ong.

2 NCD Prevention Unit, Ministry of Health, Bandar Seri Begawan, Brunei Darussalam

Hanif Abdul Rahman

3 University of Michigan, Ann Arbor, MI USA

Dayangku Siti Nur Ashikin Pengiran Tengah

4 Brunei Neuroscience Stroke and Rehabilitation Centre, Pantai Jerudong Specialist Centre, Bandar Seri Begawan, Brunei Darussalam

Chean Lin Chong

5 Raja Isteri Pengiran Anak Saleha Hospital, Cardiology, Bandar Seri Begawan, Brunei Darussalam

Nik Ani Afiqah Tuah

6 Department of Primary Care and Public Health, Imperial College London, London, UK

Associated Data

All data analysed during this study and supporting its findings are included in this published article and all studies included in this review are available in the table (1).

Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality globally. This review aimed to summarise evidence on the key features, usability and benefits of CVD risk calculators using digital platforms for CVDs prevention and management in populations.

We used search engines and thematic analyses to conduct a scoping review. As the reporting guideline for this review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR).

A total of 17 studies meeting eligibility criteria were included in the analysis, from which about 70% of the studies have prognostic level I ( n  = 8) and level II ( n  = 4) evidence. The review found that various guidelines are recommending different algorithms for CVD risk prediction. The QRISK® was the most accurate CVD risk calculator for several study populations, whereas World Health Organization/International Society of Hypertension (WHO/ISH) risk scores were the least accurate. The key features of CVD risk calculators are variables, predictive accuracy, discrimination index, applicability, understandability, and cost-effectiveness.

For the selected risk prediction tool, development and validation research must be done, which considers a mix of stroke-specific risk and CVD risk to establish its usability in the local community and advantages to the particular health-care environment. To get healthcare professionals more involved in preventing and treating CVDs, each healthcare setting should use an online CVD risk assessment tool that is more useful, accurate, and easy to use, based on the population and health system.

Cardiovascular disease (CVD) is the primary cause of illness and deaths globally that contribute to enormous healthcare costs. The prevalence of CVD-related deaths is increased from 12.1 million in 1990 to 18.6 million in 2019 and is estimated to reach 24 million by 2030 [ 1 ], which results in considerable financial burdens due to high CVD managing costs and the related loss of income. In 2035, CVD will affect more than 130 million people with a total cost of $1.1 trillion [ 2 ].

CVD refers to any disorder that can affect the heart and blood vessels, including coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease and deep vein thrombosis [ 3 ]. Public health strategies to reduce CVD morbidity and mortality consist of population-level risk factor reduction, individual-based primary prevention and secondary prevention and treatment. Population-level strategies focus on decreasing the whole population’s exposure to CVD risk factors across the life course regardless of the CVD risk, focusing on lifestyle factors. CVD risk refers to the risk of suffering fatal or nonfatal CVD events, for example, the risk of myocardial infarction or stroke in the next ten years [ 4 ]. Individual-based primary prevention is targeted at high-risk groups to prevent the onset of CVD through risk factor reduction. The secondary prevention and treatment aim at early detection and treatment to prevent disease progression in people with established CVD [ 5 ]. CVD treatments resulted in minimal reductions in risk factors [ 6 ]. The ‘vertical’ and ‘total’ cardiovascular risk approaches can mitigate individual CVD risks. The ‘vertical’ approach refers to managing a single risk according to predefined thresholds for treatment initiation, with the presence or absence of levels of concomitant risk factors. The World Health Organization (WHO) recommends the 'total' cardiovascular risk approach in preventing CVD with consideration of healthcare resources, cost-effectiveness and high-risk groups [ 4 ]. The approach deems the individual's probability of having fatal or nonfatal cardiovascular events in a given estimated period considering the presence of several predicting risk factors rather than a single risk factor [ 7 ].

Several risk calculators directly estimate the outcome of Stroke specifically or as a combined outcome of CVD risk, such as Stroke Riskometer™, a unique tool for assessing the specific risk of Stroke and endorsed by the World Stroke Organization [ 8 ]. While other risk calculators are developed to assess the individuals' total CVD risk, the first risk scores were from the Framingham Heart Study (FHS) done in 1976 [ 9 ]. The study was based on the western population that may not apply to other populations [ 10 ] in developing countries including, Brunei Darussalam.

CVDs such as Ischemic Heart Disease (IHD) and stroke are the top causes of death and significant public health problems in Brunei Darussalam [ 11 ]. In 2019, CVDs accounted for 25.5% of all causes of death at an age-standardised rate of 165.5 deaths per 100,000 population [ 12 ]. Brunei Darussalam has adapted the CVD risk scoring system from the WHO/ International Society of Hypertension (ISH) chart for the Western Pacific Region A (WPRA) with the absence of evaluation for validity and accuracy in the local population. The Ministry of Health (MOH) Brunei Darussalam introduced the BruHealth mobile application during the COVID-19 pandemic using a digital platform with several key features [ 13 ] that have vast impacts on the population health.

The Framingham Risk Score (FRS) is the first CVD risk assessment tool developed about 60 years ago with the concept of primary prevention and estimates a 10-year risk for CVD [ 14 ]. The European Society of Cardiology Guidelines in 2016 recommended using the Systematic Coronary Risk Evaluation (SCORE) algorithm [ 15 ], based on 12 European cohort studies. It is composed of two distinctive charts for implementation in high and low-risk countries [ 15 ]. The American College of Cardiology/American Heart Association (ACC/AHA) Guidelines endorses (ACC/AHA) CVD risk calculator among individuals from 20 to 79 years to detect the high-risk group and predict atherosclerotic cardiovascular disease (ASCVD) as acute myocardial infarction (MI) [ 16 ]. The National Institute for Health and Care Excellence (NICE), United Kingdom (UK), advocates the QRISK® risk score and updates it every year [ 17 ]. The QRISK® is validated by comparing it against the FRS, and the Scottish ASSIGN scores [ 18 ]. The WHO and ISH have jointly developed the WHO/ISH risk prediction charts using data collected from the different regions of WHO sub-regions [ 19 ]. The WHO/ISH risk prediction consists of two sets of charts used in settings where blood cholesterol can be measured and settings in which blood cholesterol cannot be measured. The charts categorise individuals into different risk levels [ 4 ]. The Stroke Riskometer™ has the aptitude to improve Stroke and NCD prevention markedly. The algorithm is derived from the Framingham Stroke Risk Score (FSRS) prediction based on the INTERSTROKE study. It has performed comparatively poor in predicting stroke events with FSRS and QStroke [ 20 ].

There are online cardiovascular risk calculators to measure the probability of developing CVD without defining the appropriate population. Studies evaluate the CVD risk calculators that are clinically effective and cost-effective [ 21 ]. This review aims to summarise evidence on CVD risk calculators' key features, usability, and benefits using digital platforms for CVDs prevention and management. Also, we discuss the development and validation process, variables, predictive accuracy, discrimination index, applicability, understandability and cost-effectiveness for CVD risk assessment, in developed and developing countries including Brunei Darussalam.

