ArticleFrontiers in medicine2023
Machine learning to identify risk factors associated with the development of ventilated hospital-acquired pneumonia and mortality: implications for antibiotic therapy selection.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.Frontiers in artificial intelligence · 2026Pooled it
- Integrating the Hospital Frailty Risk Score into Explainable Machine Learning to Predict Mortality in Older Adults with Pneumonia: A Chilean Population-Based Study.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning prediction of antibiotic resistance in Gram-negative pneumonia: a clinical-genomic integrated model.BMC microbiology · 2026Article
- Quantifying clinical indicators for identifying and prognosticating aspiration pneumonia in Chinese geriatric patients: a retrospective cohort study integrating multivariable regression and interpretable machine learning.Frontiers in medicine · 2026Article
- Random forest-based predictor selection and pneumonia risk probability assessment in acute respiratory infections: A cross-sectional study in Chongqing, China, 2023-2024.Biosafety and health · 2025Article
- Artificial intelligence in hospital infection prevention: an integrative review.Frontiers in public health · 2025Review
- The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review.Antibiotics (Basel, Switzerland) · 2024Review
- Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Background: Among 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. Methods: A 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. Results: 457 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% Conclusion: Patients with vHABP experienced an acute and severe decompensation upon diagnosis. The risk factors identified in this study could provide actionable data for clinicians to identify those at risk for vHABP at the onset of pneumonia and to target antimicrobial stewardship efforts to improve treatment success.
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