aceInterpretable Machine Learning Predicts Successful Weaning and ICU discharge alive Following Prone Positioning Ventilation in Moderate-to-Severe Acute Respiratory Distress Syndrome (ARDS) Secondary to Pneumonia
Objective We developed and validated interpretable machine learning models predicting successful weaning and survival to ICU discharge in patients with moderate-to-severe acute respiratory distress syndrome (ARDS) secondary to pneumonia receiving prone position ventilation (PPV). Methods We included 131 patients with moderate-to-severe ARDS secondary to pneumonia undergoing PPV for internal, temporal, and external validation. Models were developed using least absolute shrinkage and selection operator (LASSO) reg…
A support vector machine model for weaning held up across temporal and external validation and may help individualize risk stratification during ventilation and prone positioning, alongside clinical judgement.
The ICU discharge model was unstable in temporal and external validation, so its predictions need cautious interpretation and cannot yet support reliable use.
The ICU discharge model showed limited temporal and external stability, SHAP findings are hypothesis-generating rather than causal, and the authors state that multi-center prospective validation is still needed.
Evidence
- Peer-reviewedRespiratory Medicine2026-10-09
How should this claim be treated?
Truvace Impact Record TRV-2026-1655, v1: “Interpretable Machine Learning Predicts Successful Weaning and ICU discharge alive Following Prone Positioning Ventilation in Moderate-to-Severe Acute Respiratory Distress Syndrome (ARDS) Secondary to Pneumonia.” Truvace, 2026-10-17. /record/TRV-2026-1655 (accessed at citation time). sha256 83a5824a3579dc6c…
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