Field Synopsis of Sex in Clinical Prediction Models for Cardiovascular Disease

Abstract

Background Several widely-used risk scores for cardiovascular disease (CVD) incorporate sex effects, yet there has been no systematic summary of the role of sex in clinical prediction models (CPMs). To better understand the potential of these models to support sex-specific care, we conducted a field synopsis of sex effects in CPMs for CVD. Methods and Results We identified CPMs in the Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry, a comprehensive database of CVD CPMs published from 1/1990–5/2012. We report the proportion of models including sex effects on CVD incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 592 CVD-related CPMs, 193 (33%) included sex as a predictor or presented sex-stratified models. Sex effects were included in 78% (53/68) of models predicting incidence of CVD in a general population, versus only 35% (59/171), 21% (12/58) and 17% (12/72) of models predicting outcomes in patients with coronary artery disease (CAD), stroke, and heart failure, respectively. Among sex-including CPMs, women with heart failure were at lower mortality risk in 8/8 models; women undergoing revascularization for CAD were at higher mortality risk in 10/12 models. Factors associated with the inclusion of sex effects included the number of outcome events and using cohorts at-risk for CVD (rather than with established CVD). Conclusions While CPMs hold promise for supporting sex-specific decision making in CVD clinical care, sex effects are included in only one third of published CPMs.

Associations of the MCM6-rs3754686 proxy for milk intake in Mediterranean and American populations with cardiovascular biomarkers, disease and mortality: Mendelian randomization

“…Moreover, in contrast to other studies on the MCM6 -rs4988235 8 28 in Danes in which a dominant effect is reported, the association of the MCM6 -rs3754686 SNP appears to reflect an additive model. Although we observed the association between the MCM6 -rs3754686 SNP and milk intake to be statistically significant on studying men and women jointly, we wished to also investigate the associations in men and women separately given that previous studies had not provided these data in detail 8 28 and that prior evidence exists for sex-based heterogeneity both in milk intake 22 and CVD risk 17 18 29 30 . The results of the sex-differences in meta-analysis suggested that the effect of the association between the SNP and milk intake is greater in women than in men and reaches statistical significance (including after correction for multiple comparisons) when all populations are included.…”

“…Moreover, whereas evidence has accumulated regarding sex-specific differences in CVD incidence and risk factors 16 17 18 , as well as some sex-specific effects of several SNPs for CVD 19 20 , the implications of sex differences in Mendelian randomization studies have received less attention. Analysis of these sex-specific differences is needed as it may help us to strengthen causal inference, as previously reported 21 .…”

Tufts PACE Clinical Predictive Model Registry: update 1990 through 2015

“…The Registry might be consulted by reviewers and editors when evaluating the scientific and clinical merits of new models. The Registry also permits not only the study of CPMs within specific index conditions and outcomes, but also the study of predictor variables of interest across different index conditions [16, 17].…”

Racial and Ethnic Differences in Graft Loss Among Female Liver Transplant Recipients

“…THERE is growing recognition of the importance of individualized medicine, including gender-specific models of care. Sex differences in coronary and stroke risk, for example, have resulted in the incorporation of sex in clinical prediction models to better prognosticate cardiovascular outcomes [1,2]. Similarly, sex differences in risk of progressive kidney disease have led to the incorporation of sex in validated prediction tools for identifying individuals at risk for renal failure [3].…”