Methods of scoping review

We conducted a scoping review using the following search engines: PubMed, SpringerLink, ScienceDirect, and Google Scholar. The duration of the search was from 1998 to 2020. The search keywords were combined using Primary Medical Subject Headings (MeSH) and Boolean terms. The main keywords used include “cardiovascular risk assessment”, “CVD risk assessment”, “cardiovascular risk score”, “cardiovascular disease risk score”, “CVD risk score”, “cardiovascular risk calculator”, “cardiovascular disease risk calculator”, “CVD risk calculator”, “cardiovascular risk”, “coronary risk score”, “risk equation”, “risk scoring method”, “risk prediction”, “risk algorithms”, “QRISK” and “WHO/ISH”. The type of included articles are reviews and observational studies. We used Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) in the review (Fig.  1 ) [ 22 ]. The initial literature search of this review yielded about 230 eligible abstracts. We analysed the abstracts of the publications and excluded abstracts, case reports, letters, commentaries and clinical trials. We reviewed 80 full-text articles and only included 17 articles in the final analysis that met the eligibility inclusion criteria of this review, which primarily included studies published only in the English, focused on CVD risk calculators for primary CVDs prevention and management, and provided evidence on CVD risk calculators with defined population and apparent outcomes of interest.

An external file that holds a picture, illustration, etc.
Object name is 12889_2022_13944_Fig1_HTML.jpg

Identification of studies via databases

Table ​ Table1 1 shows the characteristics of included studies on CVD risk calculators. The included studies can be categorised into Level I ( n  = 8), II ( n  = 4) and IV ( n  = 5) based on the levels of evidence for prognostic studies [ 23 ].

Characteristics of included studies on CVD risk calculators

Summary of existing CVD risk calculators

The CVD risk calculators widely investigated in studies include FRS, SCORE, ACC/AHA (ASCVD) Risk Estimator, QRISK® and WHO/ISH risk prediction charts [ 36 ]. The typical evaluation method for CVD risk calculators comprises a sample of local patients without prior CVD and with a presence of acute MI. Then, individuals apply the different calculators to estimate the predicted 10-year risk of CVD events if presented just before suffering the acute MI [ 21 ]. QRISK® is the most accurate CVD risk calculator for several study populations. A study conducted in Saudi Arabia states high-risk estimates for each calculator, including ACC/AHA (44.2%), Euro-SCORE (22.5%), FRS (29.5%), and QRISK® (95.3%). The QRISK® is the most accurate CVD risk assessment tool besides being most applicable for the Saudi population [ 21 ]. Another study in India showed higher risks for the Joint British Societies (JBS), FRS and ACC/AHA instruments (55.9%, 38.3% and 30.2%, respectively) than the WHO/ISH risk scores (13.4%) [ 30 ]. The ACC/AHA scored 50.2% as the most helpful guide for initiating statin therapy for primary prevention of CVD and 16.9% for the FRS, and 15.2% for the WHO/ISH risk chart in the Indian population. The WHO/ISH risk scores are the least accurate CVD risk assessment tool [ 32 ].

Table ​ Table2 2 compares QRISK® and WHO/ISH risk calculators. The WHO utilizes a hypothetical dataset for each of the six regions based on the prevalence of risk factors discovered by a previous Collaborative Risk Assessment Project [ 37 ] The WHO/ISH model regression equations have not been released for academic or clinical use [ 19 ]. The QRISK® was created using Cox proportional hazards from a large UK cohort database [ 18 ]. The QRISK® CVD risk calculators were checked internally and externally by a number of studies with different ratings for ethnicity and poverty [ 38 ].

Comparison of characteristics between QRISK® risk calculator and WHO/ISH risk charts

Table ​ Table3 3 compares QRISK® and WHO/ISH risk calculators. The QRISK® risk calculator is a CVD risk score that is dynamically updated from anonymized e-health records to reflect changes in the population. The WHO/ISH risk charts, on the other hand, can be used in healthcare settings with limited resources because they use simple variables. Due to the submission of CVD risk variables, some QRISK® data are missing. An estimated score is made using previously recorded data and expected values based on ethnicity, age, and sex. WHO/ISH risk charts will only apply to the region's most populous country.

Comparison of advantages and disadvantages between QRISK® risk calculator and WHO/ISH risk charts

Table ​ Table4 4 compares the variables of QRISK®2 and WHO/ISH. Gender, age, systolic blood pressure (SBP), diabetes, total cholesterol (mmol/l), and smoking status were variables in the WHO/ISH CVD risk chart [ 4 ]. The QRISK® risk score takes into account your ethnicity, family history (angina or heart attack in a first-degree relative younger than 60 years), cholesterol/HDL ratio, BMI, hypertensive medication, rheumatoid arthritis, chronic kidney disease, and atrial fibrillation [ 17 , 18 ]. Table ​ Table5 5 compares CVD risk assessment tools' discrimination. QRISK® outperformed other CVD risk calculators. Discrimination performance is based on the area under the curve (AUC) metric [ 10 , 17 , 18 , 25 ].

Comparison of variables for QRISK®2 risk calculator and WHO/ISH risk charts

Discrimination performance of CVD risk assessment tools according to (AUC) metric

Key features of CVD risk calculators

Development and validation.

The WHO/ISH risk prediction charts seem the only option available for the populations for which prospective studies are not available [ 30 ]. The WHO CVD Risk Chart Working Group updated the WHO CVD risk charts in 2019 based on newly validated risk prediction models using Cox proportional hazards models to estimate CVD risk in 21 Global Burden of Disease (GBD) regions [ 34 ]. It utilised data from 85 prospective cohorts based on the Emerging Risk Factors Collaboration (ERFC). The charts are recalibrated using data from the GBD studies and the Non-Communicable Disease Risk Factor Collaboration (NCD-RISC) and externally validated using data from a further 19 prospective cohorts [ 34 ]. Evidence suggests that the updated WHO CVD Risk Charts are not formally endorsed and not widely applied, except in a study done among a cohort of Bangladeshi adults [ 41 ]. The study's findings stated that the charts could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of CVDs [ 34 ]. It is mentioned above that the QRISK® was developed from an extensive UK cohort database and the statistical method used was the Cox proportional hazards. Also, multiple imputation statistical techniques allow patients with incomplete data included in analyses, enable full use of all the available data, and increase power and precision [ 18 ]. Also, QRISK® CVD risk calculators were internally and externally validated by several studies with independent contributions of ethnicity and deprivation scores. However, Asians in the QRISK cohort are only 4.1% and 3.6% of the total population in the derivation and validation cohorts. It is updated annually to reflect the population changes [ 38 ].

It is highlighted in Table ​ Table4 4 that the WHO/ISH CVD risk chart variables were gender, age, systolic blood pressure (SBP), presence or absence of diabetes, total cholesterol level (mmol/l), and smoking status [ 4 ]. The QRISK® risk score incorporates more variables including ethnicity, relevant family history (angina or heart attack in a first-degree relative younger than 60 years old), cholesterol/HDL ratio, body mass index, hypertensive medication, rheumatoid arthritis, chronic kidney disease and atrial fibrillation [ 17 , 18 ]. Nevertheless, a shortage of cohort studies analysing CVD risk and weighting such variables in different populations, particularly the Asian population, led to the poor discrimination between observed CVD events and estimated CVD risk in Asians [ 42 ]. The updated QRISK®3 risk prediction models were developed with the inclusion of additional clinical variables. The variables include chronic kidney diseases (including stage 3 CKD), systolic blood pressure, migraine, corticosteroids and systemic Lupus Erythematosus. It enables doctors to identify those at most risk of heart disease and stroke [ 43 ].

