Original article Usefulness of ambulatory ECG in the diagnosis of sleep-related breathing disorders

Abstract

Background: Sleep-related breathing disorders (SRBD) are the additional factor related to poor prognosis in patients with cardiovascular disorders. The apnoea/hypopnoea index (AHI), describing the number of apnoea and hypopnoea episodes per one hour of sleep, has been used as a marker of severity of the disorder. The disease is present in 4% of men and 2% of women above 40 years of age. However, SRBD are diagnosed in less than 3% of patients with this syndrome due to lack of awareness of the disease among health care practitioners and patients. Polysomnography (PSG) has been used as a golden standard for detecting SRBD, however this test is available only in selected centres. Therefore, a simple, fast and inexpensive test for screening for SRBD is necessary. Respiratory activity influences the amplitude of ECG signal whereas heart rate variability (HRV) depicts the activity of the autonomic nervous system. These associations have been used to develop a new method for detection of SRBD involving analysis of HRV and morphology of ECG signal in ECG monitoring.
Aim: Assessment of accuracy of SRBD detection using estimated AHI (Est.AHI), calculated from Holter ECG recordings.
Methods: In a study group consisting of 74 patients tested for SRBD, simultaneous PSG and 24-hour ECG monitoring were performed. Following PSG, AHI for each patient was calculated. According to the AHI values patients were classified as SRBD patients (AHI >15), non-SRBD patients (AHI <5), whereas 12 individuals had borderline SRBD (5≤ AHI ≤15). Age, prevalence of concomitant disorders and treatment were similar in all groups. In all individuals the Est.AHI value was calculated based on ECG recording. Considering the AHI value as a reference parameter discriminating SDB and non-SRBD patients, the number of false positive and false negative results for detecting SDB with the Est.AHI was calculated. Moreover, the SRBD detection accuracy using the Est.AHI calculation was evaluated by the receiver-operator characteristic (ROC) curves which were used to calculate area under curve (AUC), sensitivity, specificity, as well as positive (PPV) and negative (NPV) predictive values for optimal cut-off value.
Results: According to Est.AHI, 50 (68%) patients were correctly diagnosed. The ROC analysis showed high accuracy of SRBD detection using Est.AHI: AUC – 0.91 with sensitivity – 91.2%, specificity – 87.5%, PPV – 88.6%, and NPV – 88.9%. The cut-off value of Est.AHI set at 17 was optimal for the differentiation between patients with or without SRBD.
Conclusions: The Est.AHI calculated with the Lifescreen Apnea software from Holter ECG is an accurate, specific and sensitive method for the detection and classification of obstructive and mixed SRBD.