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A simple clinical risk score (ABCDMP) for predicting mortality in patients with AECOPD and cardiovascular diseases. | LitMetric

AI Article Synopsis

  • A study investigated the high mortality rates among hospitalized patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and cardiovascular diseases (CVDs) to create a new risk score for predicting mortality.
  • Using data from a large cohort, researchers developed the ABCDMP score, which includes key patient factors such as age, blood urea nitrogen levels, and vital signs to assess risk.
  • The ABCDMP score demonstrated strong predictive power, outperforming existing risk scores, and can help guide treatment decisions and future clinical research for these patients.

Article Abstract

Background: The 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 Methods: We 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.

Results: We 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.

Conclusions: The ABCDMP risk score could help predict mortality in AECOPD and CVDs patients and guide further clinical research on risk-based treatment.

Clinical Trial Registration: Chinese Clinical Trail Registry NO.:ChiCTR2100044625; URL: http://www.chictr.org.cn/showproj.aspx?proj=121626 .

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10858518PMC
http://dx.doi.org/10.1186/s12931-024-02704-6DOI Listing

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