---
title: "Machine learning model predicts early invasive mechanical ventilation in ICU pneumonia patients"
id: "pubmed-42275910"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42275910"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42275910/"
doi: "10.1016/j.ijmedinf.2026.106541"
published_at: "2026-09-15T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Machine learning model predicts early invasive mechanical ventilation in ICU pneumonia patients
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42275910
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42275910/)
- **DOI:** [10.1016/j.ijmedinf.2026.106541](https://doi.org/10.1016%2Fj.ijmedinf.2026.106541)
- **Published At:** 2026-09-15T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Pneumonia in the ICU can rapidly progress to respiratory failure requiring **invasive mechanical ventilation (IMV)**; current decisions use fragmented clinical data and would benefit from integrated risk tools. - The authors performed a retrospective cohort study using the MIMIC‑IV database to develop prediction models for IMV within 24 hours of ICU admission. - The development cohort included 5,608 patients; 856 (15.3%) required IMV within 24 hours. - The cohort was randomly split into training (80%) and test (20%) sets. Candidate predictors at ICU admission underwent multi‑step feature selection. - Eight machine learning algorithms were trained with 5‑fold cross‑validation and hyperparameter tuning; model performance was assessed for discrimination, calibration, and clinical utility. - The final model used seven predictors available at admission: **age**, oxygen flow, FiO2, pH, PaO2, PaCO2, and platelet count. - LightGBM had the best internal discrimination (AUC = 0.799) and was selected for external validation. - External validation in an independent cohort from Maoming People's Hospital (n = 155) produced an AUC of 0.702. - Calibration showed acceptable agreement between predicted and observed risk; decision curve analysis demonstrated net clinical benefit across threshold probabilities reported. - Risk stratification identified clinically distinct groups with progressively increasing IMV incidence. - SHAP analysis was applied to improve model interpretability and to identify key predictors contributing to IMV risk. - The authors implemented a web‑based calculator to facilitate individualized risk assessment. - The paper concludes the model offers an interpretable, data‑driven adjunct for early risk stratification; multicenter prospective studies were recommended to confirm clinical utility.
## Clinical Analysis & Structured Key Points
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Epub 2026 Jun 10. # Machine learning-based prediction of early invasive mechanical ventilation in ICU patients with pneumonia: Development and external validation [Yanping Lu](https://pubmed.ncbi.nlm.nih.gov/?term=Lu+Y&cauthor_id=42275910)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#full-view-affiliation-1 "The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China."), [Haojie Chen](https://pubmed.ncbi.nlm.nih.gov/?term=Chen+H&cauthor_id=42275910)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#full-view-affiliation-2 "The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China."), [Minghan Li](https://pubmed.ncbi.nlm.nih.gov/?term=Li+M&cauthor_id=42275910)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#full-view-affiliation-2 "The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China."), [Huazhen Liang](https://pubmed.ncbi.nlm.nih.gov/?term=Liang+H&cauthor_id=42275910)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#full-view-affiliation-3 "Department of Oncology, Maoming People's Hospital, Maoming, Guangdong 525000, China."), [Hualiang Lv](https://pubmed.ncbi.nlm.nih.gov/?term=Lv+H&cauthor_id=42275910)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#full-view-affiliation-4 "The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China; The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China. Electronic address: liang169168@163.com.") Affiliations Expand ### Affiliations * 1 The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China. * 2 The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China. * 3 Department of Oncology, Maoming People's Hospital, Maoming, Guangdong 525000, China. * 4 The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China; The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China. Electronic address: liang169168@163.com. * PMID: **42275910** * DOI: [ 10.1016/j.ijmedinf.2026.106541 ](https://doi.org/10.1016/j.ijmedinf.2026.106541) Item in Clipboard # Machine learning-based prediction of early invasive mechanical ventilation in ICU patients with pneumonia: Development and external validation Yanping Lu et al. Int J Med Inform. 2026. Show details Display options Display options Format Abstract PubMed PMID Int J Med Inform Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Int+J+Med+Inform%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Int+J+Med+Inform%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42275910/) . 