Evidence map›Paper›PMID 42182697›Full record

ArticleJournal of thoracic disease2026

Nomogram for predicting prognostic risk in severe pulmonary tuberculosis: a retrospective analysis from the MIMIC-IV database.

Daichen Ju, Wendi Zhou, Jiamin Lin, Liang Yan, Ning Su, Jialou Zhu, Dexian Li, Chaoxian Yu, Jinxing Hu

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Article in Journal of thoracic disease, 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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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

9 authors.

Daichen JuState Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Tuberculosis, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.
Wendi ZhouDepartment of Rehabilitation Medicine, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China.
Jiamin LinState Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Tuberculosis, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.
Liang YanState Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Hospital-Acquired Infection Control, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.
Ning SuState Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Oncology, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.
Jialou ZhuState Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Clinical Laboratory, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.
Dexian LiState Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Intensive Care Medicine, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.
Chaoxian YuState Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Intensive Care Medicine, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.
Jinxing HuState Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Tuberculosis, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The treatment of severe pulmonary tuberculosis (PTB) remains challenging, highlighting the need for prognostic tools. This study aimed to establish and validate a nomogram for predicting overall survival (OS) of PTB patients in the intensive care unit (ICU). Methods: A retrospective analysis was performed using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. A total of 1,105 PTB patients were identified and randomly divided into training and validation cohorts. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection, followed by Cox regression to construct a predictive model. A nomogram was developed based on the selected predictors. Model performance was assessed by receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results: Eight predictors were identified: age, Acute Physiology Score (APS) III, partial pressure of oxygen (PO Conclusions: We developed and validated a prognostic nomogram integrating eight clinical variables to predict survival in ICU patients with PTB. This tool may assist clinicians in early risk stratification and personalized management.

Indexed as

Medical Information Mart for Intensive Care IV (MIMIC-IV)nomogrampredictionprognosisPulmonary tuberculosis (PTB)

Identifiers

PMID42182697
PMCPMC13190061

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