Overview of risk prediction models in cardiovascular disease research

Role of inflammation following an acute myocardial infarction: design of INFINITY

After a myocardial infarction, the inflammatory response is connected to major adverse outcomes such as ischemia-reperfusion injury, adverse cardiac remodeling, infarct size and poor prognosis. INFlammatIoN amI sTudY (INFINITY) is a multicenter, prospective, observational, cohort study designed to investigate the prognostic role of the cytokines IL-6, IL-10, IL-18 and IL-17 and the adipokines leptin, apelin and chemerin in patients with acute coronary syndrome. The study will test if these inflammatory biomarkers reflect different clinical manifestations of coronary artery disease and have a prognostic role in a 6-month follow-up period. This study represents an opportunity to investigate further the prognostic role of a selected combination of proinflammatory and anti-inflammatory biomarkers in the prognosis and risk stratification of acute coronary syndrome patients.

Ceramides and Phosphatidylcholines Associate with Cardiovascular Diseases in the Elderly

Background

The ceramide- and phospholipid-based cardiovascular risk score (CERT2) has been found to predict the risk for cardiovascular disease (CVD) events, especially cardiovascular mortality. In the present study, our aim was to estimate the predictive ability of CERT2 for mortality of CVD, coronary artery disease (CAD), and stroke in the elderly and to compare these results with those of conventional lipids.

Methods

We conducted a prospective study with an 18-year follow-up period that included a total of 1260 participants ages ≥64 years. Ceramides and phosphatidylcholines were analyzed using a LC-MS. Total cholesterol and triglycerides were performed by enzymatic methods and HDL cholesterol was determined by a direct enzymatic method. Concentrations of LDL-cholesterol were calculated according to the Friedewald formula.

Results

A higher score of CERT2 was significantly associated with higher CVD, CAD, and stroke mortality during the 18-year follow-up both in unadjusted and adjusted Cox regression models. The unadjusted hazard ratios (HRs) of CERT2 (95% CI) per SD for CVD, CAD, and stroke were 1.72 (1.52–1.96), 1.76 (1.52–2.04), and 1.63 (1.27–2.10), respectively, and the corresponding adjusted HRs (95% CI) per SD for CERT2 were 1.48 (1.29–1.69), 1.50 (1.28–1.75), and 1.41 (1.09–1.83). For conventional lipids, HRs per SD were lower than for CERT2.

Conclusions

The risk score CERT2 associated strongly with CVD, CAD, and stroke mortality in the elderly, while the association between these events and conventional lipids was weak.

Predicting major events in ambulatory patients with advanced heart failure awaiting heart transplantation: a pilot study

AIMS

In heart failure (HF), prognostic risk scores focus on all-cause mortality prediction. However, in advanced HF (AdHF) ambulatory patients awaiting heart transplantation (HTx), hospitalizations for acutely decompensated/worsening HF are relevant to clinical decision-making, but unpredicted by common risk functions.

METHODS

Among consecutive ambulatory patients added to the waitlist for HTx, event discriminators within 2 years from recruitment were assessed prospectively by area under the curve from receiver-operating characteristic curves, and by Cox proportional hazards models. Primary composite end points included the first between all-cause mortality and acutely decompensated/worsening HF requiring hospitalization and specific treatments.

RESULTS

In 89 patients, 36 primary composite events were recorded in a 2-year follow-up (40% of the study sample), and associated with nonischemic etiology and nonsinus rhythm, with lower systolic blood pressure (BP), lower plasma sodium and hemoglobin concentrations, and with higher N-terminal pro-brain natriuretic peptide (NT-proBNP), larger left ventricular (LV) dimensions and lower LV ejection fraction, greater proportion of significant mitral regurgitation, lower tricuspid annulus peak systolic excursion (TAPSE), lower percentage of predicted distance at 6-minute walking test (%p6MWT) and lower global symptoms burden by the Kansas City Cardiomyopathy Questionnaire, lower peak oxygen uptake by cardiopulmonary exercise, and higher wedge pressure by right heart catheterization, as compared with those with no events (P < 0.05). Only Metabolic Exercise Cardiac Kidney Index (MECKI) at recruitment was higher with patients reporting events, which predicted composite end points in addition to and independently of NT-proBNP, and lower systolic BP (all P < 0.05). In an alternative risk model, severe mitral regurgitation and lower TAPSE replaced MECKI and BP but not NT-proBNP (all P < 0.01).

