Utilizing Machine Learning for Proactive Post-Operative Patient Management (Preprint)

Background: Postoperative complications contribute significantly to patient morbidity and mortality. Early prediction of such complications could enable the care team to intervene promptly and improve patient outcomes. Existing surgical risk scores are easy to use but lack accuracy and do not provide individualized risk or guidance for clinical decision-making. Objective: This study aimed to develop and evaluate machine learning models using the INSPIRE perioperative dataset to predict whether an individual patient will require critical postoperative interventions-ventilatory support, extracorporeal membrane oxygenation (ECMO), an intra-aortic balloon pump (IABP), or continuous renal replacement therapy (CRRT). Methods: Four artificial neural network models and 4 random forest classifiers were trained and tested using the publicly available INSPIRE dataset (131,000 surgical cases). Preoperative laboratory data, medication use, surgery type, and intraoperative vital signs were used as inputs. The dataset was randomly divided into a 70% training set (91,000 cases) and a 30% test set (38,998 cases), with a temporal split to prevent leakage. Model performance was assessed using accuracy, sensitivity, specificity, positive and negative predictive values, and area under the curve (AUC). Results: The artificial neural network models achieved high predictive performance for ECMO (accuracy 98.9%; AUC 0.992), ventilatory support (accuracy 97.7%; AUC 0.995), IABP (accuracy 98.0%; AUC 0.992), and CRRT (accuracy 94.9%; AUC 0.983). In the test set of 38,998 patients, the neural network models correctly classified 38,569 cases for ECMO, 38,097 for ventilatory support, 38,230 for IABP, and 37,021 for CRRT, corresponding to accuracies of 98.9%, 97.7%, 98.0%, and 94.9%, respectively. The corresponding random forest classifiers achieved accuracies of 98.0%, 99.0%, 98.2%, and 96.5%, respectively, with similar AUC values. These results indicate that the models can reliably identify patients who will or will not require lifesaving postoperative interventions despite the marked class imbalance. Our work began in April 2023, with the research concept taking shape over the following 1 to 2 months. Model development spanned approximately 3 to 4 months and was followed by 1 month of testing and validation conducted by other computer scientists. Conclusions: Machine learning models trained and tested on the INSPIRE dataset can provide individualized risk estimates for critical postoperative interventions. By supplying accurate predictions before or during surgery, these models can support proactive perioperative planning (eg, intensive care unit bed allocation and device readiness).

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