Predictive accuracy

Studies showed that the WHO/ISH risk prediction model identifies most people with low CVD risk, for instance, 97% (95% CI 96.4, 97.7) for Cambodia, 89.6% (95%CI 86.8, 92.2) for Mongolia and 94.4% (95%CI 91, 97.8) for Malaysia [ 29 ]. The prevalence of low CVD risk was 89.3% [ 44 ] in Jamaica and 89.7% in Cuba [ 45 ]. Another study reported that the prevalence of CVD risk factors was high, but it did not translate into high CVD risk categorisation [ 31 ]. In addition, the prevalence of high total CVD risk was estimated to be less than 10% in people aged 40 or over in China (1.1%), Iran (1.7%), Sri Lanka (2.2%), Cuba (2.8%), Nigeria (5.0%,) Georgia (9.6%) and Pakistan (10.0%) [ 26 ]. It is plausible that the QRISK® risk calculator demonstrated more prediction accuracy than other CVD risk assessment instruments (as shown in Table ​ Table5). 5 ). The discrimination performance is based on the area under the curve (AUC) statistic and identifies individuals who will experience an event [ 10 , 17 , 18 , 25 ]. A recent study among multi-ethnic Caribbean individuals demonstrated that the QRISK®2 risk prediction model (AUROC = 0.96) is superior to ASSIGN (AUROC = 0.93) and the Framingham risk prediction model (AUROC = 0.92) [ 35 ].

Applicability and understandability

It is stated in Table ​ Table2 2 about the applicability and understandability of the CVD risk calculators, that clinicians often used the WHO/ISH risk charts for quick and consistent estimation of total CVD risk in 'individuals' [ 46 ]. The charts provide an optimal visual aid when explaining the implications of elevated risk and treatment options. In the primary care settings, the charts are likely preferred due to their simplicity to patients and physicians and applicability, especially in low-resource settings where online risk calculators could be complex due to technological challenges [ 24 ]. The QRISK® risk calculator is an online CVD risk algorithm and is integrated into clinical management systems. It generates an estimated score based on existing data to evolve as data quality and completeness improve and population characteristics change [ 18 ]. Some patients might not understand the meaning or the significance of some CVD risk factors. It may make assumptions about missing data, leading to less accurate results [ 39 ]. The JBS recommendations on preventing CVD introduced the JBS3 risk calculator in 2014, focusing on lifetime risk. It uses various visual displays and other metrics, for example, "Heart Age". The JBS3 has main advantages over QRISK®, primarily having multiple ways of presenting risk information that may accommodate the needs and preferences of a range of patients and can facilitate practitioner communication [ 47 ].

Cost-effectiveness

The WHO/ISH risk charts can be used in low-resource healthcare settings as part of stepwise approaches to help target laboratory testing. Also, individuals most likely benefit from the extra information and can use the charts even when values for some risk factors are unavailable [ 19 ]. In contrast, the risk charts incorrectly categorised most people into the low CVD risk group due to no prior validation study done, leading to higher rates of under-treatment and subsequently more complications and cost spending [ 31 ]. The CVD risk assessment tools can identify patients for CVD prevention in primary care opportunistically or through active CVD risk assessment [ 15 ]. A study done in the UK reported CVD preventive measures using the QRISK® algorithm among 40–74 years individuals were highly cost-effective compared with opportunistic assessment [ 48 ]. Conversely, the QRISK® risk calculator implemented in the primary health setting will require spending high costs for the appropriate computer system, screening investigations and examinations for a complete QRISK® CVD risk assessment [ 18 ].

CVD risk calculators in Asia and Brunei Darussalam

The existing CVD risk-assessment tools are not universal due to genetic differences, cultures, lifestyle habits, and social and behavioural characteristics [ 49 ]. The Asia Pacific Cohort Studies Collaboration showed higher systolic blood pressure, total cholesterol, and CVD events in Framingham than in the Asian cohorts. Smoking is higher in the Asian cohort [ 50 ]. The FRS has overestimated the risk in the Asian population [ 51 ]. There is limited evidence on the most effective CVD risk calculator for risk stratification in Asian populations, including Brunei Darussalam. It is appropriate to develop a predictive equation using data obtained from a representative and contemporary cohort of a population [ 52 ]. Some health systems develop their stroke-specific risk calculator based on their unique population and apply it to primary prevention for populations without a history of cerebrovascular disease. However, some develop calculators to predict Stroke in atrial fibrillation, recent transient ischemic attack or history of previous stroke [ 8 ].

The WHO/ISH risk prediction chart for WPRA is the algorithm adopted for CVD risk assessment among individuals in Brunei Darussalam [ 11 ]. The charts have not been validated in Brunei Darussalam due to the absence of prospective cohort studies [ 53 ]. This risk prediction chart might have underestimated the total CVD risk in the Bruneian population due to antihypertensive therapy, as noted in the WHO/ISH risk charts guidelines [ 54 ]. Health care professionals in Brunei Darussalam must consider the key features of the CVD risk calculator and carry out external validation of the tool to assess its feasibility and effectiveness. Besides, they must consider the predictive accuracy of using the calculator to ensure its beneficial outcome tool for the population.

Recommendations

The countries that need to develop national CVD risk calculators or plan to use one of the currently available CVD risk assessment tools should consider the key features that could affect the validity and accuracy of the calculator in determining the usability and benefits of the tool in its respective health care settings. Also, there is a need to consider the development and validation study of the tool, which considers a combination of stroke-specific risk with CVD risk. The key features are variables, predictive accuracy, discrimination index, applicability, understandability, and cost-effectiveness. For Brunei Darussalam, the digital deployment of the QRISK®3 or JBS3 CVD risk calculator through the national 'BruHealth' mobile application may be feasible and applicable to assess CVD risks in the population. In addition, the use of digital machine learning and laboratory measurements could provide more reliable predictive accuracy than the WHO/ISH risk charts. Health care professionals should consider the characteristics of a population in determining the most feasible and accurate tools for the respective health system. Research studies should be conducted focusing on the validation and evaluation (usability and feasibility) of a CVD risk calculator for a particular population, utilising comparative evidence for the CVD risk calculators.

We found that various guidelines are recommending different algorithms for CVD risk prediction. The QRISK® was found to be the more accurate CVD risk calculator for several study populations, whereas WHO/ISH risk scores were discovered to be the least accurate. The key features of CVD risk calculators are variables, predictive accuracy, discrimination index, applicability, understandability, and cost-effectiveness. Also, it is valuable to integrate stoke-specific risk assessment in the CVD risk calculator. A development and validation study must be conducted for the selected risk prediction tool to determine its usability to the local population and benefits to the respective health care setting. Overall, each health care setting should utilize a more feasible, accurate and user-friendly online CVD risk assessment tool tailored to the population and health system. Future research should focus on the validation and evaluation methods of the digital CVD risk calculators to assess the feasibility and benefits of tools to the respective populations.

