ArticleJournal of healthcare engineering2022
A Nomogram for Predicting the Mortality of Patients with Acute Respiratory Distress Syndrome.
Article in Journal of healthcare engineering, 2022. 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.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting 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
- A gradient boosting machine model for predicting prognosis in patients with acute respiratory distress syndrome.American journal of translational research · 2026Article
- Base excess serves as a mediator in the hemoglobin-intensive care unit stay length relationship in neonatal respiratory distress syndrome.Translational pediatrics · 2025Article
- A prediction model for 30-day mortality in patients with ARDS admitted to the intensive care unit.European journal of medical research · 2025Article
- Comparison of artificial intelligence and logistic regression models for mortality prediction in acute respiratory distress syndrome: a systematic review and meta-analysis.Intensive care medicine experimental · 2025Review
- A Nomogram for Predicting Mortality in Patients with Pneumonia-Associated Acute Respiratory Distress Syndrome (ARDS).Journal of inflammation research · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute respiratory distress syndrome (ARDS) is an acute lung injury associated with high morbidity and mortality. This study aimed to establish an accurate prediction model for mortality risk in ARDS. 70% of patients from the Medical Information Mart for Intensive Care Database (MIMIC-III) were selected as the training group, and the remaining 30% as the testing group. Patients from a Chinese hospital were used for external validation. Univariate and multivariate regressions were used to screen the independent predictors. The receiver operating characteristic curve (ROC) analysis, the Hosmer-Lemeshow test, and the calibration curve were used for evaluating the performance of the model. Age, hemoglobin, heart failure, renal failure, Simplified Acute Physiology Score II (SAPS II), immune function impairment, total bilirubin (TBIL), and PaO
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Registered trials
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.