Machine learning to identify risk factors associated with the development of ventilated hospital-acquired pneumonia and mortality: implications for antibiotic therapy selection

Anthony Sophonsri; Mimi Lou; Pamela Ny; Emi Minejima; Paul Nieberg; Annie Wong-Beringer

doi:10.3389/fmed.2023.1268488

Machine learning to identify risk factors associated with the development of ventilated hospital-acquired pneumonia and mortality: implications for antibiotic therapy selection

Front Med (Lausanne). 2023 Dec 15:10:1268488. doi: 10.3389/fmed.2023.1268488. eCollection 2023.

Authors

Anthony Sophonsri¹, Mimi Lou¹, Pamela Ny², Emi Minejima^{1

3}, Paul Nieberg⁴, Annie Wong-Beringer^{1

2}

Affiliations

¹ Department of Clinical Pharmacy, University of Southern California, Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States.
² Department of Pharmacy, Huntington Hospital, Pasadena, CA, United States.
³ Department of Pharmacy, Los Angeles General Medical Center, Los Angeles, CA, United States.
⁴ Department of Medicine - Infectious Diseases, Huntington Hospital, Pasadena, CA, United States.

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% p = 0.005), alcohol use disorder (27% vs. 7.1%, p < 0.0001), and were hospitalized within the past 30 days (30.4% vs. 19.5%, p = 0.02). An immediate need for ventilatory support occurred in 70% of vHABP patients on the day of diagnosis. Mortality was the highest in vHABP followed by VABP and nvHABP groups (44.6% vs. 36% vs. 14.3%, p < 0.0001). Nearly all (96%) vHABP patients had positive cultures, with Gram-negative pathogens accounting for 58.8% whereby 33.0% were resistant to extended-spectrum β-lactams (ESBLs), ceftriaxone (17.5%), fluoroquinolones (20.6%), and carbapenems (12.4%). Up to half of the vHABP patients with ESBL-Enterobacterales or P. aeruginosa did not receive an effective empiric regimen; over 50% increase in mortality rate was observed among patients whom effective therapy was initiated past the day of pneumonia diagnosis. Risk factors associated with vHABP development were alcohol use disorder, APACHE II score, vasopressor therapy prior to infection, and culture positive for ESBL-Enterobacterales whereas history of hospitalization in the past 30 days, active malignancy, isolation of ceftriaxone-resistant pathogens or Pseudomonas aeruginosa, and vasopressor therapy were risk factors for vHABP-associated mortality.

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.

Keywords: critical care; machine learning; mechanical ventilation; multidrug resistance; pneumonia.

Abstract

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