Evidence map›Paper›PMID 41652587›Full record

ArticleBMC neurology2026

A nomogram for predicting the risk of acute respiratory distress syndrome in patients with severe acute brain injury.

Qiaoqiao You, Chang Su, Jianping Ye, Miaomiao Chen, JunLong Xu, Xian Zhang, Wen Hu

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In one paragraph

Article in BMC neurology, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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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

7 authors.

Qiaoqiao You *Department of Critical Care Medicine, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, 1188 Liyang Street, Liandu District, Lishui, Zhejiang, 323000, China.
Chang Su *Department of Neurosurgery, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, Lishui, 323000, Zhejiang, China.
Jianping Ye *Department of Critical Care Medicine, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, 1188 Liyang Street, Liandu District, Lishui, Zhejiang, 323000, China. 519784291@qq.com.
Miaomiao ChenDepartment of Critical Care Medicine, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, 1188 Liyang Street, Liandu District, Lishui, Zhejiang, 323000, China.
JunLong XuDepartment of Critical Care Medicine, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, 1188 Liyang Street, Liandu District, Lishui, Zhejiang, 323000, China.
Xian ZhangDepartment of Critical Care Medicine, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, 1188 Liyang Street, Liandu District, Lishui, Zhejiang, 323000, China.
Wen HuDepartment of Critical Care Medicine, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, 1188 Liyang Street, Liandu District, Lishui, Zhejiang, 323000, China.

Funding

Research Ethics Committee of Lishui Municipal People's Hospital 20250603
6 · The paper itself

Abstract

objectiveThe aim of this study is to investigate risk factors for acute respiratory distress syndrome (ARDS) in severe acute brain injury (SABI) patients and construct a nomogram-based predictive model.

methodsA retrospective analysis was conducted on 200 SABI patients admitted to Lishui Hospital between January 2021 and April 2025, who were randomly allocated into training group (n = 140) and validation group (n = 60). ARDS risk factors were identified and incorporated into a predictive model. Model performance was evaluated via Receiver operating characteristic curve (ROC curve), calibration plots, and decision curve analysis (DCA).

resultsMultivariate logistic regression revealed three independent predictors of ARDS in SABI patients: Sepsis, PaO₂/FiO₂, Pulmonary infection, (all P < 0.05). The area under the ROC curve (AUC) was 0.778 for the training set and 0.754 for the validation set. Calibration curves demonstrated good predicted-observation agreement, while DCA confirmed the clinical utility of the nomogram.

conclusionThis study developed and validated a nomogram prediction model incorporating three variables: Sepsis, PaO₂/FiO₂ and Pulmonary infection. The model demonstrated good discriminative ability and calibration in predicting the risk of ARDS in patients with SABI, thereby facilitating early risk stratification and supporting clinical decision-making.

Indexed as

Brain InjuriesNomogramsRespiratory Distress SyndromeAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsROC CurveAcute respiratory distress syndromeNomogramPrediction modelSevere acute brain injury

Identifiers

PMID41652587
PMCPMC12980938

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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.