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Open Access | 10.1172/JCI172643
Find articles by Jackson, S. in: JCI | PubMed | Google Scholar
Published June 15, 2023 - More info
BACKGROUND Despite guidelines promoting the prevention and aggressive treatment of ventilator-associated pneumonia (VAP), the importance of VAP as a driver of outcomes in mechanically ventilated patients, including patients with severe COVID-19, remains unclear. We aimed to determine the contribution of unsuccessful treatment of VAP to mortality for patients with severe pneumonia.METHODS We performed a single-center, prospective cohort study of 585 mechanically ventilated patients with severe pneumonia and respiratory failure, 190 of whom had COVID-19, who underwent at least 1 bronchoalveolar lavage. A panel of intensive care unit (ICU) physicians adjudicated the pneumonia episodes and endpoints on the basis of clinical and microbiological data. Given the relatively long ICU length of stay (LOS) among patients with COVID-19, we developed a machine-learning approach called CarpeDiem, which grouped similar ICU patient-days into clinical states based on electronic health record data.RESULTS CarpeDiem revealed that the long ICU LOS among patients with COVID-19 was attributable to long stays in clinical states characterized primarily by respiratory failure. While VAP was not associated with mortality overall, the mortality rate was higher for patients with 1 episode of unsuccessfully treated VAP compared with those with successfully treated VAP (76.4% versus 17.6%, P < 0.001). For all patients, including those with COVID-19, CarpeDiem demonstrated that unresolving VAP was associated with a transitions to clinical states associated with higher mortality.CONCLUSIONS Unsuccessful treatment of VAP is associated with higher mortality. The relatively long LOS for patients with COVID-19 was primarily due to prolonged respiratory failure, placing them at higher risk of VAP.FUNDING National Institute of Allergy and Infectious Diseases (NIAID), NIH grant U19AI135964; National Heart, Lung, and Blood Institute (NHLBI), NIH grants R01HL147575, R01HL149883, R01HL153122, R01HL153312, R01HL154686, R01HL158139, P01HL071643, and P01HL154998; National Heart, Lung, and Blood Institute (NHLBI), NIH training grants T32HL076139 and F32HL162377; National Institute on Aging (NIA), NIH grants K99AG068544, R21AG075423, and P01AG049665; National Library of Medicine (NLM), NIH grant R01LM013337; National Center for Advancing Translational Sciences (NCATS), NIH grant U01TR003528; Veterans Affairs grant I01CX001777; Chicago Biomedical Consortium grant; Northwestern University Dixon Translational Science Award; Simpson Querrey Lung Institute for Translational Science (SQLIFTS); Canning Thoracic Institute of Northwestern Medicine.
Catherine A. Gao, Nikolay S. Markov, Thomas Stoeger, Anna Pawlowski, Mengjia Kang, Prasanth Nannapaneni, Rogan A. Grant, Chiagozie Pickens, James M. Walter, Jacqueline M. Kruser, Luke Rasmussen, Daniel Schneider, Justin Starren, Helen K. Donnelly, Alvaro Donayre, Yuan Luo, G.R. Scott Budinger, Richard G. Wunderink, Alexander V. Misharin, Benjamin D. Singer, The NU SCRIPT Study Investigators
The COVID-19 pandemic resulted in an unprecedented number of patients hospitalized in intensive care units (ICUs) because of severe SARS-CoV-2 infection (1). Respiratory failure in ICU patients, whether due to respiratory infection or other causes, may necessitate mechanical ventilation if oxygen levels cannot be restored with less invasive devices, such as continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP) ventilators. Although ventilators can be life-saving, their use is associated with some risks, including secondary bacterial infections that cause pneumonia. Prior studies have established that patients with SARS-CoV-2 infection on a mechanical ventilator are more likely to have ventilator-associated pneumonia (VAP) compared with other ICU patients, including those with influenza (2–4). This increased risk of VAP might occur because SARS-CoV-2 infection induces such profound lung injury compared with other infectious insults.
In this issue of the JCI, Gao, Markov, Stoeger, and colleagues developed a new approach to assess features that associate with VAP and mortality in a cohort of ICU patients with severe pneumonia and respiratory failure (5). The research team used electronic health records from 585 mechanically ventilated patients hospitalized at Northwestern Memorial Hospital, including 190 patients with COVID-19, 252 patients with bacterial pneumonia, 50 patients with other viral respiratory infections, and 93 control patients who had respiratory failure without pneumonia. The patients with COVID-19 spent more time in the ICU and were mechanically ventilated longer than other patient groups in the study. In order to compare the trajectory of the distinct patient groups in the study with varying lengths of stay in the ICU, the authors developed a machine-learning algorithm called CarpeDiem that used 44 different clinical parameters from electronic health records to cluster days with similar patient features. This approach was further validated utilizing electronic health records from the Beth Israel Deaconess Medical Center that are publicly available in the MIMIC-IV database.
This study revealed that unsuccessful treatment of VAP is associated with mortality across all patient groups. While it may be intuitive that unresolving secondary bacterial infection associates with increased mortality risk, the authors provide clear data definitively illustrating the occurrence of subsequent bacterial pneumonia in the different patient groups. Furthermore, their findings suggest that patients with COVID-19 are at higher risk of VAP associated with the prolonged respiratory failure triggered by SARS-CoV-2 infection relative to the other ICU patient groups in this study. An important implication of the work is that better approaches to detect and treat, or prevent, VAP could potentially improve outcomes. Further studies are needed to disentangle the many comorbidities associated with VAP, such as the selection of antibiotics, other drug exposures, and individual risk factors that make patients susceptible. Last, the machine-learning tool developed by the research team may have broader clinical applications in other areas of critical care medicine and could provide an innovative approach to examine other complications and treatments in the ICU.
Conflict of interest: The author has declared that no conflict of interest exists.
Copyright: © 2023, Jackson et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.
Reference information: J Clin Invest. 2023;133(12):e172643. https://doi.org/10.1172/JCI172643.
See the related article at Machine learning links unresolving secondary pneumonia to mortality in patients with severe pneumonia, including COVID-19.