Strengths and limitations

Our scoping review has several strengths, including the fact that it is the first scoping review on evidence related to CVD risk calculators' key features, usability, and benefits when used with digital platforms for CVD prevention and management. We also made the scoping review process transparent by using a clear search methodology that referred to the level of evidence for prognostic studies for each study included in the review, as well as explicit inclusion and exclusion criteria. The key limitations are the lack of a critical evaluation of the included studies and little bias assessment, as well as the use of search engines rather than research databases to broaden the search area.

Acknowledgements

This research is made possible through the generous support of the PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Brunei Darussalam.

Abbreviations

Authors’ contributions.

All authors contributed toward databases search, drafting and critically revising the paper and agree to be accountable for all aspects of the work. The author(s) read and approved the final manuscript.

This study was supported by Universiti Brunei Darussalam, Brunei Darussalam, (the grant number is UBD/RSCH/URC/RG(b)/2021/024).

Availability of data and materials

Declarations.

As this review is based only on published studies, ethics approval and consent to participate are not applicable.

Not applicable.

The authors have no conflict of interest to declare concerning this article's authorship.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Test Grade Calculator

How to calculate test score, test grade calculator – how to use it, test grade calculator – advanced mode options.

This test grade calculator is a must if you're looking for a tool to help set a grading scale . Also known as test score calculator or teacher grader , this tool quickly finds the grade and percentage based on the number of points and wrong (or correct) answers. Moreover, you can change the default grading scale and set your own. Are you still wondering how to calculate test scores? Scroll down to find out – or simply experiment with this grading scale calculator.

If this test grade calculator is not the tool you're exactly looking for, check out our other grading calculators like the grade calculator .

Prefer watching rather than reading? We made a video for you! Check it out below:

To calculate the percentile test score, all you need to do is divide the earned points by the total points possible . In other words, you're simply finding the percentage of good answers:

percentage score = (#correct / #total) × 100

As #correct + #wrong = #total , we can write the equation also as:

percentage score = 100 × (#total - #wrong) / #total

Then, all you need to do is convert the percentage score into a letter grade . The default grading scale looks as in the table below:

If you don't like using the +/- grades, the scale may look like:

  • An A is 90% to 100%;
  • A B is 80% to 89%;
  • A C is 70% to 79%;
  • A D is 60% to 69%; and finally
  • F is 59% and below – and it's not a passing grade

Above, you can find the standard grading system for US schools and universities. However, the grading may vary among schools, classes, and teachers. Always check beforehand which system is used in your case.

Sometimes the border of passing score is not 60%, but, e.g., 50 or 65%. What then? We've got you covered – you can change the ranges of each grade! Read more about it in the last section of this article: Advanced mode options .

🙋 You might also be interested in our semester grade calculator and the final grade calculator .

Our test score calculator is a straightforward and intuitive tool!

Enter the number of questions/points/problems in the student's work (test, quiz, exam – anything). Assume you've prepared the test with 18 questions.

Type in the number the student got wrong . Instead – if you prefer – you can enter the number of gained points. Let's say our exemplary student failed to answer three questions.

Here we go! Teacher grader tool shows the percentage and grade for that score. For our example, the student scored 83.33% on a test, which corresponds to a B grade.

Underneath you'll find a full grading scale table . So to check the score for the next students, you can type in the number of questions they've got wrong – or just use this neat table.

That was a basic version of the test grade calculator. But our teacher grader is a much more versatile and flexible tool!

You can choose more options to customize this test score calculator. Just hit the Advanced mode button below the tool, and two more options will appear:

Increment by box – Here, you can change the look of the table you get as a result. The default value is 1, meaning the student can get an integer number of points. But sometimes it's possible to get, e.g., half-points – then you can use this box to declare the increment between the next scores.

Percentage scale – In this set of boxes, you can change the grading scale from the default one. For example, assume that the test was challenging and you'd like to change the scale so that getting 50% is already a passing grade (usually, it's 60% or even 65%). Change the last box, Grade D- ≥ value, from default 60% to 50% to reach the goal. You can also change the other ranges if you want to.

And what if I don't need +/- grades ? Well, then just ignore the signs 😄

How do I calculate my test grade?

To calculate your test grade:

  • Determine the total number of points available on the test.
  • Add up the number of points you earned on the test.
  • Divide the number of points you earned by the total number of points available.
  • Multiply the result by 100 to get a percentage score.

That's it! If you want to make this easier, you can use Omni's test grade calculator.

Is 27 out of 40 a passing grade?

This depends mainly on the grading scale that your teacher is using. If a passing score is defined as 60% (or a D-), then 27 out of 40 would correspond to a 67.5% (or a D+), which would be a passing grade. However, depending on your teacher’s scale, the passing score could be higher or lower.

What grade is 7 wrong out of 40?

This is a B-, or 82.5% . To get this result:

Use the following percentage score formula: percentage score = 100 × (#total - #wrong) / #total

Here, #total represents the total possible points, and #wrong , the number of incorrect answers.

Substitute your values: percentage score = 100 × (40 - 7) / 40 percentage score = 82.5%

Convert this percentage into a letter grade. In the default grading scale, 82.5% corresponds to a B-. However, grading varies — make sure to clarify with teachers beforehand.

Is 75 out of 80 an A?

Yes , a score of 75 out of 80 is an A according to the default grading scale. This corresponds to a percentage score of 93.75%.

  • Poker Odds Calculator
  • RAID Calculator

Chilled drink

Christmas tree, coffee kick.

  • Biology (100)
  • Chemistry (100)
  • Construction (144)
  • Conversion (295)
  • Ecology (30)
  • Everyday life (262)
  • Finance (571)
  • Health (440)
  • Physics (510)
  • Sports (105)
  • Statistics (184)
  • Other (183)
  • Discover Omni (40)
  • Skip to content
  • Accessibility help

CVD risk assessment and management: Scenario: Management of people with an estimated risk of 10% or more

Last revised in May 2023

Covers the management of people with a CVD risk score of 10% or more.

Scenario: Management of people with an estimated risk of 10% or more

From age 18 years onwards.

How should I manage people with a CVD risk of 10% or more?

  • Use the clinical findings, lipid profile and family history to judge the likelihood of a familial lipid disorder — for more information on diagnosing or excluding familial hypercholesterolaemia as a cause of dyslipidaemia, see the CKS topic on  Hypercholesterolaemia - familial . 
  • Exclude possible secondary causes of dyslipidaemia (such as excess alcohol, uncontrolled diabetes, hypothyroidism, liver disease and nephrotic syndrome).
  • Offer the opportunity to reassess CVD risk again after they have tried to change their lifestyle. 
  • Recognise that some people may need support to change their lifestyle — to help, refer them to programmes such as exercise referral schemes or smoking cessation services.
  • Discuss the benefits and risks of taking a lipid modification therapy, taking into account additional factors such as comorbidities, potential benefits from lifestyle interventions, the person's preferences, polypharmacy, general frailty and life expectancy.
  • Offer atorvastatin 20 mg daily if the person decides to take this and there are no contraindications — for more information on starting statin treatment, see the CKS topic on  Lipid modification - CVD prevention . 

