Original ArticleSleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes
Introduction
Cardiovascular disease (CVD) is a major cause of mortality, claiming 33% of all deaths worldwide [1]. Early detection and control of CVD risk factors are therefore greatly encouraged for health management. Heart rate variability (HRV) is a useful term which is widely applied to describe the variation of intervals between two successive heart beats [2], such intervals are often called RR intervals. Since first was proposed, HRV has been rapidly adopted as a non-invasive method to study the cardiac autonomic modulation [3]. Evidences of an association between HRV and CVD, such as myocardial infarction [4,5], stroke [6], angina [7,8], coronary heart disease [9], coronary artery disease [[10], [11], [12]] and sudden cardiac death [13], have been reported. Furthermore, studies have put forward that HRV has predictive value for CVD outcomes [[14], [15], [16]]. For the general population, reduced HRV has a high correlation with incident coronary heart disease and death.
Among studies focused on the association between HRV and CVD, HRV signals used were usually acquired during daytime on those awake participants. Sleep, a totally different physiological condition from daytime awareness, constitutes a fundamental behavioral mechanism for all living organisms. For humans in particular, numerous evidences show that sleep is vital on maintaining physical health [17,18], cognitive function [19,20], recovery [21], memory [22], mood [23] and daytime functioning [24,25]. Furthermore, sleep, or sleep-related mechanisms, impose regulatory control over the cardiovascular system, since modulation of autonomic nervous system (ANS) are profoundly influenced by the sleep-wake cycle [26,27]. Eguchi et al., demonstrated that HRV during sleep was independently associated with an increased risk of CVD in patients with type 2 diabetes [28]. Vanoli et al., found that sleep HRV was highly relevant to the identification of autonomic derangements which may account for a higher risk of lethal events after myocardial infarction [29]. Recently, reduced parasympathetic modulation during sleep been reflected by the high frequency component of HRV was reported to be one potential mechanism underlying the increased prevalence of CVD among veterans with posttraumatic stress disorder [30]. Although the association between HRV and CVD has been well recognized, it is still largely unknown that whether HRV features, especially during sleep, can assist the prediction of the occurrence of CVD events after years of latency.
Several assessment systems for CVD risk have been proposed to predict individual CVD events, such as Framingham risk score [31], Reynolds risk score [32,33], QRISK2 risk score [34] and the prediction algorithm which is recommended by the American Heart Association/American College of Cardiology (ACC/AHA) [35]. Typical risk factors in these systems include age, systolic blood pressure, total and high-density lipoprotein cholesterol, smoking, hypertension and diabetes status. By means of such assessment systems, a large number of individuals at risk of CVD fail to be detected while some not at risk are given preventive treatment unnecessarily [36], new approaches are therefore still in demand to improve the accuracy of CVD prediction. Machine learning (ML) is a subset of artificial intelligence in the field of computer science that allows computers to use data to learn [37]. ML approaches have been widely applied in disease diagnosis and prognosis which achieving satisfied accuracy [38]. Recently, by adopting routine clinical data [36], ML algorithms were employed in a large-scale study to predict the first CVD event after 10 years. In comparison with the AHA/ACC risk prediction algorithm, results shows that the accuracy of CVD risk prediction is significantly improved by the application of ML [36]. However, to our best knowledge, few study employs HRV indices in ML models for CVD event prediction. Therefore, more explorations are required in predicting CVD outcomes automatically by using ML algorithms and HRV features.
It's worth noting that sleep is not a stable, but rather a complex process. For general population, a nocturnal sleep comprises four to six sleep cycles, which normally begin with light sleep, continue to deep sleep and end in rapid eye movement (REM) sleep [39]. According to the rules introduced by Rechtschaffen and Kales (R & K rules) [40], non-REM (NREM) sleep can be further classified into four stages (Stage 1, 2, 3, 4), and the current American Academy of Sleep Medicine rules combined Stages 3 and 4 and termed it N3 [41]. During sleep, ANS function is influenced by sleep state [26], resulting in an alteration of HRV across different sleep stages [[42], [43], [44]]. For healthy adults, HRV was observed to be decreased during NREM sleep with augmented parasympathetic modulation, and increased during REM sleep with a reduction in parasympathetic modulation [42]. Therefore, it is a rational way to comprehensively evaluate HRV in different sleep stages when using sleep HRV to predict CVD outcomes.
In the present study, we retrospectively analyze HRV data derived from an open-access database. On one hand, we target to investigate whether there is an association between sleep HRV and long-term CVD outcomes. On the other hand, we aim to find out whether ML model based on sleep HRV data and clinical characteristics can predict long-term CVD outcomes.
Section snippets
Participants
The HRV data used in this study were obtained from the Sleep Heart Health Study (SHHS) database [45]. The SHHS is a multi-center cohort study which aims to investigate whether sleep-disordered breathing is associated with an increased risk of cardiovascular events. In all, 6441 men and women aged 40 years and older were enrolled between November 1, 1995 and January 31, 1998 to take part in SHHS for a baseline polysomnography (PSG) monitoring using the Compumedics PS polysomnograph at home.
Baseline clinical characteristics of the included participants
Table 1 presents the baseline clinical characteristics of all included participants. CVD group had a significantly higher age, waist/hip ratio, BMI, height and RDI than the non-CVD group. Besides, compared with non-CVD group, CVD group had higher prevalence of diabetes and hypertension.
Between-group comparison of HRV metrics
The results of cross-sectional comparisons of HRV metrics are illustrated in Table 2. Compared with non-CVD group, a significantly (p < 0.05) decreased HF was found in CVD group regardless of sleep stages.
Discussion
In the present study, we investigated the association between sleep HRV and long-term CVD outcomes, and further adopted ML method to predict the outcomes based on HRV and other clinical features. Compared with non-CVD group, decreased HRV was observed during sleep in CVD group, in which participants have at least one CVD event during follow-up. Sleep HRV was further found to be independent predictor of CVD outcomes and positively correlated with CVD latency. Moreover, our findings demonstrated
Conclusions
For subjects with CVD risks, ANS alterations during sleep may present a long time prior to the onset of a CVD event. Such alterations can be captured by the changes in multiple HRV metrics, specially, decreased HF. A combination of sleep HRV measuring and ML techniques can assist the early prediction of CVD outcomes. Since ambulatory ECG monitoring is readily available and accessible in a clinical setting, large-scale screening to detect HRV alterations may be assistant in early diagnosis and
Author contribution statement
Lulu Zhang: Investigation, Formal analysis, Validation, Roles/Writing - original draft.
Huili Wu: Conceptualization, Project administration.
Xiangyu Zhang: Data curation, Software.
Xinfa We: Project administration.
Fengzhen Hou: Data curation, Project administration, Supervision, Writing - review & editing.
Yan Ma: Methodology.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 61401518, 31671006 and 61771251), Jiangsu Provincial Key R & D Program (Social Development) (Grant No. BE2015700 and BE2016773), and Natural Science Research Major Program in Universities of Jiangsu Province (Grant No. 16KJA310002). The authors would like to acknowledge the support team of the forum in the Sleep Heart Health Study for their detailed explanations and assistance in our use of the dataset.
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