Observational studyRespiratory research2022
A prediction model for hospital mortality in patients with severe community-acquired pneumonia and chronic obstructive pulmonary disease.
Observational study in Respiratory research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Community-based prediction models of cardiovascular events, acute exacerbations and all-cause mortality in individuals with chronic obstructive pulmonary disease: a systematic review and meta-analysis on behalf of the International Cardiovascular and Respiratory Alliance.BMJ open respiratory research · 2026Pooled it
- Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Interpretable machine learning for predicting in-hospital mortality in COPD ICU patients: a rigorous validation across time and geography.Respiratory research · 2026Article
- Prognostic Value of the National Early Warning Score Combined with Nutritional and Endothelial Stress Indices for Mortality Prediction in Critically Ill Patients with Pneumonia.Medicina (Kaunas, Lithuania) · 2026Article
- Development and validation of a nomogram integrating inflammatory-nutritional-metabolic composite indices for predicting short-term adverse outcomes in hospitalized patients with pneumonia.Frontiers in medicine · 2026Article
- The Impact of Human Immunodeficiency Virus Co‑Infection on the Pathogen Spectrum and Outcomes of Severe Community‑Acquired Pneumonia: Insights from Metagenomic Next‑Generation Sequencing.Infection and drug resistance · 2026Article
- Article
- 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 scoring system to predict the mortality of hospitalized patients with SARS-CoV-2 Omicron: a nationwide, multicentre study.BMC pulmonary medicine · 2024Article
- Development and Validation of a Risk Mortality Prediction Model for Patients with Pulmonary Tuberculosis Complicated by Severe Community-Acquired Pneumonia in the Intensive Care Unit.Infection and drug resistance · 2024Article
- Comprehensive Nomograms Using Routine Biomarkers Beyond Eosinophil Levels: Enhancing Predictability of Corticosteroid Treatment Outcomes in AECOPD.Journal of inflammation research · 2024Article
- Predictive nomogram for in-hospital mortality among older patients with intra-abdominal sepsis incorporating skeletal muscle mass.Aging clinical and experimental research · 2023Article
- Machine learning-based prediction of in-ICU mortality in pneumonia patients.Scientific reports · 2023Article
- Development and validation of a predictive model for 30-day mortality in patients with severe community-acquired pneumonia in intensive care units.Frontiers in medicine · 2023Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundNo personalized prediction model or standardized algorithm exists to identify those at high risk of death among severe community-acquired pneumonia (SCAP) patients with chronic obstructive pulmonary disease (COPD). The aim of this study was to investigate the risk factors and to develop a useful nomogram for prediction of mortality in those patients.
methodsWe performed a retrospective, observational, cohort study in the intensive care unit (ICU) of West China Hospital, Sichuan University with all consecutive SCAP patients with COPD between December 2011 and December 2018. The clinical data within 24 h of admission to ICU were collected. The primary outcome was hospital mortality. We divided the patients into training and testing cohorts (70% versus 30%) randomly. In the training cohort, univariate and multivariate logistic regression analysis were used to identify independent risk factors applied to develop a nomogram. The prediction model was assessed in both training and testing cohorts.
resultsFinally, 873 SCAP patients with COPD were included, among which the hospital mortality was 41.4%. In training cohort, the independent risk factors for hospital mortality were increased age, diabetes, chronic renal diseases, decreased systolic blood pressure (SBP), and elevated fibrinogen, interleukin 6 (IL-6) and blood urea nitrogen (BUN). The C index was 0.840 (95% CI 0.809-0.872) in training cohort and 0.830 (95% CI 0.781-0.878) in testing cohort. Furthermore, the time-dependent AUC, calibration plots, DCA and clinical impact curves indicated the model had good predictive performance. Significant association of risk stratification based on nomogram with mortality was also found (P for trend < 0.001). The restricted cubic splines suggested that estimated associations between these predictors and hospital mortality were all linear relationships.
conclusionWe developed a prediction model including seven risk factors for hospital mortality in patients with SCAP and COPD. It can be used for early risk stratification in clinical practice after more external validation.
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