Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy.

Lina Zhao,Yi Li,Huadong Zhu,Yunying Wang,Zengzheng Ge

doi:10.3389/fncom.2021.739265

Abstract

Objective: The study aims to develop a mechanical learning model as a predictive model for predicting the appearance of sepsis-associated encephalopathy (SAE).Materials and Methods: The prediction model was developed in a primary cohort of 2,028 sepsis patients from June 2001 to October 2012, retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database. Least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. The model was developed using multivariable logistic regression analysis. The performance of the nomogram has been evaluated in terms of calibration, discrimination, and clinical utility.Results: There were nine particular features in septic patients that were significantly associated with SAE. Predictors of individualized prediction nomograms included age, rapid sequential evaluation of organ failure (qSOFA), and drugs including carbapenem antibiotics, quinolone antibiotics, steroids, midazolam, H2-antagonist, diphenhydramine hydrochloride, and heparin sodium injection. The area under the curve (AUC) was 0.743, indicating good discrimination. The prediction model showed calibration curves with minor deviations from the ideal predictions. Decision curve analysis (DCA) suggested that the nomogram was clinically useful.Conclusion: We propose a nomogram for the individualized prediction of SAE with satisfactory performance and clinical utility, which could aid the clinician in the early detection and management of SAE.

Highlights

Sepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction caused by a dysregulated host response without central nervous system (CNS) infection (Gofton and Young, 2012)
It is associated with higher severity on the Glasgow Coma Scale (GCS), sequential organ failure assessment score (SOFA), quick sequential organ failure assessment, the simplified acute physiology score (APACHE II) of patients followed by persistent cognitive and functional impairments (Iwashyna et al, 2010; Sonneville et al, 2017)
Continuous variables were expressed as the mean ± standard deviation (SD) or the median; categorical variables were expressed as frequency and percentage

Summary

Introduction

Sepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction caused by a dysregulated host response without central nervous system (CNS) infection (Gofton and Young, 2012). Symptoms and signs range from mild inattentiveness or disorientation, agitation, and hypersomnolence to more severe disturbance of consciousness and coma (Chung et al, 2020). 70% of the patients with bacteremia display neurological symptoms or signs ranging from lethargy to coma (Peidaee et al, 2018). SAE is associated with increased mortality, prolonged hospitalizations, and inpatient costs. It is associated with higher severity on the Glasgow Coma Scale (GCS), sequential organ failure assessment score (SOFA), quick sequential organ failure assessment (qSOFA), the simplified acute physiology score (APACHE II) of patients followed by persistent cognitive and functional impairments (Iwashyna et al, 2010; Sonneville et al, 2017). With a mortality rate of up to 63% (Eidelman et al, 1996), and morbidities mentioned above, SAE can have a major effect on the healthcare system, the economy, and the society

Methods

Results

Discussion

Conclusion