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

Kangping Hui¹, Chengying Hong², Yihan Xiong³

¹The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong Province, People's Republic of China.

Infection and Drug Resistance

|July 8, 2024

Summary

Predict Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS) in tropical disease patients using early ICU data. Key indicators like RDW-CV, hemofiltration, and mechanical ventilation predict PICS onset and improve patient outcomes.

Keywords:

LASSO regression mortality persistent inflammation immunosuppression catabolism syndrome predictive model sepsis

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Area of Science:

Critical Care Medicine
Infectious Diseases
Pathophysiology

Background:

Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS) is a critical complication in Intensive Care Unit (ICU) patients, particularly those with tropical diseases.
Early identification and management of PICS are crucial for improving patient survival and clinical outcomes.

Purpose of the Study:

To develop a comprehensive, multi-level approach for predicting and managing PICS in the first 14 days of ICU admission for patients with tropical diseases.
To identify key predictive variables from clinical and microbial data for early PICS detection.

Main Methods:

A retrospective analysis of 1733 ICU patients admitted between December 2016 and July 2019.
Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify significant predictors of PICS from disease severity and laboratory indices.
A predictive model was constructed and validated using an independent cohort.

Main Results:

13.79% of patients developed PICS, with a 38.08% mortality rate.
Key predictors identified include 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.
The predictive model demonstrated strong performance with an Area Under the Curve (AUC) of 0.828 (validation AUC: 0.848).

Conclusions:

RDW-CV, HF, MV, NE, lactic acidosis, and MDR infection upon ICU admission are pivotal for prognosticating PICS in tropical disease patients.
Timely, targeted interventions based on these predictors can significantly improve clinical outcomes.
This study provides a valuable tool for proactive PICS management in tropical disease contexts.