ArticleBMC pulmonary medicine2023
Development and validation of an in-hospital mortality risk prediction model for patients with severe community-acquired pneumonia in the intensive care unit.
Article in BMC pulmonary medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Inhaled antibiotics for treating pneumonia in invasively ventilated patients in intensive care unit: a meta-analysis of randomized clinical trials with trial sequential analysis.Critical care (London, England) · 2024Pooled it
- Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Construction of a risk prediction model for severe community-acquired pneumonia in children in Qinghai region.BMJ paediatrics open · 2026Review
- Comparisons of mortality predictions of mROX, CURB-65, and APACHE II in patients with severe pneumonia: A retrospective cohort study.Medicine · 2025Article
- Machine learning-based model for predicting all-cause mortality in severe pneumonia.BMJ open respiratory research · 2025Article
- An investigation of the relationship between sPD-1, sPD-L1 and severe pneumonia patients admitted to ICU and its clinical significance.Frontiers in medicine · 2025Article
- Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning.Frontiers in pharmacology · 2025Article
- New models to predict survival of patients with difficult ventilator-weaning diagnosed with community-acquired pneumonia.Frontiers in medicine · 2025Article
- Steroids in severe community-acquired pneumonia.Breathe (Sheffield, England) · 2024Article
- Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients: a retrospective cohort study.BMC pulmonary medicine · 2024Article
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5 authors.
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Abstract
backgroundA high mortality rate has always been observed in patients with severe community-acquired pneumonia (SCAP) admitted to the intensive care unit (ICU); however, there are few reported predictive models regarding the prognosis of this group of patients. This study aimed to screen for risk factors and assign a useful nomogram to predict mortality in these patients.
methodsAs a developmental cohort, we used 455 patients with SCAP admitted to ICU. Logistic regression analyses were used to identify independent risk factors for death. A mortality prediction model was built based on statistically significant risk factors. Furthermore, the model was visualized using a nomogram. As a validation cohort, we used 88 patients with SCAP admitted to ICU of another hospital. The performance of the nomogram was evaluated by analysis of the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, and decision curve analysis (DCA).
resultsLymphocytes, PaO2/FiO2, shock, and APACHE II score were independent risk factors for in-hospital mortality in the development cohort. External validation results showed a C-index of 0.903 (95% CI 0.838-0.968). The AUC of model for the development cohort was 0.85, which was better than APACHE II score 0.795 and SOFA score 0.69. The AUC for the validation cohort was 0.893, which was better than APACHE II score 0.746 and SOFA score 0.742. Calibration curves for both cohorts showed agreement between predicted and actual probabilities. The results of the DCA curves for both cohorts indicated that the model had a high clinical application in comparison to APACHE II and SOFA scoring systems.
conclusionsWe developed a predictive model based on lymphocytes, PaO2/FiO2, shock, and APACHE II scores to predict in-hospital mortality in patients with SCAP admitted to the ICU. The model has the potential to help physicians assess the prognosis of this group of patients.
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