CONCLUSION

Higher NT-pro-BNP, lower systolic BP and higher MECKI may contribute to predicting all-cause death and acutely decompensated/worsening HF among ambulatory patients awaiting HTx, with lower TAPSE and severe mitral regurgitation representing further alternative independent prognosticators.

A Novel Integrated Biomarker for Evaluation of Risk and Severity of Coronary Atherosclerosis, and Its Validation

Objective: To assess the feasibility of a combination of biochemical and imaging parameters for estimation of risk and severity of coronary atherosclerosis (CA), and to verify the created integrated biomarker (i-BIO) on independent cohort. Methods: Two cohorts of patients admitted to the hospital for coronary angiography and ultrasound carotid dopplerography were enrolled into the study (n = 205 and n = 216, respectively). The extent of CA was assessed by Gensini Score (GS). Results: According to GS, participants were distributed as follows: atherosclerosis-free (GS = 0), CA of any stage (GS > 0), subclinical CA (GS < 35), severe CA (GS ≥ 35). Based on the analysis of mathematical models, including biochemical and imaging parameters, we selected and combined the most significant variables as i-BIO. The ability of i-BIO to detect the presence and severity of CA was estimated using ROC-analysis with cut-off points determination. Risk of any CA (GS > 0) at i-BIO > 4 was 7.3 times higher than in those with i-BIO ≤ 4; risk of severe CA (GS ≥ 35) at i-BIO ≥ 9 was 3.1 times higher than at i-BIO < 9. Results on the tested cohort confirmed these findings. Conclusions: The i-BIO > 4 detected CA (GS > 0) with sensitivity of 87.9%, i-BIO ≥ 9 excluded patients without severe CA (GS < 35), specificity 79.8%. Validation of i-BIO confirmed the feasibility of i-BIO > 4 to separate patients with any CA with sensitivity 76.2%, and of i-BIO ≥ 9 to exclude atherosclerosis-free subjects with specificity of 84.0%.

"Would You Rather Jump Out of a Perfectly Good Airplane or Develop Cardiovascular Disease?" Validity and Reliability of the Cardiovascular Risk Perception Survey Among Military Personnel

BACKGROUND AND PURPOSE

Cardiovascular disease (CVD) is a major cause of death in the United States. The military are viewed as fit, ready to fight and that jumping out of perfectly good airplane or going to war is a greater risk than CVD. The purpose of this study was to determine reliability and validity of the Cardiovascular Risk Perception Survey (CRPS).

METHODS

A cross-sectional descriptive design was performed, supported by the Health Belief Model. Internal consistency reliability (Cronbach's alpha) and validity (principal component analysis) were examined.

RESULTS

Fifty-five participants were included in this study. Construct validity of the CRPS was supported by principal component analysis; indicating one scale that measured cardiovascular risk perception. The Cronbach's alpha is reported .865.

CONCLUSION

Initial psychometric testing of the CRPS provides evidence for construct validity and internal consistency reliability.

Predicting the Risk of Right Ventricular Failure in Patients Undergoing Left Ventricular Assist Device Implantation

Background:

Right ventricular failure (RVF) is a cause of major morbidity and mortality after left ventricular assist device (LVAD) implantation. It is, therefore, integral to identify patients who may benefit from biventricular support early post-LVAD implantation. Our objective was to explore the performance of risk prediction models for RVF in adult patients undergoing LVAD implantation.

