LASSO-Based Identification of Risk Factors and Development of a Prediction Model for Sepsis Patients

Chengying Hong,1 Yihan Xiong,2 Jinquan Xia,3 Wei Huang,4 Andi Xia,1 Shunyao Xu,1 Yuting Chen,1 Zhikun Xu,1 Huaisheng Chen,1 Zhongwei Zhang5 1Department 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, 518020, People’s Republic of China; 2Neurology Department, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 3Department 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, 518020, People’s Republic of China; 4Department 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, 518020, People’s Republic of China; 5Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of ChinaCorrespondence: 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, 518020, People’s Republic of China, Email sunshinic@hotmail.com Zhongwei Zhang, Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China, Email 716461751@qq.comObjective: The objective of this study was to utilize LASSO regression (Least Absolute Shrinkage and Selection Operator Regression) to identify key variables in septic patients and develop a predictive model f