ArticleBMC cardiovascular disorders2023
Development and external validation of a diagnostic model for cardiometabolic-based chronic disease : results from the China health and retirement longitudinal study (CHARLS).
Article in BMC cardiovascular disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCardiovascular disease(CVD) is the leading cause of death in the world. Cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for sustainable and early, evidence-based therapeutic targeting to mitigate the ravagest and development of CVD. CMBCD include dysglycemia, hypertension, and/or dyslipidemia progressing to downstream CVD events.
objectivesThe objective of our research was to develop and externally validate a diagnostic model of CMBCD.
methodsDesign: Multivariable logistic regression of a cohort for 9,463 participants aged at least 45 years were drawn from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS).
settingThe 2018 wave of the CHARLS.
participantsDiagnostic model development: Totally 6,218 participants whose individual ID < 250,000,000,000. External validation: Totally 3,245 participants whose individual ID > 250,000,000,000. OUTCOMES: CMBCD .
resultsCMBCD occurred in 25.5%(1,584/6,218)of individuals in the development data set and 26.2%(850 /3,245)of individuals in the validation data set. The strongest predictors of CMBCD were age, general health status, location of residential address, smoking, housework ability, pain, and exercise tolerance. We developed a diagnostic model of CMBCD. Discrimination was the ability of the diagnostic model to differentiate between people who with and without CMBCD. This measure was quantified by calculating the area under the receiver operating characteristic(ROC) curve(AUC).The AUC was 0.6199 ± 0.0083, 95% confidence interval(CI) = 0.60372 ~ 0.63612. We constructed a nomograms using the development database based on age, general health status, location of residential address, smoking, housework ability, pain, and exercise tolerance. The AUC was 0.6033 ± 0.0116, 95% CI = 0.58066 ~ 0.62603 in the validation data set.
conclusionsWe developed and externally validated a diagnostic model of CMBCD. Discrimination, calibration, and decision curve analysis were satisfactory.
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