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

Hui, Kangping; Hong, Chengying; Xiong, Yihan; Xia, Jinquan; Huang, Wei; Xia, Andi; Xu, Shunyao; Chen, Yuting; Zhang, Zhongwei; Chen, Huaisheng

2024

10.2147/IDR.S464906

INFECTION AND DRUG RESISTANCE   影响因子和分区

1178-6973

Introduction: 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.

sepsis persistent inflammation immunosuppression catabolism syndrome LASSO regression mortality predictive model

SCI

英语

其他

Natural Science Foundation of Guangdong Province[2024A1515012909] ; Guangzhou Municipal Science and Technology Bureau[2014A03J0643]

Infectious Diseases ; Pharmacology & Pharmacy

Infectious Diseases ; Pharmacology & Pharmacy

WOS:001261072000001

DOVE MEDICAL PRESS LTD

Web of Science

1.Jinan Univ, Clin Med Coll 2, Shenzhen, Guangdong, Peoples R China
2.Southern Univ Sci & Technol, Clin Med Coll 2, Shenzhen Peoples Hosp, Affiliated Hosp 1,Dept Crit Care Med,Jinan Univ, Shenzhen 518020, Guangdong, Peoples R China
3.Southern Univ Sci & Technol, Jinan Univ, Affiliated Hosp 1, Neurol Dept,Shenzhen Peoples Hosp,Clin Med Coll 2, Shenzhen, Guangdong, Peoples R China
4.Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen Peoples Hosp, Clin Med Coll 2,Dept Clin Med Res Ctr,Jinan Univ, Shenzhen, Guangdong, Peoples R China
5.Southern Univ Sci & Technol, Shenzhen Peoples Hosp, Affiliated Hosp 1, Dept Clin Microbiol,Shenzhen Peoples Hosp,Clin Med, Shenzhen, Guangdong, Peoples R China
6.Sichuan Univ, West China Hosp, Dept Crit Care Med, Chengdu, Sichuan, Peoples R China

Hui, Kangping,Hong, Chengying,Xiong, Yihan,et al. LASSO-Based Machine Learning Algorithm for Prediction of PICS Associated with Sepsis[J]. INFECTION AND DRUG RESISTANCE,2024,17.

Hui, Kangping.,Hong, Chengying.,Xiong, Yihan.,Xia, Jinquan.,Huang, Wei.,...&Chen, Huaisheng.(2024).LASSO-Based Machine Learning Algorithm for Prediction of PICS Associated with Sepsis.INFECTION AND DRUG RESISTANCE,17.

Hui, Kangping,et al."LASSO-Based Machine Learning Algorithm for Prediction of PICS Associated with Sepsis".INFECTION AND DRUG RESISTANCE 17(2024).