Is the SMART risk prediction model ready for real-world implementation? A validation study in a routine care setting of approximately 380 000 individuals. (23rd June 2021)

• Record Type: Journal Article
• Authors: McKay, Ailsa J; Gunn, Laura H; Ference, Brian A; Dorresteijn, Jannick A N; Berkelmans, Gijs F N; Visseren, Frank L J; Ray, Kausik K
• Is Part Of: European journal of preventive cardiology. Volume 29:Number 4(2022)
• Page Start: 654
• Page End: 663
• Publication Date: 2021-06-23
• Subjects: Risk prediction -- Secondary prevention -- Cardiovascular disease -- Risk calculator; Cardiovascular system -- Diseases -- Prevention -- Periodicals; Cardiac patients -- Rehabilitation -- Periodicals; 616.12
• DOI: 10.1093/eurjpc/zwab093
• Languages: English
• ISSNs: 2047-4873

Abstract

Abstract: 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.