Introduction
Sleep apnea syndrome (SAS) is a sleep disorder characterized by cessation in breathing. Moderate-to-severe SA has been described in up to 23% women and 49% man between ages 30 and 60 [
1]. Cardiovascular disease patients have a higher prevalence of SA than the general population and combining with SA links with negative cardiovascular outcomes, such as hypertension [
2], heart failure progression [
3,
4], and cardiac arrhythmias. Polysomnography (PSG) was the “golden standard” for SAS diagnosis. But the expense and unavailability of PSG greatly limited its screen for SAS widely. Despite increased awareness, SAS remains underdiagnosed [
5].
Previous study has shown that the transthoracic impedance measured by pacemaker minute ventilation sensors is closely correlated with the tidal volume [
6]. Therefore, the impedance might be used to detect the disturbances of ventilation during sleep. Recently, some pacemakers with ventilation sensor and special algorithms have been reported to be capable of screening sleep apnea [
7,
8]. The aim of this study was to appraise the accuracy of an advanced algorithm (Apnea Scan [ApScan] algorithm) in detecting severe SA in unselected pacing candidate.
Discussion
This study appraised the accuracy of AP Scan algorithm in SA screening. First, we reported a high prevalence rate of 76.4% for SA among pacemaker patients, which included 38.2% of the patients exhibiting moderate and severe SA. The prevalence of SA in this study is consistent with previous studies [
8,
12] (from 60 to 78%). But, the prevalence of moderate and severe SAS was relatively lower; previously, studies were about 50%, which may because the patients in this study were younger (67.1 ± 9.8 vs 73.8 ± 10.1). Among the advance SA (AHI ≥ 15 events/h) patients, obstructive SA (66.7%) and hypopnea (23.8%) accounted for more than 90%. The correlation between PM-RDI and PSG-AHI was 0.76 (
p < 0.001), and the bias of AHI was 14.0 ± 22.2/h. The Cronbach’s alpha between PM-RDI and PSG-AHI was 0.86; this result is not inferior than other home-based PSG recording systems [
13,
14]. These proved transthoracic impedance measurement together with AP Scan algorithm could be used to screen SA and monitor treatment effects in pacemaker patient. The result showed that an PM-RDI of 26 events/h is the optimal cutoff value for advance SA (AHI ≥ 15events/h) diagnosis, with a specificity of 70.6%, a sensitivity of 100%. The best PM-RDI cutoff value for severe SA diagnosis was 41 events/h; sensitivity and specificity were 81.6% and 88.6%, respectively.
Sleep apnea is one of the most common comorbidities in patients with cardiovascular disease. The PSG has been accepted as the “gold standard”for SA diagnosis, and the PSG-AHI score is used not only to diagnose SA but also to assess the severity. However, expensive and time consuming together with the limited available of sleep lab makes PSG impracticable for many patients. Previous studies revealed SA prevalence in pacemaker patients is high (59%), but mostly undiagnosed. In our study, none patients had ever been diagnosed by SA or undertaken PSG before PM implantation. But accurately, 76% patients combined with SA and more than one third patients were with advance SA. As a result, an alternative, reliable, and more convenient option could greatly improve SA detection. Some portable and home base and monitor became increasingly attractive.
Nowadays, almost all pacemaker could provide the rate adaptive pacing by combining with different kinds of sensors. Among these sensors, minute volume sensor could detect the respiratory rate and tidal volume by calculating transthoracic impedance [
15]. This function has been used to not only rate adaptive pacing, but also detect cardiac decompensations [
16]. More recently, some researchers have proved that change of respiratory and tidal volume could also be used to detect SA [
17]. Compared with PSG, the sensitivity of SA diagnosis by PM was 75~89%, and the specificity was 85~94%. With the novel algorithm, new generation pacemaker could provide the information about SA becoming increasingly attractive. Our study demonstrated PM-RDI had a good correlation in SA diagnosis. The optimal PM-RDI cutoff values for moderate and severe SA were 26 events/h and 41 events/h. Compared with other studies, it was relatively higher. This difference may due to SA definition which varies between different manufacturers. Previously, studies were carried out almost all in Sorin (Paris, France) devices. In this device, SA event was defined by breathing cessation for > 10 s or tidal volume reduced by ≥ 50% for more than 10 s. The pacemaker in our study is from Boston Scientific (Minnesota, USA); the apnoeas defined by AP Scan algorithm was breathing suspended for 10 s or more; hypopnoeas was tidal volume declined 26% of the baseline average tidal volume for > 10 s. We can see these two different devices have different criteria for hypopnoeas event. This may explain different diagnosis sensitivity and cutoff value in this study.
The change in transthoracic impedance could reflect to the change of tidal volumes, but the ventilation is decreased in both CSA and OSA. Pacemaker algorithm could not reveal the cessation of thoracic or abdominal movement, so the obstructive and central events cannot be differentiated by PM algorithms. But the purpose of this function in our opinion is not to substitute the PSG, but to screen SA in pacing patient in whom SA was seriously underdiagnosed. This also explained why nowadays algorithm is not so strict compared with previous ones. As for patient management, a further investigation with PSG is still needed. Due to lack of electroencephalography information, pacemaker could not distinguish sleep and awakening times. ApScan algorithm predefines a core sleep time; PM-RDI was calculated only during the sleep time. In this study, the sleep period set to “23 pm–6 am”. This may produce a systemic error. In real-life scenarios, the sleep time could be set more individually according to patient sleep diaries.
A special aspect of the ApScan algorithm is that it could provide convenient way to screen out SA in PM patients. In our study, nine patients were excluded. Among them, eight were due to lack of PSG data (4 refused PSG, 4 failed to have a result). Only one patient was excluded due to lack of ApScan data. The pacemaker algorithm was seemed to be more applicable. In addition, ability to continuously monitor SA means the possibility of make a diagnosis early and therefore initiating appropriate therapy in time. The last but not the least, the relationship among SA and arrhythmias gained considerable interest. Pacemaker could record the burden of arrhythmia and SA day by day for a period of time. And this will allow a prospective exploration of the relationship between these two clinic events.
In conclusion, SA is highly prevalent in pacemaker patients. Screening for SA with transthoracic impedance and ApScan algorithm may facilitate early diagnosis and timely treatment of SA in pacemaker patients, and provide long-term tendency on SA as well.
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The use of PSG as a “gold standard” and current severity guidelines are not based on cardiovascular disease end points, and the future of understanding what level of SA should be treated in non-sleepy patients remains unclear. Given the prevalence of SA, PSG is not, in my opinion, an appropriate test given its expense, availability, affordability, and utility. Like blood pressure, we need to look at outcomes in specific disease states to know what severity scores should be.
Ian Wilcox
NSW, Australia