Role of automated functional imaging and myocardial work in assessment of cardiac function in children with obstructive sleep apnea.

Abstract

Early identification of abnormal left ventricular function in children with obstructive sleep apnea (OSA) is difficult using conventional echocardiographic indices and commonly used clinical markers of myocardial damage. We sought to investigate the value of automatic function imaging and myocardial work parameters in predicting early cardiac impairment in children having OSA with preserved left heart function and thereby identifying an optimal index for assessment. Fifty-two children who presented with symptoms of nocturnal sleep snoring and open-mouth breathing and 34 healthy controls were enrolled in this study. Clinical characteristics and conventional echocardiographic data were collected, and image analysis was performed using two-dimensional speckle-tracking echocardiography to obtain left ventricular global longitudinal strain (GLS), post-systolic index, peak strain dispersion, global work index (GWI), global constructive work (GCW), global wasted work, and global work efficiency. Children with OSA had significantly lower GLS, GWI, and GCW than those without (P < 0.05). Additionally, GWI (β = -32.87, 95% CI: -53.47 to -12.27), and GCW (β = -35.09, 95% CI: -55.35 to -14.84) were found to correlate with the disease severity in the multiple linear regression mode, with worsening values observed as the severity of the disease increased. ROC curve analysis revealed that GCW was the best predictor of myocardial dysfunction, with an AUC of 0.809 (P < 0.001), and the best cutoff point for diagnosing myocardial damage in children with OSA was 1965.5mmHg%, with a sensitivity of 92.5% and a specificity of 58.7%. GLS, GWI, and GCW were identified as predictors of myocardial dysfunction in children with OSA, with GCW being the best predictor.