Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Abstract

Risk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of -usually interactive and online available -tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology. AbstractRisk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of -usually interactive and online available -tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Current...

Risk Stratification in Patients with Ischemic Stroke and Residual Cardiovascular Risk with Current Secondary Prevention

“…The smooth calibration plot and confidence bounds were subsequently predicted from this model over the whole range of relevant predicted risks (cohort predicted risk quantile 0.025 up to 0.975). As event rates vary between geographic locations 8,26 and may be influenced by the selection of study participants, recalibration to the population of interest is often necessary. 6,19,25 The intercept of the SMART-REACH model for both CVD events and non-CVD mortality was recalibrated ("calibration-in-the-large") to Nor-COAST by subtracting the expected-observed ratio from the linear predictor (Supplementary Methods).…”

“…However, demonstrating adequate calibration might be a more relevant measure since knowing that the predicted risk reflects the actual risk is important for clinical treatment decisions. 8,36 We did not account for changes in risk factor levels over time. However, changes in risk factor levels after 3 months are not likely to affect predictive performance.…”

“…However, there is a substantial interindividual variation in the risk of recurrent events among patients with established cardiovascular disease (CVD). [6][7][8] This variation results from a composite of several prognostic features like age, genetics, cardiovascular risk factors, effectiveness of preventive therapy, competing risks and remaining life-expectancy. 6,9,10 Appropriate identification of patients at high risk is important because they most likely gain greatest clinical benefit from intensive treatment of cardiovascular risk factors, novel therapies on top of standard treatment 9,11,12 and a more intensive and multidisciplinary follow-up.…”

Εμπειρική Αποτίμηση Και Συστηματική Ανασκόπηση Προγνωστικών Δεικτών Και Προγνωστικών Μοντέλων Για Χρόνια Νοσήματα

“…Costs of ischemic heart disease are more than 109 billion dollars annually in US and this cost is expected to double by 2030 (68). All CVD risk factors discussed are important risk factors for MI and CHD mortality, and there are several risk-prediction tools available for the estimation of this risk, such as the Framingham Risk Score and the HEART SCORE charts, which are also recommended for clinical use in clinical practice guidelines (69). There are several prognostic factors that define the outcome of the disease once it is already established.…”

“…A prognostic or risk prediction model is a mathematical function that relates certain risk factors (covariates) to the probability (risk) of a particular outcome happening in a well-defined population and within a specific time period (295,296). Prediction models are more and more used in modern medicine to guide clinical decision making (69).…”