Lulu Zhang1, Huili Wu2, Xiangyu Zhang3, Xinfa Wei4, Fengzhen Hou5, Yan Ma6. 1. Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing 210009, China. 2. ENT Sleep Monitoring Center, Coal General Hospital, Beijing 100028, China. 3. SEU-lenovo S-H-E Wearable Intelligent Monitoring Lab, State Key Laboratory of Bioelectronics, The School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China. 4. Department of Otolaryngology, Coal General Hospital, Beijing 100028, China. 5. Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing 210009, China. Electronic address: houfz@cpu.edu.cn. 6. Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, United States.
Abstract
OBJECTIVE: We aimed to investigate the association between sleep HRV and long-term cardiovascular disease (CVD) outcomes, and further explore whether HRV features can assist the automatic CVD prediction. METHODS: We retrospectively analyzed polysomnography (PSG) data obtained from 2111 participants in the Sleep Heart Health Study, who were followed up for a median of 11.8 years after PSG acquisition. During follow-up, 1252 participants suffered CVD events (CVD group) and 859 participants remained CVD-free (non-CVD group). HRV measures, derived from time-domain and frequency-domain, were calculated. Regression models were created to determine the independent predictor for long-term CVD outcomes, and to explore the association between HRV and CVD latency. Furthermore, based on HRV and other clinical features, a model was trained to automatically predict CVD outcomes using the eXtreme Gradient Boosting algorithm. RESULTS: Compared with the non-CVD group, decreased HRV during sleep was found in the CVD group. HRV, particularly its component of high frequency (HF), was demonstrated to be independent predictor of CVD outcomes. Moreover, normalized HF was positively correlated with CVD latency. The proposed prediction model achieved a total accuracy of 75.3%, in which sleep HRV features served as a supplement to the well-recognized CVD risk factors, such as aging, adiposity and sleep disorders. CONCLUSIONS: Association between sleep HRV and long-term CVD outcomes was demonstrated here, suggesting that altered HRV during sleep might occur many years prior to the onset of CVD. Machine learning models, combining sleep HRV and other clinical characteristics, should be promising in the early prediction of CVD outcomes.
OBJECTIVE: We aimed to investigate the association between sleep HRV and long-term cardiovascular disease (CVD) outcomes, and further explore whether HRV features can assist the automatic CVD prediction. METHODS: We retrospectively analyzed polysomnography (PSG) data obtained from 2111 participants in the Sleep Heart Health Study, who were followed up for a median of 11.8 years after PSG acquisition. During follow-up, 1252 participants suffered CVD events (CVD group) and 859 participants remained CVD-free (non-CVD group). HRV measures, derived from time-domain and frequency-domain, were calculated. Regression models were created to determine the independent predictor for long-term CVD outcomes, and to explore the association between HRV and CVD latency. Furthermore, based on HRV and other clinical features, a model was trained to automatically predict CVD outcomes using the eXtreme Gradient Boosting algorithm. RESULTS: Compared with the non-CVD group, decreased HRV during sleep was found in the CVD group. HRV, particularly its component of high frequency (HF), was demonstrated to be independent predictor of CVD outcomes. Moreover, normalized HF was positively correlated with CVD latency. The proposed prediction model achieved a total accuracy of 75.3%, in which sleep HRV features served as a supplement to the well-recognized CVD risk factors, such as aging, adiposity and sleep disorders. CONCLUSIONS: Association between sleep HRV and long-term CVD outcomes was demonstrated here, suggesting that altered HRV during sleep might occur many years prior to the onset of CVD. Machine learning models, combining sleep HRV and other clinical characteristics, should be promising in the early prediction of CVD outcomes.
Authors: G A Lanza; D Cianflone; A G Rebuzzi; G Angeloni; A Sestito; G Ciriello; G La Torre; F Crea; A Maseri Journal: Heart Date: 2005-12-30 Impact factor: 5.994
Authors: Hua Qin; Fernando Vaquerizo-Villar; Nicolas Steenbergen; Jan F Kraemer; Thomas Penzel Journal: Adv Exp Med Biol Date: 2022 Impact factor: 3.650
Authors: Joshua Hagen; Jason D Stone; W Guy Hornsby; Mark Stephenson; Robert Mangine; Michael Joseph; Scott Galster Journal: J Funct Morphol Kinesiol Date: 2020-09-03
Authors: Miki Sato; Feni Betriana; Ryuichi Tanioka; Kyoko Osaka; Tetsuya Tanioka; Savina Schoenhofer Journal: Int J Environ Res Public Health Date: 2021-12-07 Impact factor: 3.390