2026 Sep 15:218:106541. doi: 10.1016/j.ijmedinf.2026.106541. Epub 2026 Jun 10. ### Authors [Yanping Lu](https://pubmed.ncbi.nlm.nih.gov/?term=Lu+Y&cauthor_id=42275910)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#short-view-affiliation-1 "The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China."), [Haojie Chen](https://pubmed.ncbi.nlm.nih.gov/?term=Chen+H&cauthor_id=42275910)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#short-view-affiliation-2 "The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China."), [Minghan Li](https://pubmed.ncbi.nlm.nih.gov/?term=Li+M&cauthor_id=42275910)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#short-view-affiliation-2 "The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China."), [Huazhen Liang](https://pubmed.ncbi.nlm.nih.gov/?term=Liang+H&cauthor_id=42275910)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#short-view-affiliation-3 "Department of Oncology, Maoming People's Hospital, Maoming, Guangdong 525000, China."), [Hualiang Lv](https://pubmed.ncbi.nlm.nih.gov/?term=Lv+H&cauthor_id=42275910)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42275910/#short-view-affiliation-4 "The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China; The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China. Electronic address: liang169168@163.com.") ### Affiliations * 1 The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China. * 2 The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China. * 3 Department of Oncology, Maoming People's Hospital, Maoming, Guangdong 525000, China. * 4 The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China; The First Clinical College of Medicine, Guangdong Medical University, Zhanjiang, Guangdong 524023, China. Electronic address: liang169168@163.com. * PMID: **42275910** * DOI: [ 10.1016/j.ijmedinf.2026.106541 ](https://doi.org/10.1016/j.ijmedinf.2026.106541) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Background:** Pneumonia is a common critical illness in the intensive care unit (ICU), and a subset of patients rapidly progresses to respiratory failure requiring invasive mechanical ventilation (IMV). Current decisions often rely on fragmented clinical indicators, highlighting the need for integrated, data-driven tools for early risk assessment. **Methods:** We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database as the development cohort. The cohort was randomly split into training (80%) and test (20%) sets. Candidate predictors at ICU admission were selected through multi-step feature selection. Eight machine learning models were trained with 5-fold cross-validation and hyperparameter tuning. Model performance was evaluated in terms of discrimination, calibration, and clinical utility. The best-performing model was externally validated in an independent cohort from Maoming People's Hospital. A web-based calculator was further developed to facilitate potential clinical application and individualized risk assessment. **Results:** The development cohort included 5,608 patients, of whom 856 (15.3%) required IMV within 24 h. The final model retained seven predictors: age, oxygen flow, FiO2, pH, PaO2, PaCO2, and platelet count. LightGBM showed the best performance in the internal test set (AUC = 0.799) and achieved an AUC of 0.702 in the external validation cohort (n = 155). Calibration showed acceptable agreement, and decision curve analysis demonstrated net clinical benefit. Risk stratification further enabled identification of clinically distinct patient groups with progressively increasing IMV incidence. SHAP analysis further enhanced model interpretability by identifying key predictors associated with IMV risk. **Conclusions:** This study developed and externally validated a machine learning model and online calculator for predicting early IMV in ICU pneumonia patients. The model provides an interpretable, data-driven approach for early risk stratification and may serve as an adjunctive tool to assist clinical decision-making. Multicenter prospective studies are warranted to confirm clinical utility. **Keywords:** Intensive care unit; Invasive mechanical ventilation; Machine learning; Pneumonia; Prediction model. Copyright © 2026 Elsevier B.V. All rights reserved. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ## Similar articles * [ Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study. ](https://pubmed.ncbi.nlm.nih.gov/41617215/) Zhang Y, Ren X, Liu L, Zha J, Gu Y, Ye H.Zhang Y, et al.JMIR Med Inform. 2026 Jan 30;14:e80969. doi: 10.2196/80969.JMIR Med Inform. 2026.PMID: 41617215Free PMC article. * [ A clinically interpretable prediction model for acute mortality in patients with pneumonia requiring mechanical ventilation. ](https://pubmed.ncbi.nlm.nih.gov/41731516/) Hu H, Zu Y, Zhao L, Zhang Q, Zhou G, Xu P, Zhao A, Yin F, Sharma L, Chang D.Hu H, et al.Respir Res. 2026 Feb 23;27(1):126. doi: 10.1186/s12931-026-03581-x.Respir Res. 2026.PMID: 41731516Free PMC article. * [ Machine Learning Models for Mortality Prediction in Intensive Care Unit Patients With Ischemic Stroke Associated With Intracranial Artery Stenosis: Retrospective Cohort Study. ](https://pubmed.ncbi.nlm.nih.gov/41734354/) Zhang K, Chen R, Yang J, Yan Y, Liu L, Meng C, Li P, Xing G, Liu X.Zhang K, et al.JMIR Cardio. 2026 Feb 24;10:e82042. doi: 10.2196/82042.JMIR Cardio. 2026.PMID: 41734354Free PMC article. * [ Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study. ](https://pubmed.ncbi.nlm.nih.gov/41027023/) Ge X, Chen W, Shi J, Zhang J, Tai H, Zhang Y, Wang B, Liu W, Chen S, Han H.Ge X, et al.J Med Internet Res. 2025 Sep 30;27:e73840. doi: 10.2196/73840.J Med Internet Res. 2025.PMID: 41027023Free PMC article. * [ Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database. ](https://pubmed.ncbi.nlm.nih.gov/41569981/) Wang J, Lin L, Qiu LP, Zheng LL, Wu LX, Lv H, Xie H.Wang J, et al.PLoS One. 2026 Jan 22;21(1):e0341259. doi: 10.1371/journal.pone.0341259. eCollection 2026.PLoS One. 2026.PMID: 41569981Free PMC article. 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