학술논문
Machine learning to identify risk factors associated with the development of ventilated hospital-acquired pneumonia and mortality: implications for antibiotic therapy selection
Document Type
article
Source
Frontiers in Medicine, Vol 10 (2023)
Subject
Language
English
ISSN
2296-858X
Abstract
BackgroundAmong patients with nosocomial bacterial pneumonia, those who decompensated to requiring mechanical ventilation (vHABP) faced the highest mortality followed by ventilator-associated pneumonia (VABP) and non-ventilated hospital-acquired pneumonia (nvHABP). The objectives of this study were to identify risk factors associated with the development and mortality of vHABP and to evaluate antibiotic management.MethodsA multicenter retrospective cohort study of adult inpatients with nosocomial pneumonia during 2014–2019 was performed. Groups were stratified by vHABP, nvHABP, and VABP and compared on demographics, clinical characteristics, treatment, and outcomes. Multivariable models were generated via machine learning to identify risk factors for progression to vHABP as well as pneumonia-associated mortality for each cohort.Results457 patients (32% nvHABP, 37% vHABP, and 31% VABP) were evaluated. The vHABP and nvHABP groups were similar in age (median age 66.4 years) with 77% having multiple comorbidities but more vHABP patients had liver disease (18.2% vs. 7.7% p = 0.005), alcohol use disorder (27% vs. 7.1%, p