Basis for recommendation

These recommendations are based on the National Institute for Health and Care Excellence (NICE) guideline  Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease  [ NICE, 2016 ], and the Public Health England (PHE) guide  NHS  Health check: best practice guidance [ PHE, 2017 ]. 

Cardiovascular disease (CVD) risk threshold

  • NICE has determined that the treatment threshold for primary prevention of CVD is a CVD risk of 10% as assessed using QRISK [ NICE, 2016 ].  
  • SIGN states that while the NICE approach identifies the proportion of the population which is cost effective to treat, it is less clear how the additional workload was accounted for within primary care to implement the policy, and the societal ramifications of the threshold effectively (placing almost all people in England and Wales above 65 years of age at high risk of CVD) were not explored.
  • In Scotland, almost 95% of individuals are at 10% or greater risk of a cardiovascular event within 10 years by the age of 60–64. Implementing this threshold in Scotland would increase the total number of people eligible for preventive treatment by around 70% to over 1.3 million.

What lifestyle advice should I give to help reduce the risk of CVD?

  • Lifestyle advice for people with a cardiovascular disease (CVD) risk of 10% or more is the same as for people with a CVD risk of less than 10%. See the section on lifestyle advice  in the  Scenario: Management of people with an estimated CVD risk less than 10% .

How should I manage comorbidities which are associated with increased risk of CVD?

  • Similar to people with a cardiovascular disease (CVD) risk of 10% or less, treatment other medical conditions which are associated with an increased risk of CVD should be optimized where possible, to reduce their effect on CVD risk. For more information, see the section on  Managing comorbidities in the Scenario: Management of people with an estimated CVD risk less than 10% . 

What drug treatments should I offer for the primary prevention of CVD?

  • Decide whether to start statin therapy after an informed discussion with the person about the risks and benefits of statin treatment, taking into account additional factors such as potential benefits from lifestyle changes, informed patient preference, comorbidities, polypharmacy, general frailty and life expectancy.
  • Offer atorvastatin 20 mg a day (unless contraindicated) for the primary prevention of cardiovascular disease (CVD) to people (including those with type 2 diabetes) with an estimated CVD risk of 10% or more calculated using the QRISK3 assessment tool. 
  • Aged 85 years or older (beyond the QRISK range for inclusion) — consider offering atorvastatin 20 mg, which may be of benefit in reducing the risk of non-fatal myocardial infarction. But, take into account factors that may make treatment inappropriate such as comorbidities, polypharmacy, general frailty, and life expectancy.
  • With chronic kidney disease — for information on how to estimate and manage CVD risk, see the CKS topic on Chronic kidney disease . 
  • Offer atorvastatin 20 mg to adults aged over 40 years, or who have had diabetes for more than 10 years, or who have established nephropathy, or have other CVD risk factors.
  • With hypercholesterolaemia — for more information, see the CKS topic on  Hypercholesterolaemia - familial . 
  • For more information on when antihypertensive drug treatment is appropriate, see the CKS topics on Hypertension , and Diabetes - type 2 .
  • Do not routinely offer aspirin for the primary prevention of CVD.

These recommendations are based on the National Institute for Health and Care Excellence (NICE) guideline  Lipid modification: Cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease  [ NICE, 2016 ], the European Society of Cardiology (ESC)  2016 European guidelines on cardiovascular disease prevention in clinical practice  [ ESC, 2016 ], an ESC position paper  Aspirin therapy in primary cardiovascular disease prevention  [ Halvorsen, 2014 ], the British and Irish Hypertension Society (BIHS)  Statement on the use of aspirin   [ BIHS, 2017 ], and the Scottish Intercollegiate Guidelines Network (SIGN) guideline  Risk estimation and the prevention of cardiovascular disease  [ SIGN, 2017 ]. 

Offering statins to people with a CVD risk of 10% or more

  • NICE based its recommendation on 34 studies on statin treatments.
  • CKS acknowledges that the threshold for offering statin treatment (a 10% or more 10 year risk of cardiovascular disease [CVD]) is controversial. Previous guidelines had used a 20% threshold, but the cost-benefit analysis performed for the current NICE guideline showed that this threshold should be lowered to 10% if a person wishes to take a statin after an informed discussion.
  • Much debate in the medical and lay press ensued from this recommendation. For example, see the  open letter  written to NICE from a group of leading doctors raising concerns about the guidance on statins and a  BMJ leader  in 2014 which also highlights some of the concerns. See also the  response from NICE  about the criticisms. Further to this, see an  open letter  sent from a group of leading doctors to the Health Select Committee of the House of Commons in October 2014, and the  response to this letter  from Professor Haslam, Chair of NICE.

Aspirin for primary prevention of CVD

  • Aspirin is not recommended for primary prevention of cardiovascular disease [ SIGN, 2017 ]. 
  • Aspirin may be considered beneficial for primary prevention if an individual’s future risk of stroke or heart attack is higher than average.
  • An accurate quantitative assessment of CVD risk is essential before prescribing aspirin for individuals in the primary prevention of CVD, where the evidence for benefit versus harm is very limited.
  • In a systematic review of six trials (n = 95,000), which compared the long-term use of aspirin vs control in people without overt CV or cerebrovascular disease, a risk reduction from 0.57% per year to 0.51% per year of serious vascular events was found, and major gastrointestinal (GI) and extracranial bleeds increased by 0.03% per year. The risk of vascular mortality was not changed by treatment with aspirin.
  • In a Japanese study (n = 14,464), people aged 60–85 years with hypertension, dyslipidaemia or diabetes mellitus were randomized to treatment with 100 mg aspirin or placebo. The 5-year cumulative primary outcome event rate (death from CV causes) was not significantly different between the groups, but treatment with aspirin significantly increased the risk of extracranial haemorrhage requiring transfusion or hospitalization (P = 0.004).
  • In the absence of such conditions, people with a 10-year risk of major CV of more than 20% should be given low-dose aspirin, and people with a risk 10–20% should be considered potentially eligible.

Statin use in the elderly

  • One meta-analysis of 8 trials including 24,674 patients aged 65 years or over (average age 73) without established CVD concluded that statins reduce the incidence of MI (RR 0.61, 95% CI 0.43 to 0.85) and stroke (RR 0.76, 95% CI 0.63 to 0.93) but do not significantly prolong survival. Similarly, an individual patient data analysis of major statin trials has confirmed reductions in first major cardiovascular events of 22% in those aged 66–75 and 16% in those aged over 75 per 1 mmol/l reduction in LDL cholesterol.
  • However, one RCT investigating the safety and benefit of stopping statins in people aged on average 74.1 years, with advanced, life-limiting illness, found that stopping statins is safe and may be associated with benefits including improved quality of life. The proportion of participants in the discontinuation versus continuation groups who died within 60 days was not significantly different (23.8% v 20.3%, 90% CI 3.5% to 10.5%) and did not meet the non-inferiority end point. Total quality of life (QoL) was better for the group discontinuing statin therapy (mean McGill QoL score 7.11 v 6.85, p=0.04). Few participants experienced cardiovascular events (13 in the discontinuation group, 11 in the continuation group).
  • There is no evidence of decreasing effectiveness of statins in patients aged over 75 years, and evidence supporting effectiveness in people aged over 80 years of age is very limited [ ESC, 2016 ]. 
  • In the elderly, the decision to start statin therapy should be based on 10-year CVD risk estimation, life expectancy, and QoL. Age alone is not a contraindication to drug therapy [ SIGN, 2017 ]. 

