Optimize speckle-tracking echocardiography screening in CKD patients: a TyG index-based nomogram model

Area of Science:

• Cardiology
• Nephrology
• Biomedical Engineering

Background:

• Patients with chronic kidney disease (CKD) and declining left ventricular (LV) systolic function have worse outcomes.
• Speckle-tracking echocardiography (STE) detects early LV systolic dysfunction, but routine screening in CKD patients may overuse resources.
• Identifying high-risk CKD patients for abnormal global longitudinal strain (GLS) can optimize STE use.

Purpose of the Study:

• To develop a predictive model for abnormal GLS in CKD patients using the triglyceride-glucose (TyG) index.
• To provide a tool for optimizing selective STE screening in CKD populations.
• To assess the clinical utility of the TyG index in predicting cardiac dysfunction in CKD.

Main Methods:

• A prospective cross-sectional study included 212 CKD patients.
• Data collected included clinical examination, biochemistry, and transthoracic echocardiography.
• Multivariable analysis and nomogram modeling were used to identify predictors of reduced GLS (< 20%).

Main Results:

• Male sex, diastolic blood pressure, high-density lipoprotein, and TyG index were independent predictors of reduced GLS.
• The developed nomogram model showed strong predictive performance (AUC 0.838-0.842).
• The model demonstrated clinical utility in both training and validation cohorts.

Conclusions:

• A TyG index-based nomogram model can effectively predict abnormal GLS in CKD patients.
• This model can serve as a valuable screening tool to optimize STE utilization.
• Optimized STE screening can help conserve medical resources in CKD patient care.