Key result
An XGBoost machine learning model using preoperative variables predicted postoperative length of stay for severe patients with an average error within 3 days.
Why the study?
Understanding the length of stay of severe patients requiring general anesthesia is key to enhancing health outcomes, prompting evaluation of how machine learning can support resource allocation and clinical decision-making.
Can machine learning models using preoperative variables accurately predict postoperative length of stay in severe patients requiring general anesthesia?
Cohort (n=240,000)
Can machine learning models using preoperative variables accurately predict postoperative length of stay in severe patients requiring general anesthesia?
An XGBoost machine learning model using preoperative variables can predict postoperative length of stay with an average error of less than 3 days, identifying key risk factors such as direct bilirubin levels and calcium chloride medication.
May support preoperative planning in high-risk cases; leaves open prospective validation of clinical impact.
BACKGROUND Understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes. OBJECTIVE Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction. METHODS A retrospective cohort study was conducted from January 2018 to October 2020. A total cohort of 240,000 patients’ medical records was collected. The data were collected exclusively for preoperative variables to accurately analyze the predictive factors impacting the postoperative length of stay. The main outcome of this study is an analysis of the length of stay (in days) after surgery until discharge. The prediction was performed with ridge regression, random forest, XGBoost, and multi-layer perceptron neural network models. The XGBoost resulted in the best performance with an average error within 3 days. Moreover, we explain each feature’s contribution over the XGBoost model and further display distinct predictors affecting the overall prediction outcome at the patient level. RESULTS The risk factors that most importantly contributed to the stay after surgery were as follows: a direct bilirubin laboratory test, department change, calcium chloride medication, gender, and diagnosis with the removal of other organs. Our results suggest that healthcare providers take into account the risk factors such as the laboratory blood test, distributing patients, and the medication prescribed prior to the surgery. We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. CONCLUSIONS In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.
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Cho et al. (2023) conducted a cohort in Severe patients requiring general anesthesia (n=240,000). Machine learning prediction models (XGBoost) vs. Ridge regression, random forest, and multi-layer perceptron neural network models was evaluated on Length of stay (in days) after surgery until discharge. An XGBoost machine learning model using preoperative variables predicted postoperative length of stay for severe patients with an average error within 3 days.
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