Large-scale validation of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

ObjectiveTo assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation. Study Design and SettingWe evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation and assessed the change in discrimination (dAUC) between the derivation and the validation cohorts (n=1,147). ResultsPROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 529/556 CPMs (95%) were classified as high ROB, 20 (4%) low ROB, and 7 (1%) unclear ROB. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2%-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3%-2.6%; p<0.001). ConclusionHigh ROB is pervasive among published CPMs. It is associated with poor performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews. What is newO_LIHigh risk of bias is pervasive among published clinical prediction models C_LIO_LIHigh risk of bias identified with PROBAST is associated with poorer model performance at validation C_LIO_LIA subset of questions can distinguish between models with high and low risk of bias C_LI