Standardization of risk prediction model reporting in cancer‐associated thrombosis: Communication from the ISTH SSC subcommittee on hemostasis and malignancy. (30th May 2022) - Sanfilippo, Kristen M.; Wang, Tzu‐Fei; Carrier, Marc; Falanga, Anna; Gage, Brian F.; Khorana, Alok A.; Maraveyas, Anthony; Soff, Gerald A.; Wells, Phillip S.; Zwicker, Jeffrey I.
Abstract: Since the development of the Khorana score to predict risk of cancer‐associated venous thromboembolism (VTE), many modified and de novo risk prediction models (RPMs) have been proposed. Comparison of the prognostic performance across models requires comprehensive reporting and standardized methods for model development, validation and evaluation. To improve the standardization of RPM reporting, the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) tool was published in 2015. To better understand the quality of reporting and development of RPMs for cancer‐associated VTE, we performed a literature search of published RPMs and assessed each model using the TRIPOD checklist. Our results yielded 29 RPMs for which 30 items were evaluated. There was a non‐significant ( p  = 0.15) improvement in reporting of the 30 items in the post‐TRIPOD era (81%) versus the pre‐TRIPOD era (75%). Of seven items (title, sample size, missing data handling, baseline demographics, methods and results for model performance, and supplemental resources) with the lowest reporting in the pre‐TRIPOD era (<70%), there was an average improvement of 22% in the post‐TRIPOD era. Only two of the 22 studies published in the post‐TRIPOD era acknowledged compliance with TRIPOD. Informed by the results of this assessment, the Scientific and Standardization Committee (SSC) Subcommittee on Hemostasis & Malignancy of the International Society on Thrombosis and Hemostasis (ISTH) advocates for standardization of four key elements of RPMs for cancer‐associated VTE: (1) inclusion of the TRIPOD checklist, (2) clear definition of the derivation population, with justification of sample size, (3) clear definition of predictors, and (4) external validation prior to implementation.