Submissions from the SPRINT Data Analysis Challenge on clinical risk prediction: a cross-sectional evaluation. Issue 3 (23rd March 2019)

• Record Type: Journal Article
• Authors: Jackevicius, Cynthia A; An, JaeJin; Ko, Dennis T; Ross, Joseph S; Angraal, Suveen; Wallach, Joshua D; Koh, Maria; Song, Jeeeun; Krumholz, Harlan M
• Is Part Of: BMJ open. Volume 9:Issue 3(2019)
• Publication Date: 2019-03-23
• Subjects: open science -- sprint -- clinical prediction -- risk prediction; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2018-025936
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: To collate and systematically characterise the methods, results and clinical performance of the clinical risk prediction submissions to the Systolic Blood Pressure Intervention Trial (SPRINT) Data Analysis Challenge. Design: Cross-sectional evaluation. Data sources: SPRINT Challenge online submission website. Study selection: Submissions to the SPRINT Challenge for clinical prediction tools or clinical risk scores. Data extraction: In duplicate by three independent reviewers. Results: Of 143 submissions, 29 met our inclusion criteria. Of these, 23/29 (79%) reported prediction models for an efficacy outcome (20/23 [87%] of these used the SPRINT study primary composite outcome, 14/29 [48%] used a safety outcome, and 4/29 [14%] examined a combined safety/efficacy outcome). Age and cardiovascular disease history were the most common variables retained in 80% (12/15) of the efficacy and 60% (6/10) of the safety models. However, no two submissions included an identical list of variables intending to predict the same outcomes. Model performance measures, most commonly, the C-statistic, were reported in 57% (13/23) of efficacy and 64% (9/14) of safety model submissions. Only 2/29 (7%) models reported external validation. Nine of 29 (31%) submissions developed and provided evaluable risk prediction tools. Using two hypothetical vignettes, 67% (6/9) of the tools provided expected recommendations for a low-risk patient, while 44% (4/9) did for a high-risk patient. Only 2/29 (7%) of the clinical risk prediction submissions have been published to date. Conclusions: Despite use of the same data source, a diversity of approaches, methods and results was produced by the 29 SPRINT Challenge competition submissions for clinical risk prediction. Of the nine evaluable risk prediction tools, clinical performance was suboptimal. By collating an overview of the range of approaches taken, researchers may further optimise the development of risk prediction tools in SPRINT-eligible populations, and our findings may inform the conduct of future similar open science projects.