Predicting in-hospital mortality in critically ill pulmonary embolism patients: development and validation of a machine learning-based model

LIN Li; YUAN Chao; RAO Xianqun; LIU Chang; SUN Kai

doi:10.16766/j.cnki.issn.1674-4152.004523

Volume 24 Issue 6

Jun. 2026

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Article Navigation > Chinese Journal of General Practice > 2026 > 24(6): 926-931

LIN Li, YUAN Chao, RAO Xianqun, LIU Chang, SUN Kai. Predicting in-hospital mortality in critically ill pulmonary embolism patients: development and validation of a machine learning-based model[J]. Chinese Journal of General Practice, 2026, 24(6): 926-931. doi: 10.16766/j.cnki.issn.1674-4152.004523

Citation:

LIN Li, YUAN Chao, RAO Xianqun, LIU Chang, SUN Kai. Predicting in-hospital mortality in critically ill pulmonary embolism patients: development and validation of a machine learning-based model[J]. Chinese Journal of General Practice, 2026, 24(6): 926-931. doi: 10.16766/j.cnki.issn.1674-4152.004523

Citation:

LIN Li, YUAN Chao, RAO Xianqun, LIU Chang, SUN Kai. Predicting in-hospital mortality in critically ill pulmonary embolism patients: development and validation of a machine learning-based model[J]. Chinese Journal of General Practice, 2026, 24(6): 926-931. doi: 10.16766/j.cnki.issn.1674-4152.004523

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Predicting in-hospital mortality in critically ill pulmonary embolism patients: development and validation of a machine learning-based model

doi: 10.16766/j.cnki.issn.1674-4152.004523

LIN Li^{1, 2}
, YUAN Chao¹
, RAO Xianqun³
, LIU Chang⁴
, SUN Kai^{5
,
,}

1.
Department of Emergency, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China

Funds:

81871544

Received Date: 2026-02-11
Available Online: 2026-11-10

Abstract

Abstract

Objective This study aimed to develop and validate a multidimensional risk prediction model for in-hospital mortality in critically ill patients with acute pulmonary embolism (PE) using machine learning-based nomogram techniques, and to identify key risk factors associated with adverse outcomes for improved risk stratification. Methods A retrospective analysis was conducted on 505 ICU patients with acute PE from the MIMIC-Ⅳ database. Patients were randomly divided into a training set (n=354) and a testing set (n=151) at a ratio of 7∶3. LASSO regression was employed to screen predictive variables, followed by logistic regression (LR) to construct the nomogram model. Model performance was validated using ROC curves, calibration curves, and decision curve analysis (DCA). Results LASSO regression identified vasopressor use, anion gap (AG), Charlson comorbidity index (CCI), simplified acute physiology score Ⅱ (SAPS Ⅱ), and simplified pulmonary embolism severity index (sPESI) as critical predictors. The nomogram demonstrated good discriminative performance, with AUCs of 0.813 (95% CI: 0.759-0.863) in the training set and 0.762 (95% CI: 0.683-0.838) in the testing set. Calibration curves and Hosmer-Lemeshow tests (P>0.05) indicated good agreement between predicted and observed outcomes, while DCA indicated favorable clinical utility across a wide range of threshold probabilities. Conclusion The nomogram integrating vasopressor use, AG, CCI, SAPS Ⅱ, and sPESI effectively predicts the risk of in-hospital mortality in critically ill patients with PE, offering a quantitative tool for clinical decision-making and risk stratification.
- Pulmonary embolism,
- Nomograms,
- Predictive model,
- Hospital mortality,
- Intensive care unit

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References

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Predicting in-hospital mortality in critically ill pulmonary embolism patients: development and validation of a machine learning-based model

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