Prediction of Mortality in Surgical Intensive Care Unit Patients Using Machine Learning Algorithms

Kyongsik Yun; Jihoon Oh; Tae Ho Hong; Eun Young Kim

doi:10.3389/fmed.2021.621861

Prediction of Mortality in Surgical Intensive Care Unit Patients Using Machine Learning Algorithms

Front Med (Lausanne). 2021 Mar 31:8:621861. doi: 10.3389/fmed.2021.621861. eCollection 2021.

Authors

Kyongsik Yun¹, Jihoon Oh², Tae Ho Hong³, Eun Young Kim⁴

Affiliations

¹ Computation and Neural Systems, California Institute of Technology, Pasadena, CA, United States.
² Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea.
³ Division of Hepato-Biliary and Pancreas Surgery, Department of Surgery, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea.
⁴ Division of Trauma and Surgical Critical Care, Department of Surgery, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea.

Abstract

Objective: Predicting prognosis of in-hospital patients is critical. However, it is challenging to accurately predict the life and death of certain patients at certain period. To determine whether machine learning algorithms could predict in-hospital death of critically ill patients with considerable accuracy and identify factors contributing to the prediction power. Materials and Methods: Using medical data of 1,384 patients admitted to the Surgical Intensive Care Unit (SICU) of our institution, we investigated whether machine learning algorithms could predict in-hospital death using demographic, laboratory, and other disease-related variables, and compared predictions using three different algorithmic methods. The outcome measurement was the incidence of unexpected postoperative mortality which was defined as mortality without pre-existing not-for-resuscitation order that occurred within 30 days of the surgery or within the same hospital stay as the surgery. Results: Machine learning algorithms trained with 43 variables successfully classified dead and live patients with very high accuracy. Most notably, the decision tree showed the higher classification results (Area Under the Receiver Operating Curve, AUC = 0.96) than the neural network classifier (AUC = 0.80). Further analysis provided the insight that serum albumin concentration, total prenatal nutritional intake, and peak dose of dopamine drug played an important role in predicting the mortality of SICU patients. Conclusion: Our results suggest that machine learning algorithms, especially the decision tree method, can provide information on structured and explainable decision flow and accurately predict hospital mortality in SICU hospitalized patients.

Keywords: anesthesia and intensive care; informatics; intensive care; machine learning; surgery.