SynthesisThe clinical respiratory journal2023
A pooled analysis of the risk prediction models for mortality in acute exacerbation of chronic obstructive pulmonary disease.
Synthesis in The clinical respiratory journal, 2023. 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, 12 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
- Symptom trajectory types during recovery from acute exacerbation of COPD and their association with systemic inflammatory burden and prognosis.Frontiers in cell and developmental biology · 2026Article
- Development and Internal Validation of a Multivariable Prognostic Model for in-Hospital Mortality in Critically Ill Patients with Acute Exacerbation of COPD.International journal of chronic obstructive pulmonary disease · 2026Observational
- Explainable machine learning model for 1-year readmission risk prediction in AECOPD patients: integrating relief feature selection with sample augmentation.BMC infectious diseases · 2025Article
- Article
- Hemoglobin-Albumin-Lymphocyte-Platelet Index and Risk of In-Hospital Mortality in 793 Adult Patients Hospitalized for Acute Exacerbations of Chronic Obstructive Pulmonary Disease.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Article
- Predictive score for in-hospital mortality in patients with severe acute exacerbations of chronic obstructive pulmonary disease.Archives of medical science : AMS · 2025Article
- Development and validation of the machine learning model for acute exacerbation of chronic obstructive pulmonary disease prediction based on inflammatory biomarkers.Frontiers in medicine · 2025Article
- Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions --A Bibliometric Analysis from 2009 to 2023.International journal of chronic obstructive pulmonary disease · 2024Article
- Review
- Article
- 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
5 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThe prognosis for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is not optimistic, and severe AECOPD leads to an increased risk of mortality. Prediction models help distinguish between high- and low-risk groups. At present, many prediction models have been established and validated, which need to be systematically reviewed to screen out more suitable models that can be used in the clinic and provide evidence for future research.
methodsWe searched PubMed, EMBASE, Cochrane Library and Web of Science databases for studies on risk models for AECOPD mortality from their inception to 10 April 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Stata software (version 16) was used to synthesize the C-statistics for each model.
resultsA total of 37 studies were included. The development of risk prediction models for mortality in patients with AECOPD was described in 26 articles, in which the most common predictors were age (n = 17), dyspnea grade (n = 11), altered mental status (n = 8), pneumonia (n = 6) and blood urea nitrogen (BUN, n = 6). The remaining 11 articles only externally validated existing models. All 37 studies were evaluated at a high risk of bias using PROBAST. We performed a meta-analysis of five models included in 15 studies. DECAF (dyspnoea, eosinopenia, consolidation, acidemia and atrial fibrillation) performed well in predicting in-hospital death [C-statistic = 0.91, 95% confidence interval (CI): 0.83, 0.98] and 90-day death [C-statistic = 0.76, 95% CI: 0.69, 0.82] and CURB-65 (confusion, urea, respiratory rate, blood pressure and age) performed well in predicting 30-day death [C-statistic = 0.74, 95% CI: 0.70, 0.77].
conclusionsThis study provides information on the characteristics, performance and risk of bias of a risk model for AECOPD mortality. This pooled analysis of the present study suggests that the DECAF performs well in predicting in-hospital and 90-day deaths. Yet, external validation in different populations is still needed to prove this performance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.