Development and external validation of a diagnostic model for cardiometabolic-based chronic disease : results from the China health and retirement longitudinal study (CHARLS)

• Published in: BMC cardiovascular disorders Vol. 23; no. 1; pp. 1 - 8
• Main Author: Li, Yong
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 23.08.2023; BioMed Central; BMC
• Subjects: Age; Alcohol; Analysis; Cardiometabolic diseases; Cardiovascular disease; Cardiovascular diseases; Care and treatment; Chronic illnesses; Diabetes; Diagnosis; Disease prevention; Dyslipidemia; Heart; Housework; Hypertension; Insulin resistance; Longitudinal studies; Mail fraud; Marital status; Metabolic diseases; Metabolic disorders; Nomograms; Nomography (Mathematics); Pain; Prevention; Regression analysis; Risk factors; Smoking; Therapeutic targets; Variables; China
• ISSN: 1471-2261; 1471-2261
• DOI: 10.1186/s12872-023-03418-1

Background Cardiovascular 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. Objectives The objective of our research was to develop and externally validate a diagnostic model of CMBCD. Methods Design: 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). Setting: The 2018 wave of the CHARLS. Participants:Diagnostic 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 . Results CMBCD 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 [+ or -] 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 [+ or -] 0.0116, 95% CI = 0.58066 ~ 0.62603 in the validation data set. Conclusions We developed and externally validated a diagnostic model of CMBCD. Discrimination, calibration, and decision curve analysis were satisfactory. Keywords: Cardiometabolic diseases, Hypertension, Insulin resistance, Risk factors, Nomograms