Abstract
BackgroundClinical predictive models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision-making and individualize care. The Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry is a comprehensive database of cardiovascular disease (CVD) CPMs. The Registry was last updated in 2012, and there continues to be substantial growth in the number of available CPMs.MethodsWe updated a systematic review of CPMs for CVD to include articles published from January 1990 to March 2015. CVD includes coronary artery disease (CAD), congestive heart failure (CHF), arrhythmias, stroke, venous thromboembolism (VTE), and peripheral vascular disease (PVD). The updated Registry characterizes CPMs based on population under study, model performance, covariates, and predicted outcomes.ResultsThe Registry includes 747 articles presenting 1083 models, including both prognostic (n = 1060) and diagnostic (n = 23) CPMs representing 183 distinct index condition/outcome pairs. There was a threefold increase in the number of CPMs published between 2005 and 2014, compared to the prior 10-year interval from 1995 to 2004. The majority of CPMs were derived from either North American (n = 455, 42%) or European (n = 344, 32%) populations. The database contains 265 CPMs predicting outcomes for patients with coronary artery disease, 196 CPMs for population samples at risk for incident CVD, and 158 models for patients with stroke. Approximately two thirds (n = 701, 65%) of CPMs report a c-statistic, with a median reported c-statistic of 0.77 (IQR, 0.05). Of the CPMs reporting validations, only 333 (57%) report some measure of model calibration. Reporting of discrimination but not calibration is improving over time (p for trend < 0.0001 and 0.39 respectively).ConclusionsThere is substantial redundancy of CPMs for a wide spectrum of CVD conditions. While the number of CPMs continues to increase, model performance is often inadequately reported and calibration is infrequently assessed. More work is needed to understand the potential impact of this literature.
Highlights
Clinical predictive models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision-making and individualize care
Three hundred seventy-four CPMs were added to the Registry during this update
There was a threefold increase in the number of CPMs published between 2005 and 2014, compared to the prior 10-year interval (1995–2004) (Fig. 2)
Summary
Clinical predictive models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision-making and individualize care. There have been recent collaborative efforts to understand the extent and Wessler et al Diagnostic and Prognostic Research (2017) 1:20 limitations of the CPM literature, to establish methodological standards [7] and reporting guidelines [8], and to create a community of researchers dedicated to advancing this field, including the creation of a new journal [9] To contribute to this broad effort to better understand the scope and limitations of the literature, we created the Tufts Predictive Analytics and Comparative Effectiveness (PACE) Clinical Predictive Model (CPM) Registry, which describes published CPMs for patients at risk for and with known CVD. The Registry is available at http://pace.tuftsmedical center.org/cpm to aid clinicians and researchers in understanding the state of CPM development across the spectrum of CVD
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