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Original Research

Open Access

Nomogram for predicting in-hospital mortality of patients with respiratory failure caused by severe community-acquired pneumonia

  • Qing Hu1,2
  • Pan Pan3
  • Bing Xiang1,2,*,

1Chengdu Shangjin Nanfu Hospital of West China Hospital, Sichuan University, 610000 Chengdu, Sichuan, China

2Department of Hematology, West China Hospital, Sichuan University, 610000 Chengdu, Sichuan, China

3Emergency Department, West China Hospital, Sichuan University, 610000 Chengdu, Sichuan, China

DOI: 10.22514/sv.2025.063 Vol.21,Issue 5,May 2025 pp.29-38

Submitted: 27 February 2024 Accepted: 28 June 2024

Published: 08 May 2025

*Corresponding Author(s): Bing Xiang E-mail: study@hhu.edu.cn

Abstract

Background: To identify clinical factors associated with in-hospital mortality in patients suffering from respiratory failure due to severe community-acquired pneumonia and develop a predictive nomogram for clinical outcomes. Methods: A retrospective analysis was conducted on the clinical data of individuals who experienced respiratory failure due to severe community-acquired pneumonia. Univariate analysis investigated the correlation between clinical variables. Multivariate stepwise logistic regression analysis identified independent risk factors for mortality. Based on these factors, a nomogram was established to predict in-hospital mortality. Results: Out of the total 527 patients, 225 (42.6%) survived while 302 (57.4%) eventually passed away. There was a positive correlation between age, sepsis, heart rate, and blood lactate levels and in-hospital mortality. On the other hand, there was a negative correlation between systolic and diastolic blood pressure, hemoglobin oxygen saturation, platelets, blood sodium, C reactive protein (CRP), and bicarbonate ion levels. The multivariate analysis revealed that age, heart rate, systolic blood pressure, platelets, blood sodium, CRP, blood lactate, and bicarbonate ion were independent risk factors. The developed nomogram, incorporating eight factors, demonstrated high predictive accuracy, as indicated by the area under the receiver operating characteristic curve (ROC) of 0.813. Both calibration plots and decision curve analysis supported the nomogram’s predictive accuracy and clinical utilization. Conclusions: The study successfully created a nomogram that includes eight independent risk factors for predicting in-hospital mortality in patients with respiratory failure caused by severe community-acquired pneumonia. This tool can assist clinicians in evaluating patient prognosis and making well-informed decisions about patient care.


Keywords

Community-acquired pneumonia; Nomogram; Severe pneumonia; Respiratory failure; Intensive care unit


Cite and Share

Qing Hu, Pan Pan, Bing Xiang. Nomogram for predicting in-hospital mortality of patients with respiratory failure caused by severe community-acquired pneumonia. Signa Vitae. 2025. 21(5);29-38.

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