The content on the NICE Clinical Knowledge Summaries site (CKS) is the copyright of Clarity Informatics Limited (trading as Agilio Software Primary Care) . By using CKS, you agree to the licence set out in the CKS End User Licence Agreement .

What's on This Page: ×

Was this helpful?

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

The feedback you submit here is used only to help improve this page.

That’s great! Thank you for your feedback!

Thank you for your feedback!

About Scoring

Whether you are creating a helpful quiz or a fun personality test, you want to use scoring. This feature gives you the ability to attach a point value to the answers of specific question types. These point values are summed up to give your respondent a final score.

Questions that Can Be Scored

Only the following question types are compatible with the scoring feature:

  • All multiple choice variations
  • Matrix – Likert Qtip: Carousel and standard likert matrix questions are compatible with scoring. Profile questions are not compatible with scoring.
  • Matrix – bipolar
  • Text entry – single line
  • Text entry – multi-line
  • Text entry – essay text box
  • Text entry – password
  • Slider – slider type
  • Side by side – scaled response columns (single answer and multiple answer)

Any questions not listed here (including different types of the questions listed above, such as open-ended matrix tables and form questions) cannot be included in scoring categories.

Scoring a Survey

Opening the scoring editor.

Where scoring can be accessed

  • Make sure you are in the Survey tab.
  • Click Survey options.
  • Select Scoring .

Scoring Multiple Choice Questions

Scoring page with one choice out of a question scored to indicate it is correct

Scoring Matrix questions

Just like with multiple choice questions, you can click into any of the fields in a matrix table to assign a point value to that answer. However, there are quicker ways to score a matrix table.

A personality assessment matrix. Dropdown next to first statement expanded to show options

Scoring Text Entry questions

a scoring box and a field for the correct text entry question

Scoring Sliders

Sliders are unique in the way they are scored. The score value you assign to each choice acts as a multiplier for the slider value that respondent selects in the survey. If the score value is set to “5” and the respondent selects “10” on a 0–10 slider scale, then the assigned score will be 50 (5 times 10).

Example: Here is a hypothetical question where we ask residents to rate the level of damage in their maintenance request. If there are lots of requests, maintenance can help those residents with the biggest maintenance needs first.

Slider question in survey editor

Here is what the question looks like when we score it. We might add multipliers to indicate damage to one fixture is more major than damage to another. For example, all damage ratings for the fridge will be multiplied by 2, since a broken fridge can lead to the tenant’s food spoiling. Meanwhile, damage to the dishwasher is cut in half (multiplied by 0.5), since kitchen utensils can be cleaned in the sink while the dishwasher is broken.

Slider question in scoring. It looks like a list of options with a single field next to each

If you want to include your slider responses in scoring as they are, without special multipliers, make sure you assign the point value of 1 to each slider row.

Returning to the Survey Tab

When you’re done editing scores, you can return to the survey editor by clicking  Back to survey builder .

Gray back to survey builder button in the upper-left

Scoring Options

clicking scoring options

  • Your scoring categories will be listed at the top. You can manage your scoring categories here, which includes renaming categories, translating category names, deleting categories, and creating new categories.
  • If desired, you can display scores to survey respondents using the Show Scoring Summary for Category options. See the Displaying the Score for Respondents section for more information.
  • The setting Treat empty statistics for scoring categories as 0 , if enabled, will take any answer choice that was not scored, and assign it a value of 0. If this option is not enabled, then any answer choice that was not scored will be treated as a null value. This affects statistics like the minimum in your reporting. With the setting enabled and answer choices unscored, the min in your reporting would be 0. With the setting disabled and answer choices unscored, the min would be null. This is most useful when using the Qualtrics 360 product .

Setting Up Scoring Categories

A Qualtrics survey can be scored multiple times for different categories. For example, in an employee satisfaction survey, you may want to add categories for work ethic, teamwork, and leadership.

By default, there is 1 scoring category labeled “Score.” To use more than 1 scoring category in your survey, you will need to create new categories.

creating scoring categories

Scoring options on the upper-right of the scoring editor

  • Name your category. Qtip : To translate the name of your scoring category, select Translate next to the category’s name.
  • Click Save .

Choosing the Category You’d Like to Score

category dropdown

Deleting Categories

Click the gear next to a category and select  Delete Category to remove it.

Gear to the right of a category in the scoring options window

Displaying the Score for Respondents

Showing scores after questions or at the survey’s end.

You can choose to show respondents their scores after each question, or show them a summary of their score at the survey’s end.

Scoring options on the upper-right of the scoring editor

  • Select At the End of the Survey to show the total score at the end of the survey, along with the respondent’s graded answers.
  • Select After Each Question to show the graded question scores at the end of each page of the survey.
  • Click  Save .

Displaying Scores in Survey Questions and Messages

You can pipe in the total score at any point in your survey where you have access to the piped text menu. Just click on the menu and select Scoring .

See the Piped Text support page’s section on scoring for more details.

Piped text in an end of survey message.

Qtip : Piped text can be used in lots of places, including questions of all kinds, descriptive texts , end of survey messages , and email tasks !

Displaying Different Messages Based on Respondents’ Scores

When it comes to assessments, we want to communicate different things to people based on how they performed. We can congratulate people who scored 100%, but we might want to provide guidance and study guides to those who scored below a 75%. Maybe we’re running a personality assessment, and want to tell the respondent what profile matches their results.

Check out the Displaying Messages Based on Scoring support page to learn more!

Category Groups

You can sort your scoring categories into groups for your organizational convenience.

Show Categories by Group is selected in upper-rightmost corner of manage scoring categories window

  • Click  New Group .

Along the bottom, creating scoring category groups

  • Click  Save when you’re finished.

Compatible Project Types

Scoring is  only available in the following types of projects:

  • Survey projects
  • XM Solutions

Related Articles

Selecting a Scoring Model

The first step in setting up intelligent scoring is selecting a category model to use for scoring.

Viewing Your Support History

After logging into your Customer Success Hub, you can view your past support ticket history, including transcripts of your interactions with support and any connected escalations, and reply directly to email tickets you’ve submitted. If you are a Brand Administrator, you can also view the support tickets for your entire brand from this page.

