Development of a Diagnostic Model Based on Serum ST2 and Left Atrial Strain for Left Ventricular Systolic Dysfunction in Patients With Chronic Coronary Syndrome.

AIMS: The study investigated the predictive value of serum soluble ST2 (sST2) and left atrial strain parameters for left ventricular systolic dysfunction (LVSD), and the correlation analysis with corresponding indicators in patients with chronic coronary syndrome (CCS).

METHODS

A total of 135 patients with CCS who presented to the Cardiology Department Zhongda hospital and underwent echocardiographic examinations from January 2025 to December 2025 were ultimately included. Based on two-dimensional speckle tracking echocardiography, left ventricular global longitudinal strain and left atrial strain were measured within 72 h of CCS diagnosis, and the patients were categorized into two groups: LV GLS > -17%, LVSD group; and LV GLS≤-17%, non- LVSD group. Baseline data were collected from all patients and compared between the two groups. The correlation between sST2 and LA parameters in CCS patients was evaluated using Spearman correlation.  Based on the results of the multivariate logistic regression analysis, a predictive model for LVSD in CCS patients was established.

RESULTS

In the LVSD group, sST2 levels, early diastolic mitral inflow velocity (E)/ early diastolic mitral annular velocity (e') ratio, left atrial volume index (LAVI), and left atrial stiffness index (LASI) were increased. In the CCS patients, sST2, E/e', LAVI, and LASI demonstrated good correlation. sST2 and LASI were independent predictors for diagnosing LVSD in patients with CCS. ROC curve analysis showed that ST2 and LASI had diagnostic value for left ventricular systolic dysfunction in patients with CCS.

CONCLUSION

This study found that in patients with CCS complicated by LVSD, sST2 levels were elevated, left atrial strain parameters were impaired, and a certain correlation was observed between the two. Furthermore, we investigated whether sST2 and LASI are independent predictors for diagnosing LVSD in CCS patients. By combining these two indicators, a diagnostic model can be constructed.