Methods:

A systematic search was performed on Medline, Embase, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews from inception until August 2019 for all relevant studies. Performance was assessed by discrimination (via C statistic) and calibration if reported. Study quality was assessed using the Prediction Model Risk of Bias Assessment Tool criteria.

Results:

After reviewing 3878 citations, 25 studies were included, featuring 20 distinctly derived models. Five models were derived from large multicenter cohorts: the European Registry for Patients With Mechanical Circulatory Support, Interagency Registry for Mechanically Assisted Circulatory Support, Kormos, Pittsburgh Bayesian, and Mechanical Circulatory Support Research Network RVF models. Seventeen studies (68%) were conducted in cohorts implanted with continuous-flow LVADs exclusively. The definition of RVF as an outcome was heterogenous among models. Seven derived models (28%) were validated in at least 2 cohorts, reporting limited discrimination (C-statistic range, 0.53–0.65). Calibration was reported in only 3 studies and was variable.

Conclusions:

Existing RVF prediction models exhibit heterogeneous derivation and validation methodologies, varying definitions of RVF, and are mostly derived from single centers. Validation studies of these prediction models demonstrate poor-to-modest discrimination. Newer models are derived in cohorts implanted with continuous-flow LVADs exclusively and exhibit modest discrimination. Derivation of enhanced discriminatory models and their validations in multicenter cohorts is needed.

Predicting risk of substantial weight gain in German adults-a multi-center cohort approach

Background

A risk-targeted prevention strategy may efficiently utilize limited resources available for prevention of overweight and obesity. Likewise, more efficient intervention trials could be designed if selection of subjects was based on risk. The aim of the study was to develop a risk score predicting substantial weight gain among German adults.

Methods

We developed the risk score using information on 15 socio-demographic, dietary and lifestyle factors from 32 204 participants of five population-based German cohort studies. Substantial weight gain was defined as gaining ≥10% of weight between baseline and follow-up (>6 years apart). The cases were censored according to the theoretical point in time when the threshold of 10% baseline-based weight gain was crossed assuming linearity of weight gain. Beta coefficients derived from proportional hazards regression were used as weights to compute the risk score as a linear combination of the predictors. Cross-validation was used to evaluate the score's discriminatory accuracy.

Results

The cross-validated c index (95% CI) was 0.71 (0.67-0.75). A cutoff value of ≥475 score points yielded a sensitivity of 71% and a specificity of 63%. The corresponding positive and negative predictive values were 10.4% and 97.6%, respectively.

Conclusions

The proposed risk score may support healthcare providers in decision making and referral and facilitate an efficient selection of subjects into intervention trials.

Risk stratification based on components of the complete blood count in patients with acute coronary syndrome: A classification and regression tree analysis

To develop a risk stratification model based on complete blood count (CBC) components in patients with acute coronary syndrome (ACS) using a classification and regression tree (CART) method. CBC variables and the Global Registry of Acute Coronary Events (GRACE) scores were determined in 2,693 patients with ACS. The CART analysis was performed to classify patients into different homogeneous risk groups and to determine predictors for major adverse cardiovascular events (MACEs) at 1-year follow-up. The CART algorithm identified the white blood cell count, hemoglobin, and mean platelet volume levels as the best combination to predict MACE risk. Patients were stratified into three categories with MACE rates ranging from 3.0% to 29.8%. Kaplan-Meier analysis demonstrated MACE risk increased with the ascending order of the CART risk categories. Multivariate Cox regression analysis showed that the CART risk categories independently predicted MACE risk. The predictive accuracy of the CART risk categories was tested by measuring discrimination and graphically assessing the calibration. Furthermore, the combined use of the CART risk categories and GRACE scores yielded a more accurate predictive value for MACEs. Patients with ACS can be readily stratified into distinct prognostic categories using the CART risk stratification tool on the basis of CBC components.

