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

Area of Science:

• Cardiology
• Nephrology
• Biomedical Engineering

Background:

• Chronic kidney disease (CKD) is linked to poorer clinical outcomes due to worsening left ventricular (LV) systolic function.
• Speckle-tracking echocardiography (STE)-derived global longitudinal strain (GLS) detects early LV systolic dysfunction but routine screening is resource-intensive.
• Identifying high-risk CKD patients for abnormal GLS is crucial for optimizing STE use.

Purpose of the Study:

• To develop and validate a predictive model for abnormal GLS in CKD patients using the triglyceride-glucose (TyG) index.
• To provide a tool for selective STE screening in CKD populations.
• To improve healthcare resource utilization in managing CKD patients with potential cardiac dysfunction.

Main Methods:

• Prospective cross-sectional study of 212 CKD patients.
• Clinical examination, biochemistry measurements, and transthoracic echocardiography were performed.
• Multivariable analysis and nomogram development were used to identify predictors of reduced GLS.

Main Results:

• Male sex, diastolic blood pressure, high-density lipoprotein, and TyG index were independent predictors of reduced GLS.
• The developed nomogram model showed robust 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 screen CKD patients for abnormal GLS.
• This tool can optimize the utilization of STE, leading to conserved medical resources.
• The model offers a practical approach to managing cardiac health in CKD patients.