NCT05435001

Screening of Sleep Apnea by Holter Electrocardiography: Validation of Heart Rate Variability Analysis Algorithm

Evolution of a New Algorithm of Heart Rate Variability Analysis From Two-channel Holter Electrocardiogram in Pre-diagnosis of Obstructive Sleep Apnea Syndrome: a Study on Diagnostic Accuracy

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 107 (actual)
• Sponsor: Izmir Dr Suat Seren Chest Diseases and Surgery Education and Research Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Not accepted

Summary

Obstructive sleep apnea syndrome (OSAS) is a growing health concern affecting up to 60 % of population with cardiovascular disease. Despite the high cardiovascular morbidity and mortality associated with this syndrome, the substantial inconvenience and cost of polysomnography recordings may delay routine evaluation. Polysomnography (PSG) is the gold standard for diagnosis. However, this is a costly and time-consuming examination. Sympathoadrenergic balance obtained from the routine Holter monitoring suggesting the presence of OSAS, can enable patients to be guided and their PSGs to be primarily held.Abnormalities in nocturnal cyclical heart rate (HR) variations have previously been described in sleep-related breathing disorders. Compared with PSG, holter electrocardiogram has the advantages of pervasion, lower cost, no need for overnight hospitalization, greater similarity to normal conditions, and good compliance. The observation of changes in heart rate associated with apneic events has a potential to be used as an alternative technique for identification of subjects with OSAS. In regard to the feasibility of screening OSAS by HRV analysis by holter electrocardiogram monitoring, it has already been reported that a 24-h electrocardiographic monitoring might be useful to diagnose OSAS. It became a more feasible technique to use following the development of a convenient recorder for OSAS screening by analyzing changes in heart rate.

Detailed description

To find out whether a new algorithm of HRV analysis from holter ECG monitoring can be used as a screening test for the diagnosis of patients with moderate-to-severe risk of obstructive sleep apnea syndrome (OSAS) with an acceptable accuracy.For this, overnight sleep pattern will be investigated in at least 107 individuals by polysomnography and 24-h ambulatory electrocardiography. Heart rate variability sleep apnea risk score (HRV-SARS) will be calculated using HRV analyses. The patients were recruited from individuals referred to our university hospital's sleep center for a polysomnography recording because of clinically suspected OSAS (with at least one of the following obstructive sleep apnea symptoms: witnessed apnea, snoring and/or daytime sleepiness) from May to July 2022. Prospectively 107 patients enrolled in the study according to inclusion and exclusion criteria. Exclusion criteria were permanent or paroxysmal atrial fibrillation, permanent pacemaker, history of other sleep disorders, severe cardiopulmonary disease, severe diabetes mellitus, autonomic dysfunction or major physical or mental ailments. All patients underwent both a full polysomnography recording and ECG Holter monitoring. This new algorithm of HRV analysis from holter ECG monitoring may represent an accurate and inexpensive screening tool in clinically suspected OSAS patients and may help focus resources on those at the highest risk.

Conditions

• Sleep Apnea Syndromes

Interventions

Type	Name	Description
DEVICE	Holter ECG Monitoring	Holter electrocardiogram monitoring will be carried out for 24 h simultaneously with the PSG monitoring using a 2- lead ambulatory electrocardiograph (Fysiologic; kind courtesy: MedTech Company, Amsterdam, Holland). We will calculate the time-domain, frequency-domain and non-linear indices by HRV. Several parameters describing the differences between RR intervals will be calculated: the square root of the mean of the sum of the squares of differences between adjacent normal RR intervals (r-MSSD), SD of NN intervals (SDNN), SD of the averages of NN intervals in all 5-minute segments of the recording (SDANN), and mean of the SD of all NN intervals for all consecutive 5-minute segments of the recording (SDNN index). All variables will be calculated for the 24-hour, daytime (2:00 to 9:00 PM), and nighttime (midnight to 7 AM) periods, and the differences between daytime and nighttime values (D\[D/N\]) will be computed.

Timeline

• Start date: 2022-05-05
• Primary completion: 2022-07-01
• Completion: 2022-08-01
• First posted: 2022-06-28
• Last updated: 2023-02-21

Locations

1 site across 1 country: Turkey (Türkiye)