LASSO-Based Machine Learning Algorithm for Prediction of PICS Associated with Sepsis

Abstract

Kangping Hui,1,* Chengying Hong,2,* Yihan Xiong,3 Jinquan Xia,4 Wei Huang,5 Andi Xia,2 Shunyao Xu,2 Yuting Chen,2 Zhongwei Zhang,6 Huaisheng Chen2 1The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong Province, People’s Republic of China; 2Department of Critical Care Medicine, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong Province, People’s Republic of China; 3Neurology Department, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong Province, People’s Republic of China; 4Department of Clinical Medical Research Center, the Second Clinical Medical College, Jinan University (Shenzhen People’s Hospital), the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong Province, People’s Republic of China; 5Department of Clinical Microbiology, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong Province, People’s Republic of China; 6Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huaisheng Chen, Department of Critical Care Medicine, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong Province, People’s Republic of China, Email [email protected] Zhongwei Zhang, Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People’s Republic of China, Email [email protected]: This study aims to establish a comprehensive, multi-level approach for tackling tropical diseases by proactively anticipating and managing Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS) within the initial 14 days of Intensive Care Unit (ICU) admission. The primary objective is to amalgamate a diverse array of indicators and pathogenic microbial data to pinpoint pivotal predictive variables, enabling effective intervention specifically tailored to the context of tropical diseases.Methods: A focused analysis was conducted on 1733 patients admitted to the ICU between December 2016 and July 2019. Utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression, disease severity and laboratory indices were scrutinized. The identified variables served as the foundation for constructing a predictive model designed to forecast the occurrence of PICS.Results: Among the subjects, 13.79% met the diagnostic criteria for PICS, correlating with a mortality rate of 38.08%. Key variables, including red-cell distribution width coefficient of variation (RDW-CV), hemofiltration (HF), mechanical ventilation (MV), Norepinephrine (NE), lactic acidosis, and multiple-drug resistant bacteria (MDR) infection, were identified through LASSO regression. The resulting predictive model exhibited a robust performance with an Area Under the Curve (AUC) of 0.828, an accuracy of 0.862, and a specificity of 0.977. Subsequent validation in an independent cohort yielded an AUC of 0.848.Discussion: The acquisition of RDW-CV, HF requirement, MV requirement, NE requirement, lactic acidosis, and MDR upon ICU admission emerges as a pivotal factor for prognosticating PICS onset in the context of tropical diseases. This study highlights the potential for significant improvements in clinical outcomes through the implementation of timely and targeted interventions tailored specifically to the challenges posed by tropical diseases.Keywords: sepsis, persistent inflammation immunosuppression catabolism syndrome, LASSO regression, mortality, predictive model

Keywords

• sepsis
• persistent inflammation immunosuppression catabolism syndrome
• lasso regression
• mortality
• predictive model