Model for Prediction of Left Ventricular Myocardial Hypertrophy in Patients with Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is diagnosed in 25% of adults and associated with high fatal risks of cardiovascular complications. Left ventricular hypertrophy (LVH) is recognized as one of the markers of such risks. In this study, we attempted to create a mathematical model for predicting LVH among OAS patients with various levels of disease severity.

Materials and methods. In a prospective cohort study, we included 368 patients (358 male; age 46.0 [42.0; 49.0] yr.) with diagnosed OSA, arterial hypertension, grade I-II obesity (WHO classification 1997). The severity of sleep apnea was verified during nighttime computed somnography (CSG) on WatchPAT-200 hardware (ItamarMedical, Israel) with original software zzzPATTMSW ver. 5.1.77.7 (ItamarMedical, Israel) by registering the main respiratory polygraphic characteristics from 11.00 PM to 7:30 AM. Verification of LVH was performed in one- and two-dimensional modes in standard echocardiographic positions using Xario-200 ultrasound scanner (Toshiba, Japan) with 3.5 MHz transducer. Hemodynamic parameters of left ventricular (LV) systolic function (EF %, ESV, EDV) were determined by quantitative assessment of two-dimensional echocardiograms using the modified Simpson method. Evaluation of the systolic function of the right ventricle (RV) was performed in the «M»-mode by measuring the systolic excursion of the fibrous ring of the tricuspid valve (TAPSE).

Results. ESS and TSat90% (AUC = 0.975; SD = 0.00741; CI 95% [0.953; 0.988]) should be considered the best predictors for predicting LVH in various degrees of OSA severity, allowing us to offer a predictive model with a sensitivity of 93.7% and specificity of 93.8%, after conducting a questionnaire screening and computer somnographic study.

Conclusions. Our proposed model of clinical prediction of LVH among patients with various degrees of OAS is based on a carefully planned analysis of questionnaire and instrumental data, and is well applicable in real diagnostic procedures by a wide range of therapeutic practitioners.

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