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Establishment and effectiveness evaluation of pre-test probability model of coronary heart disease combined with cardiopulmonary exercise test indexes. | LitMetric

AI Article Synopsis

  • - A study aimed to create a predictive model for coronary heart disease (CHD) using cardiopulmonary exercise test (CPET) data, comparing its effectiveness to existing models like the Duke Clinical Score (DCS) and the updated Diamond-Forrester Model (UDFM).
  • - Researchers analyzed data from 342 patients, classifying them into CHD and non-CHD groups based on coronary artery blockage and established a multivariate logistic regression model that included several health indicators as risk factors.
  • - The new predictive model showed good accuracy, slightly outperforming DCS and UDFM, but further validation with larger sample sizes is necessary for confirmation.

Article Abstract

To establish a pre-test probability model of coronary heart disease (CHD) combined with cardiopulmonary exercise test (CPET) indexes and to compare the clinical effectiveness with Duke clinical score (DCS) and updated Diamond-Forrester model (UDFM), thus further explore the predictive value. 342 cases were used to establish the prediction model equation and another 80 cases were used to verify the effectiveness. The patients were divided into CHD group (n = 157) and non-CHD group (n = 185) according to coronary artery stenosis degree >50% or not. Combining DCS and UDFM as reference models with CPET indexes, a multivariate logistic regression model was established. The area under the ROC curve of the three models were calculated to compare the predictive effectiveness. There were significant differences in gender, chest pain type, myocardial infarction history, hypertension history, smoking, pathological Q wave and ST-T change between two groups (P < 0.01), as well as age, LVEF, heart rate at anaerobic domain, peak oxygen uptake in kilograms of body weight, percentage of peak oxygen uptake to the predicted value, the oxygen uptake efficiency slope and carbon dioxide ventilation equivalent slope (P < 0.05). Multivariate analysis showed gender, age, chest pain type, myocardial infarction history, hypertension history, smoking, pathological Q wave, ST-T change, and peak oxygen pulse were independent risk factors of CHD. The pre-test probability model of CHD combined with CPET indexes has good distinguish and calibrate ability, its prediction accuracy is slightly better than DCS and UDFM, which still needs to be verified externally in more samples.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10541865PMC
http://dx.doi.org/10.1038/s41598-023-41884-xDOI Listing

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