Evidence map›Paper›PMID 41484854›Full record

ArticleBMC pulmonary medicine2026

Construction and validation of a prognostic nomogram for predicting short-term mortality in acute respiratory failure patients: a retrospective cohort study based on the MIMIC-IV database.

Yunlong Wang, Haichao Zhang, Xinyue Ma, Wei Yang

Abstract readValidation Study
In one paragraph

Article in BMC pulmonary 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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5 · Who and what money

Authors and funding

4 authors.

Yunlong WangDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Haichao ZhangDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Xinyue MaDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Wei YangDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China. icuyangwei@sina.com.

Funding

the Key R&D project of Heilongjiang Province 2022ZX01A30
6 · The paper itself

Abstract

objectiveThis study aimed to identify short-term mortality risk factors for patients with acute respiratory failure (ARF) the MIMIC-IV database, construct a prognostic nomogram and evaluate its predictive performance compared to conventional scoring systems.

methodsClinical data from patients diagnosed with ARF were retrospectively collected from the MIMIC-IV database and randomly divided into training and validation groups. The variables were selected via the Lasson regression, and a nomogram was constructed. The nomogram was compared with acute physiology score III (APSIII), simplified acute physiology scores II (SAPS II) and oxford acute severity of illness score (OASIS) model via the C-index, area under the receiver operating characteristic curve (ROC), net reclassification index (NRI), integrated discrimination improvement index (IDI), decision curve analysis (DCA).

resultsA total of 559 patients were included. The study identified nine independent risk factors: age (HR: 1.022, 95% CI: 1.008-1.036, P = 0.002), WBC (HR: 1.060, 95% CI:1.033-1.086, P < 0.001), glucose levels (HR:1.002, 95% CI: 1.001-1.004, P = 0.003), temperature (HR: 0.544, 95% CI: 0.430-0.689, P < 0.001), metastatic solid tumor (HR: 2.138, 95% CI: 1.045-4.372, P = 0.037), malignant cancer (HR: 2.455, 95% CI: 1.456-4.138, P < 0.001), diabetes without chronic complications (HR: 0.288, 95% CI: 0.157-0.807, P < 0.001), cerebrovascular disease (HR: 2.156, 95% CI: 1.180-3.940, P = 0.012), dementia (HR: 2.23, 95% CI: 1.132-4.392, P = 0.020). The nomogram demonstrated strong discriminative performance with C-indices of 0.782 and 0.749 in the training and validation sets, respectively. The AUC for the Training and Validation cohorts were 0.811 (APS III: 0.652; SAPS II: 0.672; OASIS: 0.624) and 0.790 (APS III: 0.634; SAPS II: 0.652; OASIS: 0.609), respectively. The nomogram also significantly outperformed traditional scoring systems, as evidenced by positive NRI and IDI values.

conclusionThe newly developed nomogram exhibits superior predictive capability to traditional scoring systems (APS III, SAPS II and OASIS scores), offering clinicians a practical and reliable tool for accurately assessing short-term mortality risks in ICU patients with ARF.

Indexed as

NomogramsRespiratory InsufficiencyAgedDatabases, FactualFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk AssessmentRisk FactorsROC CurveSeverity of Illness IndexSimplified Acute Physiology ScoreAcute respiratory failurein-ICU mortalityMIMIC-IV databaseModelNomogram

Identifiers

PMID41484854
PMCPMC12866387

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