ArticleJournal of intensive care2018
Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study.
Article in Journal of intensive care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Association between early prophylactic heparin therapy and mortality in patients with septic shock secondary to respiratory infections: analysis based on MIMIC-IV database.BMC infectious diseases · 2025Article
- The Association Between Central Venous Pressure and Acute Kidney Injury Development in Patients with Septic Shock.Journal of clinical medicine · 2025Article
- Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.Scientific reports · 2025Article
- Nebulized Lipopolysaccharide Causes Delayed Cortical Neuroinflammation in a Murine Model of Acute Lung Injury.International journal of molecular sciences · 2024Article
- Sepsis-Related Lung Injury and the Complication of Extrapulmonary Pneumococcal Pneumonia.Diseases (Basel, Switzerland) · 2024Review
- Article
- Development and validation of an in-hospital mortality risk prediction model for patients with severe community-acquired pneumonia in the intensive care unit.BMC pulmonary medicine · 2023Article
- Resolvin D2 induces anti-microbial mechanisms in a model of infectious peritonitis and secondary lung infection.Frontiers in immunology · 2022Article
- Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPneumonia complicated by septic shock is associated with significant morbidity and mortality. Classification and regression tree methodology is an intuitive method for predicting clinical outcomes using binary splits. We aimed to improve the prediction of in-hospital mortality in patients with pneumonia and septic shock using decision tree analysis.
methodsClassification and regression tree models were applied to all patients with pneumonia-associated septic shock in the international, multicenter Cooperative Antimicrobial Therapy of Septic Shock database between 1996 and 2015. The association between clinical factors (time to appropriate antimicrobial therapy, severity of illness) and in-hospital mortality was evaluated. Accuracy in predicting clinical outcomes, sensitivity, specificity, and area under receiver operating curve of the final model was evaluated in training (
resultsThe study cohort contained 4222 patients, and in-hospital mortality was 51%. The mean time from onset of shock to administration of appropriate antimicrobials was significantly higher for patients who died (17.2 h) compared to those who survived (5.0 h). In the training dataset (
conclusionsOverall mortality (51%) in patients with pneumonia complicated by septic shock is high. Increased time to administration of antimicrobial therapy, Acute Physiology and Chronic Health Evaluation II Score, serum lactate, and age were associated with increased in-hospital mortality. Classification and regression tree methodology offers a simple prognostic model with good performance in predicting in-hospital mortality.
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