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TRV-2026-1050Certified recordPeer-reviewed

Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era

Background Technological innovation in total joint arthroplasty (TJA) has largely focused on intraoperative precision through robotics, navigation, and implant design, while preoperative decision-making remains comparatively underdeveloped. Accurate estimation of patient-specific risk is central to surgical indications, yet existing tools provide limited resolution for consequential outcomes such as 1-year mortality. Methods A machine learning model was developed using the TriNetX Research Network. Patients unde…

Health · G Space — documented gain · certified 2026-09-10 · v1 · article view · machine-readable

Current reading — gain

An XGBoost model using 43 routine preoperative variables estimated 1-year mortality after total knee and hip arthroplasty with AUROC 0.761 and stable calibration, stratifying patients so the top 5% had 6.2-fold higher mortality than baseline.

What this doesn’t fix

Model evaluated only on internal validation without external validation, and calibration showed modest overprediction in the highest risk decile, indicating need for further validation before clinical deployment.

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Truvace Impact Record TRV-2026-1050, v1: “Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era.” Truvace, 2026-09-10. /record/TRV-2026-1050 (accessed at citation time). sha256 1c194d3bbf06d04f…

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