Evidence map›Paper›PMID 42028134›Full record

ArticleJournal of intensive medicine2026

Clinical models for predicting 30-day mortality in ARDS: A focus on ventilatory ratio-defined subgroups.

Zhangwei Liang, Xinyi Luo, Ya Wang, Weilin Wang, Yuelin Liu, Ying Zhu, Juan Ouyang, Jie Zhang, Yin Xi, Yimin Li and 1 more

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Article in Journal of intensive medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Zhangwei LiangDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Xinyi LuoDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Ya WangDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Weilin WangDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Yuelin LiuDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Ying ZhuDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Juan OuyangDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Jie ZhangDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Yin XiDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Yimin LiDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.
Yonghao XuDepartment of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: It is supposed that acute respiratory distress syndrome (ARDS) patients with increased dead space, indicated by elevated ventilatory ratio (VR), had higher mortality. The difference in mortality predictors among ARDS patients categorized by VR remains unclear, so we aimed to investigate the risk factors for mortality prediction in subgroups defined by VR and develop risk models to predict the 30-day mortality in distinct ARDS subgroups. Methods: This study performed a retrospective analysis using the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD) databases, as well as data from NHLBI ARDS Clinical Trials Network (ARDSnet). Patients were divided into high VR (VR ≥2) and low VR (VR <2) subgroups based on baseline VR. In ARDSnet cohort, two 30-day mortality risk prediction models were constructed using logistic regression, internally validated using the bootstrap method, and externally validated in the MIMIC-IV and eICU-CRD cohorts. The performance of models was evaluated using receiver operating characteristic curves, Brier scores, calibration curves, and decision curve analysis (DCA) curves, and DeLong test was used to compare the predictive efficacy of different models in each subgroup. Results: This study included a total of 2977 ARDS patients: 1031 in the high VR training cohort, 1506 in the low VR training cohort, 159 in the high VR external validation cohort, and 281 in the low VR external validation cohort. Through stepwise regression analysis, 11 predictors were finally selected to construct high VR prediction model including age, body mass index (BMI), heart rate, mean arterial pressure, body temperature, blood urea nitrogen (BUN), bilirubin, minute ventilation, peak inspiratory pressure, inspired oxygen fraction (FiO Conclusion: This study developed and validated prognostic prediction models for patients of high and low VR subgroups, respectively. The models demonstrated good discrimination, calibration, and clinical utility. Prognostic risk factors differed between high and low VR subgroups, and prediction models developed for specific VR subgroups exhibited better predictive performance within their respective subgroup populations.

Indexed as

Acute respiratory distress syndromeDatabaseMortalityPredictionVentilatory ratio

Identifiers

PMID42028134
PMCPMC13100851

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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.