Predicting Which Children with Juvenile Idiopathic Arthritis Will Have a Severe Disease Course: Results from the ReACCh-Out Cohort

OBJECTIVE

We studied an inception cohort of children with juvenile idiopathic arthritis (JIA) to (1) identify distinct disease courses based on changes over 5 years in 5 variables prioritized by patients, parents, and clinicians; and (2) estimate the probability of a severe disease course for each child at diagnosis.

METHODS

Assessments of quality of life, pain, medication requirements, patient-reported side effects, and active joint counts were scheduled at 0, 6, 12, 18, 24, 36, 48, and 60 months. Patients who attended at least 6 assessments were included. Multivariable cluster analysis, r², and silhouette statistics were used to identify distinct disease courses. One hundred candidate prediction models were developed in random samples of 75% of the cohort; their reliability and accuracy were tested in the 25% not used in their development.

RESULTS

Four distinct courses were identified in 609 subjects. They differed in prioritized variables, disability scores, and probabilities of attaining inactive disease and remission. We named them Mild (43.8% of children), Moderate (35.6%), Severe Controlled (9%), and Severe Persisting (11.5%). A logistic regression model using JIA category, active joint count, and pattern of joint involvement at enrollment best predicted a severe disease course (Controlled + Persisting, c-index = 0.87); 91% of children in the highest decile of risk actually experienced a severe disease course, compared to 5% of those in the lowest decile.

CONCLUSION

Children in this JIA cohort followed 1 of 4 disease courses and the probability of a severe disease course could be estimated with information available at diagnosis.

Cardiovascular risk assessment: a global perspective

An important strategy in primary prevention of cardiovascular diseases (CVD) is the early identification of high-risk individuals. Effective implementation of a strategy to identify these individuals in a clinical setting is reliant on the availability of appropriate CVD risk-assessment models and guideline recommendations. Several well-known models for CVD risk assessment have been developed and utilized in the USA and Europe, but might not be suitable for use in other regions or countries. Very few reports have discussed the development of risk-assessment models and recommendations from a global perspective. In this Review, we discuss why risk-assessment methods developed from studies in one geographical region or ethnic population might not be suitable for other regions or populations, and examine the availability and characteristics of predictive models in areas beyond the USA or Europe. In addition, we compare the differences in risk-assessment recommendations outlined in CVD clinical guidelines from developed and developing countries, and consider their potential effect on clinical practice. This overview of cardiovascular risk assessment from a global perspective can potentially guide low-to-middle-income countries in the development or validation of their own CVD risk-assessment models, and the formulation of recommendations in their own clinical guidelines according to local requirements.

Independent external validation of cardiovascular disease mortality in women utilising Framingham and SCORE risk models: a mortality follow-up study

Background

We conducted an independent external validation of three cardiovascular risk score models (Framingham risk score model and SCORE risk charts developed for low-risk regions and high-risk regions in Europe) on a prospective cohort of 4487 Australian women with no previous history of heart disease, diabetes or stroke. External validation is an important step to evaluate the performance of risk score models using discrimination and calibration measures to ensure their applicability beyond the settings in which they were developed.

Methods

Ten year mortality follow-up of 4487 Australian adult women from the National Heart Foundation third Risk Factor Prevalence Study with no baseline history of heart disease, diabetes or stroke. The 10-year risk of cardiovascular mortality was calculated using the Framingham and SCORE models and the predictive accuracy of the three risk score models were assessed using both discrimination and calibration.

Results

The discriminative ability of the Framingham and SCORE models were good (area under the curve > 0.85). Although all models overestimated the number of cardiovascular deaths by greater than 15%, the Hosmer-Lemeshow test indicated that the Framingham and SCORE-Low models were calibrated and hence suitable for predicting the 10-year cardiovascular mortality risk in this Australian population. An assessment of the treatment thresholds for each of the three models in identifying participants recommended for treatment were found to be inadequate, with low sensitivity and high specificity resulting from the high recommended thresholds. Lower treatment thresholds of 8.7% for the Framingham model, 0.8% for the SCORE-Low model and 1.3% for the SCORE-High model were identified for each model using the Youden index, at greater than 78% sensitivity and 80% specificity.