Time Between Ticket Statuses

The longer a call center representative ignores a ticket, the longer the customer goes without a solution, and the more likely that the customers will be (justifiably) upset. Therefore, it can be incredibly important to a close-the-loop program that there are report-outs on the time taken to resolve a ticket. In Qualtrics, you can report on the time between various ticket statuses, gaining insight into the time it takes your team to take action on or resolve issues.

Matrix Table Question

Matrix table questions allow you to combine multiple questions with the same answers. This is most useful when you need to ask multiple questions that should be rated on the same scale.

Updating Scoring Criteria (D...

You can make edits to both the scoring model underlying your scoring and the rubric itself. This page compiles best practices and resources you can use as you evolve your scoring criteria.

Using Intelligent Scoring in...

Now that you’ve successfully set up intelligent scoring, let’s talk about how to incorporate that data into your Studio dashboards. After you create a rubric, the objects described on this page are created based on how you defined your rubric’s scoring rules.

Request Demo

Ready to learn more about Qualtrics?

  • FanNation FanNation FanNation
  • SI.COM SI.COM SI.COM
  • SI SWIMSUIT SI SWIMSUIT SI SWIMSUIT
  • SI Sportsbook SI Sportsbook SI Sportsbook
  • SI Tickets SI Tickets SI Tickets
  • SI Showcase SI Showcase SI Showcase
  • SI Resorts SI Resorts SI Resorts

assign score 10

Palworld — 10 Things I Wish I Knew When Starting

  • Author: Robbie Landis

How to Play Palworld, Pokémon with Guns, Tips, Tricks and Essential Knowledge

Developer Pocketpair found lightning in a bottle with 2024's first breakout hit, Palworld, or as the meme squad refers to it, Pokémon with guns . It creates a fun and satisfying gaming experience by blending the aesthetics and creature collecting of Pokémon with the gathering, crafting, and survival of games like Minecraft and Valheim.

In just one week Palworld sold over 8 Million copies on Steam despite only being in Early Access. While more and more gamers are jumping into Palpagos, here are some essential tips for those just beginning to make your adventures go a bit more smoothly.

Essential Tips to Play Palworld

Instead of jumping into Palworld blind, here's some tips and advice you should know about the Pokémon with Guns game before you dive in.

Check the Survival Guide

Palworld's in-game main menu, including a Survival Guide.

Palworld/Pocketpair

Palworld has a built-in tutorial that leads you through the first few quests. But if you want to know more of the specifics you can find many concepts explained in the Survival Guide of your Paldeck.

Assign Your Pals Jobs

A player assigning Rushroar to gather rocks in Palworld.

Palworld Players can assign their Pals to specific tasks if they seem to be wandering around camp, or leaving certain jobs unfinished. This doesn't always work in the early version of the game or if your tasks are too close together. Sometimes Pals will leave their task to rest, take a break or curb their appetite regardless of where they're assigned.

Check Your Pals Abilities

Lamboll is one of the first Pal's players will encounter in Palworld.

Every Pal in Palworld is suited for specific types of task in your base. It may seem wise to fill your base with Pals who can do as much as possible, but currently this leads to them always running off and finding new things to do. Sometimes they leave other tasks unfinished. Try to focus your Pals on specific tasks so they don't get sidetracked.

Listen for Lucky Pals

Just like in Pokémon, players can find rare versions of Pals called Lucky Pals. They have bigger models and a unique sparkle icon.

Just like in Pokémon, players can find rare versions of Pals called Lucky Pals. They have bigger models and a unique sparkle icon. You'll know they're near because they emit a shimmering type of sound effect. They're stronger than normal, so be prepared when you see one to capture it.

Incubation Timer Settings

Players who find Pal Eggs can hatch them using incubators.

Players will find Pal Eggs of all types all over Palworld. In order to hatch them players will need to build incubators and wait a set amount of depending on the rarity and conditions. However, players may find their timers are different from their friends based on the difficulty settings they choose. You can access these settings before launching your server, even setting it so low they hatch instantly.

Stat Allocation

The player stat sheet in Palworld.

Stats are an important part of creating a character and can make a player's life easier or much harder later on in the game. While there is a way to reset your stats, early versions of the game were bugged when trying to do so. Attack is generally considered useless since Pals will do bigger damage and players don't want to be too strong because then Pals might faint when being caught. Hit Points, Stamina, Work Speed and Carry Limit are all very important depending on your playstyle.

Base Location is Important

Palbeds and a berry farm in Palworld.

When you first start playing Palworld it instructs you to just build your first base. Don't choose the first location you find, take a moment to search around for prime real estate. You want someplace flat with little nooks and crannies because your Pals will find their way in there and get stuck. Choose someplace with Ore if possible as you can make your own Rock and Wood gathering sites inside a base later on.

You also only get one base camp for a while and you don't want to be moving it around constantly.

Pal Strengths and Weaknesses

Palworld's elemental chart shows the strengths and weaknesses of each type.

Just like in most games that feature elemental types, each Pal in Palworld has their own set of strengths and weaknesses. Learn this early on and it'll make your battles against bosses a lot easier.

Check Pal Passive Skills

Killamari is a Squid-inspired Pal in Palworld.

Along with the active skills they use to deal damage, Pals each have between zero and three passive skills. These do a number of things from aiding you in battle, to helping you out in your base. So check all the Pals to see who's best at what, even if they don't look cool enough for battle they could be a huge asset to your base camp.

Fast Travel from Base

The Palbox is where players manage all of their Pals in Palworld.

This may seem like a no-brainer but don't forget you can fast travel directly from your Palbox to other fast travel points you've unlocked. When you eventually get a second base set up, you can fast-travel directly to it as well.

This is great for when you set up a base specifically for mining ore for ingots because base storage is not shared. You'll have to teleport back and forth to bring ore to your furnace if you don't set one up in a second location.

Latest Esports News

Helldivers 2 Promo Image Soldier

Helldivers 2 - 1.000.102 Patch Notes: Terminid Enemy Nerfs & More

Diablo 4 Season of the Construct The Gauntlet and Leaderboards

Diablo 4 Leaderboards and The Gauntlet Guide

Spider-Man 2 Image Multiplayer

Cancelled Spider-Man: The Great Web Trailer Leaks Online

Latest video game news.

Pokemon Scarlet and Violet Charizard Tera Raid

How to beat the Charizard seven-star Tera Raid event

dragons dogma 2 big buu

Our celebrity Dragon's Dogma 2 creations will give you nightmares

Fortnite Gatekeeper Shotgun

Every Fortnite Chapter 5 Season 2 weekly quest

IMAGES

  1. How do I use the Assignment Scores report?

    assign score 10

  2. How do I use the Assignment Scores report?

    assign score 10

  3. How Accurate is SafeAssign? How to Read SafeAssign Scores

    assign score 10

  4. How Accurate is SafeAssign? How to Read SafeAssign Scores

    assign score 10

  5. How to assign different points for multiple choice questions and

    assign score 10

  6. Solved Python: (Assign grades) Write a program that reads

    assign score 10

VIDEO

  1. Cem Olcay Auto Fills Drum Fill Generator

  2. End score 10-4

  3. Can you score 10/10? Addition challenge

  4. Healthy mouse

  5. 1 lab 6 assign score

  6. ENGR10 Assign color Green to SCORE instead of cut

COMMENTS

  1. Home

    ASSIGN is a cardiovascular risk score developed in Dundee University, Scotland in 2006. ASSIGN includes social deprivation for the first time, and family history of cardiovascular disease, with the classic risk factors. It identifies people free of cardiovascular disease most likely to develop it over ten years.

