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Cardiovascular Risk Scoring

Using data from the long-term Framingham Heart Study, statisticians worked out a way to predict the probability of heart problems. The first Framingham coronary prediction method (1998) estimates the risk of coronary health disease (CHD) over the course of 10 years. Angina pectoris, myocardial infarction (heart attack), and death from coronary disease are considered CHD for the purposes of the scoring. Other heart and vascular diseases are not included.

The factors used to calculate the score include age, sex, total cholesterol level (mg/dl), HDL cholesterol level (mg/dl), systolic blood pressure (mm/Hg), and whether the person smokes. The answer is given as a percentage. Each person gets an individual score (FRS) that will change as the person ages and has changes in cholesterol and blood pressure levels.

After its publication of the FRS in 1998, other scoring systems were developed, and there were updates to FRS. All of these systems had limitations (e.g. they claimed validity in a given age range) and they had different input factors and predicted different types of cardiovascular diseases. The population of patients they used to develop the algorithms were different. But, many have been validated – that is, independent testers applied the algorithms to patient pools and found results close to the predictions.

What is the purpose of these scores?

First, a high score can help change patient behavior by alerting them to risk. Some would call this "scared straight" method mean or poor bedside manner, but it is effective for some patients.

More importantly, the probability result from the system helps doctors prescribe treatment. Indeed, the most current guidelines for US doctors, the 2013 AHA/ACC guidelines, explicitly call for the results of a score to be used when determining whether to employ statin medication, when possible. You might think this is too simplistic, but use of a probabilistic method is partly a response to critics who objected to treating to target cholesterol numbers. The critics had said just focusing on cholesterol levels was wrong. And other factors should be considered. So that’s what the scoring systems are – they take into account multiple factors. They were calculated by a statistical process called Multivariate Regression.

Inputs to scoring systems

Different systems require some or all of these:

Age – in years
Sec – male or female
Total Cholesterol (TC) – mg//dl
HDL – mg/dl
Systolic blood pressure (mmHg) – the higher number on the blood pressure reading
Blood pressure treatment (yes or no)
Current smoking (yes or no)
Family history of cardiovascular disease in first degree relative aged <60 years (yes or no)
C-reactive protein level in the blood – mg/dl

Outputs

Some of the following:

Cardiovascular death
Nonfatal MI
Nonfatal stroke
Coronary revascularization
Transient ischemic attack
Intermittent claudication
Heart Failure
Coronary insufficiency or angina
Coronary revascularization
Fatal or nonfatal stroke
Transient ischemic attack

Validation and Simplicity

The scoring systems have been “validated” – meaning independent analysts have calculated scores for patients and looked at how those patients have fared and seen results match the predictions. The major scoring systems recommended by public health authorities have been validated.

The great thing about the systems is that scores it can be calculated from only a few measurements. The downside is that they can’t be perfect and can produce only a percentage probability of a problem.

Scoring Systems

Joint British Societies Risk Calculator - http://www.jbs3risk.com/JBS3Risk.swf

ACC/AHA pooled cohort hard CVD risk calculator (2013) - http://tools.acc.org/ASCVD-Risk-Estimator/

Reynolds CVD risk score (both men and woman) - http://www.reynoldsriskscore.org/default.aspx

QRISK®2-2015 Web Calculator. - http://www.qrisk.org/

The Multi-Ethnic Study of Atherosclerosis - https://www.mesa-nhlbi.org/

Do the different scoring systems produce different estimates?

Of course they do. Otherwise there would be no point in developing a new estimation algorithm. They differ in inputs and outputs

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