Interpretable machine learning for predicting intraoperative oxygenation impairment during one-lung ventilation in thoracic surgery: a retrospective cohort study

Interpretable machine learning for predicting intraoperative oxygenation impairment during one-lung ventilation in thoracic surgery: a retrospective cohort study. Download PDF Abstract Intraoperative oxygenation impairment during one-lung ventilation poses significant challenges in thoracic anesthesia. Traditional statistical models often fail to capture complex non-linear clinical relationships.

This study aimed to evaluate machine learning algorithms for predicting this impairment and interpret potential feature associations using the SHAP method. Clinical data from 519 patients undergoing thoracic surgery were retrospectively analyzed. Model performance was measured using the area under the receiver operating characteristic curve.

SHAP was applied to explore non-linear associations and feature interactions, without implying biological causality. The random forest model exhibited the highest area under the curve (0.924), performing better than logistic regression (0.790). SHAP analysis identified positive end-expiratory pressure, preoperative arterial oxygen partial pressure, and body mass index as the primary predictors.

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