ArticleThe Journal of clinical investigation2023
Machine learning links unresolving secondary pneumonia to mortality in patients with severe pneumonia, including COVID-19.
Article in The Journal of clinical investigation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 53 citations in OpenAlex.
- A randomized, double-blind, placebo-controlled trial of IL-7 in critically ill patients with COVID-19.JCI insight · 2025Trial
- Comparing clinical assessments of pneumonia to bronchoalveolar lavage results in critically ill patients with suspected pneumonia.Annals of the American Thoracic Society · 2026Observational
- Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.BMC medical informatics and decision making · 2026Article
- Observational
- Pathogen-specific host responses define distinct pneumonia endotypes in the human lung.bioRxiv : the preprint server for biology · 2026Article
- Development of a Pilot Machine Learning Model to Predict Successful Short-Term Treatment Success in Critically Ill Patients With Community-Acquired Pneumonia.CHEST critical care · 2026Article
- An alveolar macrophage-targeted ciprofloxacin polymeric prodrug improves survival in a murine model ofAntimicrobial agents and chemotherapy · 2026Article
- Developing and externally validating machine learning models to forecast short-term risk of ventilator-associated pneumonia.medRxiv : the preprint server for health sciences · 2026Article
- Airway immunometabolic responses during pulmonary bacterial and viral infections.Cell reports · 2025Review
- Transitions in lung microbiota landscape associate with distinct patterns of pneumonia progression.Cell host & microbe · 2025Article
- Large language models accurately identify immunosuppression in intensive care unit patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Multi-omics profiling in severe pneumonia: the importance of robust study design.The European respiratory journal · 2025Article
- Antibiotic de-escalation patterns and outcomes in critically ill patients with suspected pneumonia as informed by bronchoalveolar lavage results.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Article
- Developing and validating machine learning models to predict next-day extubation.Scientific reports · 2025Article
- AMPK is necessary for Treg functional adaptation to microenvironmental stress during malignancy and viral pneumonia.The Journal of clinical investigation · 2025Article
- Regulatory T Cells Require TBET to Regulate Activated CD8+ T Cells During Recovery from Influenza.American journal of respiratory cell and molecular biology · 2025Article
- An Observational Cohort Study of Bronchoalveolar Lavage Fluid Galactomannan andOpen forum infectious diseases · 2025Article
- Deciphering respiratory viral infections by harnessing organ-on-chip technology to explore the gut-lung axis.Open biology · 2025Review
- Large language models outperform traditional structured data-based approaches in identifying immunosuppressed patients.medRxiv : the preprint server for health sciences · 2025Article
- "Good and bad investments" in public health stocks amid the COVID-19 shock: evidence from a transformer-based model.Frontiers in public health · 2025Article
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Authors and funding
21 authors at 2 institutions in 2 countries.
Funding
Abstract
BACKGROUNDDespite 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.METHODSWe 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.RESULTSCarpeDiem 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.CONCLUSIONSUnsuccessful 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.FUNDINGNational 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.