Conclusions

Framingham risk score model and SCORE risk chart for low-risk regions are recommended for use in the Australian women population for predicting the 10-year cardiovascular mortality risk. These models demonstrate good discrimination and calibration performance. Lower treatment thresholds are proposed for better identification of individuals for treatment.

Predicting the risk of physical disability in old age using modifiable mid-life risk factors

BACKGROUND

We aimed to investigate the relationship between potentially modifiable risk factors in middle age and disability after 13 years using the Framingham Offspring Study (FOS). We further aimed to develop a disability risk algorithm to estimate the risk of future disability for those aged 45-65 years.

METHODS

FOS is a longitudinal study. We used examination 5 (1991-1995; 'baseline') and examination 8 (2005-2008; 'follow-up'). We included participants aged between 45-65 years at 'baseline' with complete predictor and outcome measures (n=2031; mean age 53.9 years). Predictors considered were body mass index, smoking, hypertension, diabetes and dyslipidaemia. We used multinomial logistic regression to identify predictors of disability or death.We assessed external validity using Australian data.

RESULTS

By examination 8, 156 participants had disability and 198 had died. Disability was associated with smoking (OR (95% CI) 1.81 (1.18 to 2.78)); obesity (2.95 (1.83 to 4.77)); diabetes 1.96 (1.11 to 3.45) and being female (OR 1.67 (1.13 to 2.45). The model performed moderately well in predicting disability and death in an Australian population. Based on our algorithm, a 45-year-old man/woman with the combined risk factors of obesity, diabetes and smoking has similar likelihood of surviving free of disability to a 65-year-old man/woman without any of the same risk factors.

CONCLUSIONS AND RELEVANCE

The derived risk algorithm allows, for the first time, quantification of the substantial combined impact on future disability of key modifiable risk factors in mid-life. Here we demonstrated the combined impact of obesity, diabetes and smoking to be similar to 20 years of aging.

Development and application of chronic disease risk prediction models

Currently, non-communicable chronic diseases are a major cause of morbidity and mortality worldwide, and a large proportion of chronic diseases are preventable through risk factor management. However, the prevention efficacy at the individual level is not yet satisfactory. Chronic disease prediction models have been developed to assist physicians and individuals in clinical decision-making. A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual. Accurate prediction of an individual's future risk for a certain disease enables the comparison of benefits and risks of treatment, the costs of alternative prevention strategies, and selection of the most efficient strategy for the individual. A large number of chronic disease prediction models, especially targeting cardiovascular diseases and cancers, have been suggested, and some of them have been adopted in the clinical practice guidelines and recommendations of many countries. Although few chronic disease prediction tools have been suggested in the Korean population, their clinical utility is not as high as expected. This article reviews methodologies that are commonly used for developing and evaluating a chronic disease prediction model and discusses the current status of chronic disease prediction in Korea.

[Validation of APACHE II and SOFA scores in 2 cohorts of patients with suspected infection and sepsis, not admitted to critical care units]

OBJECTIVE

To validate the APACHE II and SOFA scores in patients with suspected infection in clinical settings other than intensive care units.

MATERIALS AND METHODS

A secondary analysis was performed on 2,530 adult patients participating in 2 cohort studies, with suspected infection as admission diagnosis within the first 24 h of hospitalization. The performance of both scoring systems was studied in order to set calibration and discrimination, respectively, on the outcomes such as mortality, admission to Intensive Care Unit, development of septic shock, or multiple organ dysfunctions.

RESULTS

The AUC-ROC values for mortality at discharge and on day 28 in the first cohort were around 0.50 for the SOFA and APACHE II scores; whereas for the second cohort the discrimination value was around 0.70. Calibration of both scoring systems for primary outcomes, according to Hosmer-Lemeshow test, showed p>.05 in the first cohort; while in the second cohort calibration it only showed a p>.05 in the case of the SOFA for mortality at hospital discharge.

