aceMachine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors
Purpose Immune checkpoint inhibitor (ICI)-induced cardiac immune-related adverse events (cardiac irAEs) are rare yet serious complications. Clinical assessment tools to identify at-risk patients would allow for more effective prevention strategies, thus improving clinical outcomes. We constructed various machine learning (ML) models to predict these events among patients receiving ICI therapy. Methods A cohort of patients receiving ICI therapy from 2010 to 2023 was identified from the TriNetX database. Cardiac i…
Machine learning models trained on TriNetX data stratified patients receiving immune checkpoint inhibitors into risk tiers for cardiac immune-related adverse events within 90 days, achieving moderate discrimination.
Findings are described as preliminary and based on retrospective diagnosis codes within 90 days, with authors noting need for longitudinal time-series data incorporating real-time labs and new therapies to refine predictions.
Evidence
- Peer-reviewedSupportive Care in Cancer2026-07-29
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Truvace Impact Record TRV-2026-0591, v1: “Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors.” Truvace, 2026-07-30. /record/TRV-2026-0591 (accessed at citation time). sha256 320cdab8c090bff9…
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