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Suicide risk prediction models show limited accuracy for transgender individuals, with high false negatives and miscalibration. Targeted validation and subgroup-aware strategies are crucial for equitable risk assessment in marginalized populations.

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Area of Science:

  • Clinical Informatics
  • Psychiatry
  • Health Equity

Background:

  • Suicide risk prediction models are vital for early intervention.
  • Their performance in underrepresented groups, like transgender individuals, is often uncertain.
  • General population models may not accurately serve diverse demographic subgroups.

Purpose of the Study:

  • To evaluate the real-world performance of the VSAIL suicide risk prediction model in transgender individuals.
  • To assess the model's discriminative ability, calibration, and clinical utility within this specific population.
  • To identify limitations and inform strategies for improving predictive equity.

Main Methods:

  • Transgender individuals were identified using electronic health record data.
  • Transgender status was confirmed through manual chart review.
  • The VSAIL model's performance was assessed using metrics like AUROC, AUPRC, Brier score, and Spiegelhalter's z-statistic.

Main Results:

  • The VSAIL model demonstrated modest discriminative ability (AUROC=0.777, AUPRC=0.115).
  • A significant rate of false negatives (77%) was observed, reducing clinical utility.
  • The model showed significant miscalibration (Brier=0.023, p<0.001), indicating poor reliability.

Conclusions:

  • General population-trained suicide risk models have limitations in accurately predicting risk for transgender patients.
  • Targeted subgroup validation is essential to ensure the effectiveness and equity of predictive models.
  • Ongoing algorithm monitoring and subgroup-aware modeling are necessary to improve suicide risk prediction in marginalized populations.