CONCLUSION

This validation study of SOFA and APACHE II scores in patients with suspected infection in-hospital units other than the Intensive Care Unit, showed no consistent performance for calibration and discrimination. Its application in emergency and in-hospital patients is limited.

Adopting nested case-control quota sampling designs for the evaluation of risk markers

Two-phase study methods, in which more detailed or more expensive exposure information is only collected on a sample of individuals with events and a small proportion of other individuals, are expected to play a critical role in biomarker validation research. One major limitation of standard two-phase designs is that they are most conveniently employed with study cohorts in which information on longitudinal follow-up and other potential matching variables is electronically recorded. However for many practical situations, at the sampling stage such information may not be readily available for every potential candidates. Study eligibility needs to be verified by reviewing information from medical charts one by one. In this manuscript, we study in depth a novel study design commonly undertaken in practice that involves sampling until quotas of eligible cases and controls are identified. We propose semiparametric methods to calculate risk distributions and a wide variety of prediction indices when outcomes are censored failure times and data are collected under the quota sampling design. Consistency and asymptotic normality of our estimators are established using empirical process theory. Simulation results indicate that the proposed procedures perform well in finite samples. Application is made to the evaluation of a new risk model for predicting the onset of cardiovascular disease.

Development and validation of a risk score predicting substantial weight gain over 5 years in middle-aged European men and women

Background

Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population.

Methods

We used lifestyle and nutritional data from 53°758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining ≥10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample).

Results

Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63–0.65) in the derivation sample and 0.57 (95% CI  = 0.56–0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of ≥200 points were 9% and 96%, respectively.

Conclusion

The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.

Evaluating incremental values from new predictors with net reclassification improvement in survival analysis

Developing individualized prediction rules for disease risk and prognosis has played a key role in modern medicine. When new genomic or biological markers become available to assist in risk prediction, it is essential to assess the improvement in clinical usefulness of the new markers over existing routine variables. Net reclassification improvement (NRI) has been proposed to assess improvement in risk reclassification in the context of comparing two risk models and the concept has been quickly adopted in medical journals (Pencina et al., Stat Med 27:157-172, 2008). We propose both nonparametric and semiparametric procedures for calculating NRI as a function of a future prediction time [Formula: see text] with a censored failure time outcome. The proposed methods accommodate covariate-dependent censoring, therefore providing more robust and sometimes more efficient procedures compared with the existing nonparametric-based estimators (Pencina et al., Stat Med 30:11-21, 2011; Uno et al., Comparing risk scoring systems beyond the roc paradigm in survival analysis, 2009). Simulation results indicate that the proposed procedures perform well in finite samples. We illustrate these procedures by evaluating a new risk model for predicting the onset of cardiovascular disease.

Cardiovascular risk estimation in women with a history of hypertensive pregnancy disorders at term: a longitudinal follow-up study

BACKGROUND

Cardiovascular disease is associated with major morbidity and mortality in women in the Western world. Prediction of an individual cardiovascular disease risk in young women is difficult. It is known that women with hypertensive pregnancy complications have an increased risk for developing cardiovascular disease in later life and pregnancy might be used as a cardiovascular stress test to identify women who are at high risk for cardiovascular disease. In this study we assess the possibility of long term cardiovascular risk prediction in women with a history of hypertensive pregnancy disorders at term.

METHODS

In a longitudinal follow-up study, between June 2008 and November 2010, 300 women with a history of hypertensive pregnancy disorders at term (HTP cohort) and 94 women with a history of normotensive pregnancies at term (NTP cohort) were included. From the cardiovascular risk status that was known two years after index pregnancy we calculated individual (extrapolated) 10-and 30-year cardiovascular event risks using four different risk prediction models including the Framingham risk score, the SCORE score and the Reynolds risk score. Continuous data were analyzed using the Student's T test and Mann-Whitney U test and categorical data by the Chi-squared test. A poisson regression analysis was performed to calculate the incidence risk ratios and corresponding 95% confidence intervals for the different cardiovascular risk estimation categories.