  2. Estimate the risk

    The ASSIGN score is the estimated risk of people who are free of cardiovascular disease at that time, of the same age and sex and risk factor values to those entered into the score, developing coronary heart disease, a transient ischaemic attack or stroke, or death from cardiovascular disease over the next ten years. ...

  3. About

    Introduction. ASSIGN is a cardiovascular risk score. The score was developed to prioritise the prevention of cardiovascular disease in those currently free of it by identifying those at highest risk. The score is ostensibly the ten-year percentage risk of developing cardiovascular disease (any manifestation of coronary heart disease or ...

  4. Cardiovascular disease risk assessment and prevention

    The JBS3 calculator is not only able to estimate short term (10-year) risk, but also lifetime risk of CVD events. Patients with a 10-year risk of CVD of less than 10% may benefit from an assessment of their lifetime risk using the JBS3 tool, and a discussion on the impact of lifestyle interventions and, if necessary, drug therapy.

  5. Cardiovascular Risk and Risk Scores: ASSIGN, Framingham, QRISK ...

    Sole contributor's statement of interest The sole contributor, Hugh Tunstall-Pedoe, developed the ASSIGN cardiovascular risk score jointly with Mark Woodward in 2006, in relation to the revised SIGN (Scottish Intercollegiate Guidelines Group) guideline 97 'Risk estimation and the prevention of cardiovascular disease'.10 11 It is adopted by ...

  6. Beyond 10-Year Risk: A Cost-Effectiveness Analysis of Statins for the

    The dashed line represents cost-effectiveness threshold of £20 000/quality-adjusted life-year (QALY). ASSIGN 10 indicates individuals with an ASSIGN score ≥10%; and ASSIGN 20, individuals with an ASSIGN score ≥20. Compared with no treatment, all statin strategies led to large reductions in CVD-related health care costs . However, these ...

  7. About

    Development of the ASSIGN score. The ASSIGN score was developed in the summer of 2006 by Professor Hugh Tunstall-Pedoe and Professor Mark Woodward based at the University of Dundee, Scotland, working with the SIGN (Scottish Intercollegiate Guidelines Network) group on cardiovascular...

  8. ASSIGN Score

    PMID: 35926802 Free PMC Article. The Michigan Risk Score to predict peripherally inserted central catheter-associated thrombosis. Chopra V, Kaatz S, Conlon A, Paje D, Grant PJ, Rogers MAM, Bernstein SJ, Saint S, Flanders SAJ Thromb Haemost 2017 Oct;15 (10):1951-1962.

  9. PDF Cardiovascular Risk and Risk Scores: ASSIGN, Framingham, QRISK and

    ASSIGN score was a statistically signifi-cant improvement in discrimination, but disappointingly modest.10 Diminishing returns is a common finding on adding new factors to the Framingham dataset.14 Improvement is not inevitable; more does not mean better. The present paper3 independently confirms our own finding in SHHEC10 that ASSIGN ...

  10. Cardiovascular Risk Prediction Models and Scores in the Era of

    5. Assign Risk Score. Assign risk score was formulated to estimate the 10-year risk of cardiovascular events in subjects without established CVD by adding social deprivation and family history to the risk factors including in the Framingham score (sex, age, T2DM, smoking, TC and SBP) [48,49,50].

  11. Adding social deprivation and family history to cardiovascular ...

    ASSIGN scores, lower on average, correlated closely with Framingham values for 10-year cardiovascular risk. Discrimination of risk in the SHHEC population was significantly, but marginally, improved overall by ASSIGN. However, the social gradient in cardiovascular event rates was inadequately reflected by the Framingham score, leaving a large ...

  12. Evaluation of cardiovascular diseases risk calculators for CVDs

    The Framingham Risk Score (FRS) is the first CVD risk assessment tool developed about 60 years ago with the concept of primary prevention and estimates a 10-year risk for CVD . The European Society of Cardiology Guidelines in 2016 recommended using the Systematic Coronary Risk Evaluation (SCORE) algorithm [ 15 ], based on 12 European cohort ...

  13. ASSIGN Risk Score (ASSIGN CVD 10-YEAR RISK) Supp V2.0

    The ASSIGN Cardiovascular Disease 10-Year Risk Score (ASSIGN CVD 10-YEAR RISK) describes the cardiovascular risk of the subject. The score is expressed as a percentage. The ASSIGN Cardiovascular Disease 10-Year Risk Score is derived via an algorithm that includes labs, vital signs, medical history, substance use, social deprivation and demography.

  14. Adding social deprivation and family history to cardiovascular risk

    ASSIGN scores, lower on average, correlated closely with Framingham values for 10-year cardiovascular risk. Discrimination of risk in the SHHEC population was significantly, but marginally ...

  15. Test Grade Calculator

    To calculate your test grade: Determine the total number of points available on the test. Add up the number of points you earned on the test. Divide the number of points you earned by the total number of points available. Multiply the result by 100 to get a percentage score. That's it!

  16. excel

    now specify scale 1 and 10 in Y set of values. for x set of values specify B and 0. and then for new x set of values specify value that you want to normalize. B =120. since it works on y=mx + c. (x1,y1) = (120,0) and (x2,y2) = (0,10) any new x that you enter will be normalized on basis of this.

  17. Scenario: CVD risk 10% or more

    Offer atorvastatin 20 mg a day (unless contraindicated) for the primary prevention of cardiovascular disease (CVD) to people (including those with type 2 diabetes) with an estimated CVD risk of 10% or more calculated using the QRISK3 assessment tool. For people: Aged 85 years or older (beyond the QRISK range for inclusion) — consider offering ...

  18. java

    I need to write a program that reads student scores, gets the best score, and then assigns grades based on the following scheme: 1) Grade is A if score is >= best - 10. 2) Grade is B if score is >= best - 20; 3) Grade is C if score is >= best - 30; 4) Grade is D if score is >= best - 40; 5) Grade is F otherwise.

  19. Scoring

    High and Low Scores Table (360) Hidden Strengths / Improvement Areas Table (360) Scoring Overview Table (360) Report Summary Table (360) Word Cloud Visualization; Report Options (360) Dashboards Tab Dashboards Basic Overview (360) New Dashboards Experience; Adding, Copying, & Removing a Dashboard (EX)

  20. The Assignment of Scores Procedure for Ordinal Categorical Data

    One method of assigning a score to these ordinal categorical data is to assign a score to ordinal categorical data subjectively (e.g., 5 for strongly agree, 4 for agree, 3 for no opinion, 2 for disagree, and 1 for strongly disagree ). However, the original scale is an ordinal scale, without the concept of distance.

  21. Palworld

    Palworld Players can assign their Pals to specific tasks if they seem to be wandering around camp, or leaving certain jobs unfinished. This doesn't always work in the early version of the game or ...