Left ventricular diastolic dysfunction in uremia patients: the role of four-dimensional automatic left atrial...

Early detection of left ventricular diastolic dysfunction (LVDD) in uremic patients is critical for optimizing clinical outcomes in chronic kidney disease (CKD). This study developed a predictive model using four-dimensional automatic left atrial quantification (4D Auto LAQ) technology to assess LVDD progression in this population.

This single-center retrospective study analyzed data from The Second Affiliated Hospital of Hainan Medical University. The cohort comprised 108 uremic patients receiving maintenance therapy and 38 age- and sex-matched healthy controls. Routine laboratory parameters and four-dimensional (4D) echocardiography data were extracted from the hospital's electronic medical record system. Systematic comparisons between groups were conducted using these metrics. A least absolute shrinkage and selection operator (LASSO)-penalized logistic regression model was developed and validated to identify predictors. The model specifically assessed predictors of LVDD severity in uremia.

Left atrial volume parameters increased progressively with rising left ventricular filling pressure. In uremic patients with LVDD, both left atrial reservoir and conduit functions were significantly impaired. Notably, even among individuals with preserved diastolic function, left atrial conduit longitudinal strain (LAScd) exhibited significant alterations (P<0.001). The LASSO-penalized logistic regression model demonstrated strong discriminatory performance for predicting LVDD in uremic patients, yielding an area under the curve (AUC) of 0.811 [95% confidence interval (CI): 0.728-0.893]. Multivariable analysis identified left atrial reservoir longitudinal strain (LASr) [odds ratio (OR) =1.105; 95% CI: 1.028-1.188; P=0.007] and hypertension prevalence (OR =3.287; 95% CI: 1.139-9.481; P=0.028) as independent predictors of LVDD.

The 4D Auto LAQ technique enables accurate measurement of cardiac mechanics, revealing progressive functional deterioration in uremic cardiomyopathy. This method effectively identifies early-stage LVDD during critical disease stages, supporting improved cardiovascular risk evaluation.