Machine Learning-Based Prediction of Postoperative Vasoplegia After Cardiac Surgery

초록

Objective: To develop and internally validate a machine-learning model for the early prediction of postoperative vasoplegia after cardiac surgery. Design: A retrospective cohort study with model development and internal validation. Setting: Tertiary academic intensive care units (ICUs) using routinely collected perioperative and postoperative clinical data. Participants: Adults admitted to intensive care after cardiac surgery. Interventions: None. Measurements and Main Results: Incident postoperative vasoplegia (greater than 6 to 48 or less hours after ICU arrival) was defined as either (1) concurrent cardiac index greater than 2.2 L/min/m2, systemic vascular resistance less than 800 dyne cent s/cm5, and mean arterial pressure less than 65 mmHg or (2) cardiac index greater than 2.2 L/min/m2 with combined norepinephrine plus epinephrine infusion greater than 0.2 mg/kg/min. Candidate predictors included demographics, comorbidities and perioperative factors, admission vital signs and laboratory tests, and vasoactive infusions within 0-6 hours of ICU arrival. On the held-out test set, the best-performing model achieved moderate discrimination (area under the receiver operating characteristic curve 0.74-0.75) with acceptable calibration. Influential features included underlying hypertension, congestive heart failure, diabetes, male sex, and preoperative angiotensin-converting enzyme inhibitor exposure. Conclusions: A machine-learning model using routinely available perioperative and early postoperative data accurately predicted postoperative vasoplegia after cardiac surgery. These findings support prospective evaluation for risk stratification and may inform targeted prevention and management strategies. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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