RESULTS

After a mean follow-up of 2.5 years, HTP women had significantly higher mean (SD) extrapolated 10-year cardiovascular event risks (HTP 7.2% (3.7); NTP 4.4% (1.9) (p<.001, IRR 5.8, 95% CI 1.9 to 19)) and 30-year cardiovascular event risks (HTP 11% (7.6); NTP 7.3% (3.5) (p<.001, IRR 2.7, 95% CI 1.6 to 4.5)) as compared to NTP women calculated by the Framingham risk scores. The SCORE score and the Reynolds risk score showed similar significant results.

CONCLUSIONS

Women with a history of gestational hypertension or preeclampsia at term have higher predicted (extrapolated) 10-year and 30-year cardiovascular event risks as compared to women with a history of uncomplicated pregnancies. Further large prospective studies have to evaluate whether hypertensive pregnancy disorders have to be included as an independent variable in cardiovascular risk prediction models for women.

Risk of cardiovascular disease after pre-eclampsia and the effect of lifestyle interventions: a literature-based study

OBJECTIVE

This study addresses the following questions. Do cardiovascular risk factors fully explain the odds ratio of cardiovascular risk after pre-eclampsia? What is the effect of lifestyle interventions (exercise, diet, and smoking cessation) after pre-eclampsia on the risk of cardiovascular disease?

DESIGN

Literature-based study.

SETTING

N/A.

POPULATION OR SAMPLE

N/A.

METHODS

Data for the calculations were taken from studies identified by PubMed searches. First, the differences in cardiovascular risk factors after pre-eclampsia compared with an uncomplicated pregnancy were estimated. Second, the effects of lifestyle interventions on cardiovascular risk were estimated. Validated risk prediction models were used to translate these results into cardiovascular risk.

RESULTS

After correction for known cardiovascular risk factors, the odds ratios of pre-eclampsia for ischaemic heart disease and for stroke are 1.89 (IQR 1.76-1.98) and 1.55 (IQR 1.40-1.71), respectively. After pre-eclampsia, lifestyle interventions on exercise, dietary habits, and smoking cessation decrease cardiovascular risk, with an odds ratio of 0.91 (IQR 0.87-0.96).

CONCLUSIONS

Cardiovascular risk factors do not fully explain the risk of cardiovascular disease after pre-eclampsia. The gap between estimated and observed odds ratios may be explained by an additive risk of cardiovascular disease by pre-eclampsia. Furthermore, lifestyle interventions after pre-eclampsia seem to be effective in decreasing cardiovascular risk. Future research is needed to overcome the numerous assumptions we had to make in our calculations.

Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!

One of the most popular modeling approaches to genetic risk prediction is to use a summary of risk alleles in the form of an unweighted or a weighted genetic risk score, with weights that relate to the odds for the phenotype in carriers of the individual alleles. Recent contributions have proposed the use of Bayesian classification rules using Naïve Bayes classifiers. We examine the relation between the two approaches for genetic risk prediction and show that the methods are mathematically related. In addition, we study the properties of the two approaches and describe how they can be generalized to include various models of inheritance.

Cautionary tales in the interpretation of studies of tools for predicting risk and prognosis

Assessing future risk or prognosis in individual subjects is an often difficult and humbling task for clinicians. In recent times numerous prediction tools have been developed to make the task more accurate and thereby render management decisions more appropriate. If these tools are to be used effectively, an understanding is needed of their method of development, performance characteristics, ease of use and applicability in clinical settings, and potential impact on clinical decision-making. In this fourth article in a series on critical appraisal, we discuss questions that need to be asked of any new risk prediction tool.