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Background <p>There is limited investigation on the longitudinal association between common electrocardiogram (ECG) features and the incidence of obstructive sleep apnea (OSA). This study aimed to examine the association of common ECG features with the incidence of OSA in a prospective cohort.</p> Methods <p>2,563 participants aged 60 years or more were selected from the baseline survey of the Guangzhou Heart Study. OSA was evaluated by the Berlin Questionnaire. Eight electrocardiogram features including PR interval, QRS duration, QT interval, QTc interval, heart rate, P-wave, R-wave, and T-wave were extracted from 24-hour single-lead Holter. Relative risk (RR) with 95% confidence interval (CI) was estimated using the multivariate logistic regression model. A receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of ECG features.</p> Results <p>397 (15.5%) participants were divided into the OSA group and 2,166 (84.5%) into the OSA non-group. When comparing the highest with the lowest quartiles, heart rate was related to a 30% reduced risk of OSA (RR: 0.70, 95%CI: 0.51–0.97) after adjustment for possible confounders. Participants with prolonged PR interval were more likely to be at risk of OSA (RR: 2.68, 95%CI: 1.02–6.55). No significant association was found between the other six ECG features and OSA risk. Area under ROC curve was 0.676 (95% CI: 0.648–0.704), 0.676 (95%CI: 0.648–0.704), and 0.678 (95%CI: 0.651–0.706) for heart rate, PR interval, and their combination, respectively.</p> Conclusions <p>The results suggest that heart rate and PR interval are related to OSA incidence. Future studies should be carried out in different populations, and consider the use of portable monitors together with scales to comprehensively determine OSA, and comprehensively elucidate the relationship of various ECG features and their changes with OSA occurrence.</p>

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Association of electrocardiogram features with risk of obstructed sleep apnea: a population-based cohort study

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • Chuchu Wu,
  • Jun Huang,
  • Minjing Huang,
  • Yiting Tan,
  • Chuanjiang Chen,
  • Murui Zheng,
  • Wenjing Zhao,
  • Yangjie Xu,
  • Lili Guo,
  • Xiuyi Wu,
  • Yumei Xue,
  • Hai Deng,
  • Xudong Liu

摘要

Background

There is limited investigation on the longitudinal association between common electrocardiogram (ECG) features and the incidence of obstructive sleep apnea (OSA). This study aimed to examine the association of common ECG features with the incidence of OSA in a prospective cohort.

Methods

2,563 participants aged 60 years or more were selected from the baseline survey of the Guangzhou Heart Study. OSA was evaluated by the Berlin Questionnaire. Eight electrocardiogram features including PR interval, QRS duration, QT interval, QTc interval, heart rate, P-wave, R-wave, and T-wave were extracted from 24-hour single-lead Holter. Relative risk (RR) with 95% confidence interval (CI) was estimated using the multivariate logistic regression model. A receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of ECG features.

Results

397 (15.5%) participants were divided into the OSA group and 2,166 (84.5%) into the OSA non-group. When comparing the highest with the lowest quartiles, heart rate was related to a 30% reduced risk of OSA (RR: 0.70, 95%CI: 0.51–0.97) after adjustment for possible confounders. Participants with prolonged PR interval were more likely to be at risk of OSA (RR: 2.68, 95%CI: 1.02–6.55). No significant association was found between the other six ECG features and OSA risk. Area under ROC curve was 0.676 (95% CI: 0.648–0.704), 0.676 (95%CI: 0.648–0.704), and 0.678 (95%CI: 0.651–0.706) for heart rate, PR interval, and their combination, respectively.

Conclusions

The results suggest that heart rate and PR interval are related to OSA incidence. Future studies should be carried out in different populations, and consider the use of portable monitors together with scales to comprehensively determine OSA, and comprehensively elucidate the relationship of various ECG features and their changes with OSA occurrence.