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

Jiarui Zhang1, Qun Yi1,2, Chen Zhou3

  • 1Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Guo-xue-xiang 37#, Wuhou District, Chengdu, 610041, Sichuan Province, China.

Respiratory Research
|February 10, 2024
PubMed

Insights

A new ABCDMP risk score accurately predicts mortality in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and cardiovascular diseases (CVDs). This score outperforms existing models and aids in stratifying patients for better management.

Area of Science:

  • Cardiology
  • Pulmonology
  • Clinical Risk Prediction

Background:

  • High morbidity and mortality in hospitalized patients with AECOPD and CVDs.
  • Lack of a specific risk score for predicting mortality in this population.
  • Need for a tool to assess prognosis in patients with AECOPD and CVDs.

Purpose of the Study:

  • To derive and validate a simple clinical risk score for predicting mortality in patients with AECOPD and CVDs.
  • To identify independent prognostic risk factors.
  • To compare the new score with existing risk models.

Main Methods:

  • Prospective, noninterventional, multicenter cohort study.
  • Multivariable logistic regression for risk factor identification.
  • Derivation and validation of the ABCDMP risk score.
  • Comparison with BAP-65, CURB-65, DECAF, and NIVO scores.

Main Results:

  • The ABCDMP score includes age, BUN, consolidation, diastolic blood pressure, mental status, and pulse.
  • Good discrimination (AUC 0.847 in derivation, 0.811 in validation) and calibration.
  • Significantly better predictivity for in-hospital mortality compared to existing scores (P < 0.001).
  • Moderate predictive performance for 3-year mortality.

Conclusions:

  • The ABCDMP risk score effectively predicts mortality in AECOPD and CVDs patients.
  • The score can guide clinical research and risk-based treatment strategies.
  • Facilitates patient stratification for tailored management.
Abstract

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