A Machine Learning Approach for Postoperative Outcome Prediction: Surgical Data Science Application in a Thoracic Surgery Setting

Abstract

BACKGROUND: The use of innovative methodologies, such as Surgical Data Science (SDS), based on artificial intelligence (AI) could prove to be useful for extracting knowledge from clinical data overcoming limitations inherent in medical registries analysis. The aim of the study is to verify if the application of an AI analysis to our database could develop a model able to predict cardiopulmonary complications in patients submitted to lung resection. METHODS: We retrospectively analyzed data of patients submitted to lobectomy, bilobectomy, segmentectomy and pneumonectomy (January 2006-December 2018). Fifty preoperative characteristics were used for predicting the occurrence of cardiopulmonary complications. The prediction model was developed by training and testing a machine learning (ML) algorithm (XGBOOST) able to deal with registries characterized by missing data. We calculated the receiver operating characteristic curve, true positive rate (TPR), positive predictive value (PPV) and accuracy of the model. RESULTS: We analyzed 1360 patients (lobectomy: 80.7%, segmentectomy: 11.9%, bilobectomy 3.7%, pneumonectomy: 3.7%) and 23.3% of them experienced cardiopulmonary complications. XGBOOST algorithm generated a model able to predict complications with an area under the curve of 0.75, a TPR of 0.76, a PPV of 0.68. The model's accuracy was 0.70. The algorithm included all the variables in the model regardless of their completeness. CONCLUSIONS: Using SDS principles in thoracic surgery for the first time, we developed an ML model able to predict cardiopulmonary complications after lung resection based on 50 patient characteristics. The prediction was also possible even in the case of those patients for whom we had incomplete data. This model could improve the process of counseling and the perioperative management of lung resection candidates.

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