[Development and validation of an interpretable machine learning model for predicting reguirement of early invasive mechanical ventilation in patients with acute pancreatitis].

objectivesTo explore the risk factors for early respiratory failure in patients with acute pancreatitis (AP) and develop an interpretable machine learning model to predict the need for invasive mechanical ventilation (MV) within 72 h of ICU admission.

methodsThis retrospective cohort study was conducted using data of adult patients with acute pancreatitis from the MIMIC-IV database, excluding those receiving invasive MV at ICU admission. The primary outcome was initiation of invasive MV within 72 h after ICU admission. Multiple machine learning models were developed and internally validated, and the best-performing model was externally validated using the eICU-CRD database. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).

resultsA total of 517 patients with acute pancreatitis from the MIMIC-IV database were included, with 361 assigned to the training cohort and 156 to the internal testing cohort; 269 patients from the eICU-CRD database were included as the external validation cohort. The patients who developed early respiratory failure exhibited more severe organ dysfunction at ICU admission. The random forest model achieved an AUC of 0.908 in the training cohort, 0.733 in the internal testing cohort, and 0.681 in the external validation cohort. SHAP analysis identified sequential organ failure assessment (SOFA) score, use of vasoactive agents, and acute kidney injury (AKI) as the most important predictive factors.

conclusionsThe interpretable machine learning model demonstrates moderate predictive performance in both the internal and external validation cohorts and can be used for early risk assessment of invasive MV in patients with acute pancreatitis. The key predictive variables primarily reflect multi-organ dysfunction consistent with the underlying pathophysiological mechanisms of acute pancreatitis.