Is the SMART risk prediction model ready for real-world implementation? A validation study in a routine care setting of approximately 380 000 individuals

AIMS: Reliably quantifying event rates in secondary prevention could aid clinical decision-making, including quantifying potential risk reductions of novel, and sometimes expensive, add-on therapies. We aimed to assess whether the SMART risk prediction model performs well in a real-world setting. METHODS AND RESULTS: We conducted a historical open cohort study using UK primary care data from the Clinical Practice Research Datalink (2000-2017) diagnosed with coronary, cerebrovascular, peripheral, and/or aortic atherosclerotic cardiovascular disease (ASCVD). Analyses were undertaken separately for cohorts with established (≥6 months) vs. newly diagnosed ASCVD. The outcome was first post-cohort entry occurrence of myocardial infarction, stroke, or cardiovascular death. Among the cohort with established ASCVD [n = 244 578, 62.1% male, median age 67.3 years, interquartile range (IQR) 59.2-74.0], the calibration and discrimination achieved by the SMART model was not dissimilar to performance at internal validation [Harrell's c-statistic = 0.639, 95% confidence interval (CI) 0.636-0.642, compared with 0.675, 0.642-0.708]. Decision curve analysis indicated that the model outperformed treat all and treat none strategies in the clinically relevant 20-60% predicted risk range. Consistent findings were observed in sensitivity analyses, including complete case analysis (n = 182 482; c = 0.624, 95% CI 0.620-0.627). Among the cohort with newly diagnosed ASCVD (n = 136 445; 61.0% male; median age 66.0 years, IQR 57.7-73.2), model performance was weaker with more exaggerated risk under-prediction and a c-statistic of 0.559, 95% CI 0.556-0.562. CONCLUSIONS: The performance of the SMART model in this validation cohort demonstrates its potential utility in routine healthcare settings in guiding both population and individual-level decision-making for secondary prevention patients.