ArticleJournal of inflammation research2024
A Nomogram for Predicting Mortality in Patients with Pneumonia-Associated Acute Respiratory Distress Syndrome (ARDS).
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Novel Phenotypes of Acute Respiratory Failure and Differential Response to Awake Prone Positioning: A Multi-Cohort Study.MedComm · 2026Article
- A gradient boosting machine model for predicting prognosis in patients with acute respiratory distress syndrome.American journal of translational research · 2026Article
- A prediction model for 30-day mortality in patients with ARDS admitted to the intensive care unit.European journal of medical research · 2025Article
- An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome.Frontiers in medicine · 2025Article
- Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome.Frontiers in medicine · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Background: There is no predictive tool developed for pneumonia-associated acute respiratory distress syndrome (ARDS) specifically so far, and the clinical risk classification of these patients is not well defined. Our study aims to construct an early prediction model for hospital mortality in patients with pneumonia-associated ARDS. Methods: In this single-center retrospective study, consecutive patients with pneumonia-associated ARDS admitted into intensive care units (ICUs) in West China Hospital of Sichuan University in China between January 2012 and December 2018 were enrolled. The least absolute shrinkage and selection operator (LASSO) regression and then multivariate logistic regression analysis were used to identify independent predictors which were used to develop a nomogram. We evaluated the performance of differentiation, calibration, and clinical utility of the nomogram. Results: The included patients were divided into the training cohort (442 patients) and the testing cohort (190 patients) with comparable baseline characteristics. The independent predictors for hospital mortality included age (OR: 1.04; 95% CI: 1.02, 1.05), chronic cardiovascular diseases (OR: 2.62; 95% CI: 1.54, 4.45), chronic respiratory diseases (OR: 1.87; 95% CI: 1.02, 3.43), lymphocytes (OR: 0.56; 95% CI: 0.39, 0.81), albumin (OR: 0.94; 95% CI: 0.90, 1.00), creatinine (OR: 1.00; 95% CI: 1.00, 1.01), D-dimer (OR: 1.06; 95% CI: 1.03, 1.09) and procalcitonin (OR: 1.14; 95% CI: 1.07, 1.22). A web-based dynamic nomogram (https://h1234.shinyapps.io/dynnomapp/) was constructed based on these factors. The concordance index (C index) of the nomogram was 0.798 (95% CI: 0.756, 0.840) in the training cohort and 0.808 (95% CI: 0.747, 0.870) in testing cohort. The precision-recall (PR) curves, calibration curves, decision curve analyses (DCA) and clinical impact curves showed that the nomogram has good predictive value and clinical utility. Conclusion: We developed and evaluated a convenient nomogram consisting of 8 clinical characteristics for predicting mortality in patients with pneumonia-associated ARDS.
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