ArticleRespiratory research2024
A simple clinical risk score (ABCDMP) for predicting mortality in patients with AECOPD and cardiovascular diseases.
Article in Respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Predicting Disease Severity and Mortality Using a Simple Clinical Risk Score (ABCDMP) in Patients with AECOPD in the UAE.Hospital pharmacy · 2026Article
- A machine learning model for predicting short-term in-hospital mortality in acute myocardial infarction with coexisting chronic obstructive pulmonary disease.Frontiers in cardiovascular medicine · 2026Article
- Development and validation of a machine learning-based model to predict the risk of hospitalization death in hospitalized patients with AECOPD.Scientific reports · 2025Article
- Predictive score for in-hospital mortality in patients with severe acute exacerbations of chronic obstructive pulmonary disease.Archives of medical science : AMS · 2025Article
- Risk Factors for Acute Exacerbation in Patients with COPD Group E in Ningbo: Risk Analysis of Irregular Review, Cardiovascular Complications, and Lung Function Decline.International journal of chronic obstructive pulmonary disease · 2025Article
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Authors and funding
20 authors at 11 institutions in 1 country.
Funding
Abstract
backgroundThe morbidity and mortality among hospital inpatients with AECOPD and CVDs remains unacceptably high. Currently, no risk score for predicting mortality has been specifically developed in patients with AECOPD and CVDs. We therefore aimed to derive and validate a simple clinical risk score to assess individuals' risk of poor prognosis. STUDY DESIGN AND
methodsWe evaluated inpatients with AECOPD and CVDs in a prospective, noninterventional, multicenter cohort study. We used multivariable logistic regression analysis to identify the independent prognostic risk factors and created a risk score model according to patients' data from a derivation cohort. Discrimination was evaluated by the area under the receiver-operating characteristic curve (AUC), and calibration was assessed by the Hosmer-Lemeshow goodness-of-fit test. The model was validated and compared with the BAP-65, CURB-65, DECAF and NIVO models in a validation cohort.
resultsWe derived a combined risk score, the ABCDMP score, that included the following variables: age > 75 years, BUN > 7 mmol/L, consolidation, diastolic blood pressure ≤ 60 mmHg, mental status altered, and pulse > 109 beats/min. Discrimination (AUC 0.847, 95% CI, 0.805-0.890) and calibration (Hosmer‒Lemeshow statistic, P = 0.142) were good in the derivation cohort and similar in the validation cohort (AUC 0.811, 95% CI, 0.755-0.868). The ABCDMP score had significantly better predictivity for in-hospital mortality than the BAP-65, CURB-65, DECAF, and NIVO scores (all P < 0.001). Additionally, the new score also had moderate predictive performance for 3-year mortality and can be used to stratify patients into different management groups.
conclusionsThe ABCDMP risk score could help predict mortality in AECOPD and CVDs patients and guide further clinical research on risk-based treatment. CLINICAL
trial registrationChinese Clinical Trail Registry NO.:ChiCTR2100044625; URL: http://www.chictr.org.cn/showproj.aspx?proj=121626 .
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