トランスジェンダー患者における自殺イベントリスクモデルの将来予測的検証

Suicide risk prediction models offer a promising avenue for early intervention, but their effectiveness in underrepresented populations remain uncertain. This study evaluated VSAIL's, a real-world, externally validated, and deployed suicide risk prediction model, performance in predicting suicide risk among transgender individuals. Transgender individuals were identified from electronic health record data and transgender status was verified via manual chart review. Results indicated modest discriminative ability (AUROC=0.777, AUPRC=0.115), however, a high rate of false negatives (77%), and significant miscalibration (Brier=0.023, Spiegelhalter's z-statistic p<0.001) reduced clinical utility. Findings underscore the importance of targeted subgroup validation and highlight limitations of general population-trained models in accurately identifying suicide risk among transgender patients. They also suggest the need for ongoing algorithm monitoring and subgroup-aware modeling strategies to improve predictive equity in marginalized populations.