Key result
A diagnostic model based on seven clinical and demographic factors demonstrated moderate discrimination for cardiometabolic-based chronic disease, with an AUC of 0.6033 in the external validation cohort.
Why the study?
Cardiovascular disease is the leading cause of death globally, and the cardiometabolic-based chronic disease model provides a basis for early therapeutic targeting to mitigate its progression, prompting the need to develop and externally validate a diagnostic model.
Does a diagnostic model based on clinical and social determinants accurately predict cardiometabolic-based chronic disease in adults aged ≥45 years?
Observational (n=9,463)
Does a diagnostic model based on clinical and social determinants accurately predict cardiometabolic-based chronic disease in adults aged ≥45 years?
Effect estimate: AUC 0.6033 (95% CI 0.58066-0.62603)
A diagnostic model based on seven simple clinical and social variables demonstrated modest discrimination for identifying cardiometabolic-based chronic disease in Chinese adults over 45 years old.
Modest CMBCD diagnostic model performance in Chinese cohort data; leaves open clinical utility pending prospective validation in diverse populations.
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 ± 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. CONCLUSIONS: We developed and externally validated a diagnostic model of CMBCD. Discrimination, calibration, and decision curve analysis were satisfactory.
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Yong Li (2023) conducted an observational in Cardiometabolic-based chronic disease (CMBCD) (n=9,463). Diagnostic model for CMBCD (age, general health status, location, smoking, housework ability, pain, exercise tolerance) was evaluated on Discrimination of the diagnostic model for CMBCD in the validation dataset (AUC) (AUC 0.6033, 95% CI 0.58066-0.62603). A diagnostic model based on seven clinical and demographic factors demonstrated moderate discrimination for cardiometabolic-based chronic disease, with an AUC of 0.6033 in the external validation cohort.
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