ArticleERJ open research2022
Predicting in-hospital death in pneumonic COPD exacerbation
Article in ERJ open research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Prognostic risk prediction model for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD): a systematic review and meta-analysis.Respiratory research · 2024Pooled it
- A pooled analysis of the risk prediction models for mortality in acute exacerbation of chronic obstructive pulmonary disease.The clinical respiratory journal · 2023Pooled it
- Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning.Frontiers in immunology · 2023Trial
- Prognostic Value of Chest CT-Derived Pectoralis Muscle Metrics for In-Hospital Mortality and Invasive Mechanical Ventilation in AECOPD.International journal of chronic obstructive pulmonary disease · 2026Article
- Review
- Prehospital Performance of Five Early Warning Scores to Predict Long-Term Mortality in Patients with Suspected Respiratory Infections.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning-based prediction of in-hospital mortality in patients with chronic respiratory disease exacerbations.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThere is no established clinical prediction model for in-hospital death among patients with pneumonic COPD exacerbation. We aimed to externally validate BAP-65 and CURB-65 and to develop a new model based on the eXtreme Gradient Boosting (XGBoost) algorithm.
methodsThis multicentre cohort study included patients aged ≥40 years with pneumonic COPD exacerbation. The input data were age, sex, activities of daily living, mental status, systolic and diastolic blood pressure, respiratory rate, heart rate, peripheral blood eosinophil count and blood urea nitrogen. The primary outcome was in-hospital death. BAP-65 and CURB-65 underwent external validation using the area under the receiver operating characteristic curve (AUROC) in the whole dataset. We used XGBoost to develop a new prediction model. We compared the AUROCs of XGBoost with that of BAP-65 and CURB-65 in the test dataset using bootstrap sampling.
resultsWe included 1190 patients with pneumonic COPD exacerbation. The in-hospital mortality was 7% (88 out of 1190). In the external validation of BAP-65 and CURB-65, the AUROCs (95% confidence interval) of BAP-65 and CURB-65 were 0.69 (0.66-0.72) and 0.69 (0.66-0.72), respectively. XGBoost showed an AUROC of 0.71 (0.62-0.81) in the test dataset. There was no significant difference in the AUROCs of XGBoost
conclusionBAP-65, CURB-65 and XGBoost showed low predictive performance for in-hospital death in pneumonic COPD exacerbation. Further large-scale studies including more variables are warranted.
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Registered trials
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