Abstract 164: A Field Synopsis of Gender Effects in Clinical Prediction Models for Cardiovascular Disease

• Published in: Circulation Cardiovascular quality and outcomes Vol. 8; no. suppl_2
• Main Authors: Paulus, Jessica K, Lai, Lana, Raman, Gowri, Lutz, Jennifer S, Wessler, Benjamin S, Kent, David M
• Format: Journal Article
• Language: English
• Published: 01.05.2015
• ISSN: 1941-7713; 1941-7705
• DOI: 10.1161/circoutcomes.8.suppl_2.164

Abstract only Introduction: Gender differences in incidence, prognosis and treatment response have been observed across the spectrum of cardiovascular diseases (CVD). However, despite several decades of investigation, consistent findings regarding the magnitude and directionality of gender differences in CVD are elusive. We therefore conducted the first field synopsis of the role of gender on CVD conditions using a registry of clinical prediction models (CPMs). Methods: The Tufts PACE Center (CPM) Registry is based on a systematic review of cardiovascular CPMs published in English-language articles from 1/1990-5/2012. All included CPMs permit calculation of outcome probabilities from information provided in an equation, point score or nomogram. For the 15 most common unique index condition-outcome pair models, we calculated the proportion of models that included coefficients for the effect of gender on CVD incidence or prognosis, or presented gender-stratified models. The sample size, age distribution and proportion of females in the model development cohorts were summarized. Results: Out of 579 CPMs with CVD as either an index condition or outcome, 169 (29%) contained a coefficient for gender and 33 (6%) presented gender-stratified models. Gender was more frequently included as a covariate or stratification variable in models predicting incident CVD versus prognosis for patients with known CVD. Gender was included in 60/74 (81%) models predicting morbidity and/or mortality among a population sample, yet in only 9/53 (17%) of models predicting morbidity and/or mortality among patients with stroke, and 9/53 (17%) of models predicting mortality among patients with congestive heart failure. Gender was more likely to be included in CPMs developed from cohorts with larger sample sizes (150/299 cohorts with n≥2000 versus 54/277 cohorts with n<2000, p<0.001). For each 10% increase in the proportion of women in the model development cohort, there was a 22% increased odds of including gender in the CPM (OR = 1.22, 95% CI 1.08-1.39, p=0.002). Conclusions: Gender is an important prognostic factor in CVD, but is only included in about one third of published CPMs. Gender is much more frequently included as a predictor of incident CVD among the disease-free than of prognosis in those with established CVD.