Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data

Abstract

Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-learning-based model to assess the patient’s individual risk of postoperative mortality based on preoperative data to allow analysis of personal risk factors. After ethical approval, a model for prediction of postoperative in-hospital mortality based on preoperative data of 66,846 patients undergoing elective non-cardiac surgery between June 2014 and March 2020 was created with extreme gradient boosting. Model performance and the most relevant parameters were shown using receiver operating characteristic (ROC−) and precision-recall (PR-) curves and importance plots. Individual risks of index patients were presented in waterfall diagrams. The model included 201 features and showed good predictive abilities with an area under receiver operating characteristic (AUROC) curve of 0.95 and an area under precision-recall curve (AUPRC) of 0.109. The feature with the highest information gain was the preoperative order for red packed cell concentrates followed by age and c-reactive protein. Individual risk factors could be identified on patient level. We created a highly accurate and interpretable machine learning model to preoperatively predict the risk of postoperative in-hospital mortality. The algorithm can be used to identify factors susceptible to preoperative optimization measures and to identify risk factors influencing individual patient risk.

Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication

“…When strictly comparing metrics such as F1 score, LLMs still underperform dedicated classification models utilizing tabular features. [45][46][47][48][49][50][51][52][53][54][55][56][57][58] Traditional machine learning models are rarely utilized in the clinical setting because of difficulty in interpreting a model's predictions. In contrast, LLMs present natural language explanations understandable to human clinicians, and can develop a rationale for each outcome variable of interest.…”

“…The LLM in this study exhibited very good performance at ASA-PS classification prediction, ICU admission prediction, and hospital mortality prediction. When strictly comparing metrics such as F1 score, LLMs still underperform dedicated classification models utilizing tabular features . Traditional machine learning models are rarely utilized in the clinical setting because of difficulty in interpreting a model’s predictions.…”

Machine learning-based model development for predicting risk factors of prolonged intra-aortic balloon pump therapy in patients with coronary artery bypass grafting

“…However, there is a lack of studies on the prediction of risk factors associated with prolonged LOIT in patients undergoing CABG surgery. Furthermore, the development of personalized systems is imperative for accurately predicting outcomes among specific operator groups, which highlights the importance of machine learning models [ 22 , 23 ]. The aim of personalized medicine has been to make models match the individual across multiple scales to solve clinical issues.…”