Cardiopulmonary Coupling.

Cardiopulmonary coupling (CPC) is a technique that generates sleep spectrogram by calculating the cross-spectral power and coherence of heart rate variability and respiratory tidal volume fluctuations. There are several forms of CPC in the sleep spectrogram, which may provide information about normal sleep physiology and pathological sleep states. Since CPC can be calculated from any signal recording containing heart rate and respiration information, such as photoplethysmography (PPG) or blood pressure, it can be widely used in various applications, including wearables and non-contact devices. When derived from PPG, an automatic apnea-hypopnea index can be calculated from CPC-oximetry as PPG can be obtained from oximetry alone. CPC-based sleep profiling reveals the effects of stable and unstable sleep on sleep apnea, insomnia, cardiovascular regulation, and metabolic disorders. Here, we introduce, with examples, the current knowledge and understanding of the CPC technique, especially the physiological basis, analytical methods, and its clinical applications.

Effects of Cardiac Resynchronization Therapy on Cardio-Respiratory Coupling.

In this study, the effect of cardiac resynchronization therapy (CRT) on the relationship between the cardiovascular and respiratory systems in heart failure subjects was examined for the first time. We hypothesized that alterations in cardio-respiratory interactions, after CRT implantation, quantified by signal complexity, could be a marker of a favorable CRT response. Sample entropy and scaling exponents were calculated from synchronously recorded cardiac and respiratory signals 20 min in duration, collected in 47 heart failure patients at rest, before and 9 months after CRT implantation. Further, cross-sample entropy between these signals was calculated. After CRT, all patients had lower heart rate and CRT responders had reduced breathing frequency. Results revealed that higher cardiac rhythm complexity in CRT non-responders was associated with weak correlations of cardiac rhythm at baseline measurement over long scales and over short scales at follow-up recording. Unlike CRT responders, in non-responders, a significant difference in respiratory rhythm complexity between measurements could be consequence of divergent changes in correlation properties of the respiratory signal over short and long scales. Asynchrony between cardiac and respiratory rhythm increased significantly in CRT non-responders during follow-up. Quantification of complexity and synchrony between cardiac and respiratory signals shows significant associations between CRT success and stability of cardio-respiratory coupling.

The effect of acute exposure to morphine on breathing variability and cardiopulmonary coupling in men with obstructive sleep apnea: A randomized controlled trial.

Opioid-related deaths from respiratory depression are increasing but there is only limited information on the effect of morphine on breathing during sleep. This study aimed to detect and quantify opioid-induced cardiorespiratory pattern changes during sleep in obstructive sleep apnea (OSA) patients using novel automated methods and correlate these with conventional polysomnography (PSG) measures. Under a randomized double-blind placebo-controlled crossover design, 60 male OSA patients attended two one-night visits to the sleep laboratory, at least a week apart. Either a 40-mg controlled-release oral morphine dose or placebo was administered. Breathing during sleep was measured by standard in-laboratory PSG. We analysed the inter-breath interval (IBI) from the PSG flow channel to quantify breathing irregularity. Cardiopulmonary coupling (CPC) was analysed using the PSG electrocardiogram (ECG) channel. Following the consumption of morphine, the 60 OSA patients had fewer breaths (p = .0006), a longer inter-breath interval (p < .0001) and more irregular breathing with increased IBI coefficient of variation (CV) (p = .0015) compared to the placebo night. A higher CPC sleep quality index was found with morphine use. The change of key IBI and CPC parameters was significantly correlated with the change of key PSG sleep-disordered breathing parameters. In conclusion, 40 mg controlled-release morphine resulted in a longer breathing cycle and increased breathing irregularity but generally more stable sleep in OSA patients. The significant links between the IBI and CPC techniques and a range of PSG sleep-disordered breathing parameters may suggest a practical value as surrogate overnight cardiorespiratory measurements, because both respiratory flow and ECG can be detected by small portable devices.

Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis.

PURPOSE: Despite the increasing number of research studies of cardiopulmonary coupling (CPC) analysis, an electrocardiogram-based technique, the use of CPC in underserved population remains underexplored. This study aimed to first evaluate the reliability of CPC analysis for the detection of obstructive sleep apnea (OSA) by comparing with polysomnography (PSG)-derived sleep outcomes. METHODS: Two hundred five PSG data (149 males, age 46.8 ± 12.8 years) were used for the evaluation of CPC regarding the detection of OSA. Automated CPC analyses were based on ECG signals only. Respiratory event index (REI) derived from CPC and apnea-hypopnea index (AHI) derived from PSG were compared for agreement tests. RESULTS: CPC-REI positively correlated with PSG-AHI (r = 0.851, p < 0.001). After adjusting for age and gender, CPC-REI and PSG-AHI were still significantly correlated (r = 0.840, p < 0.001). The overall results of sensitivity and specificity of CPC-REI were good. CONCLUSION: Compared with the gold standard PSG, CPC approach yielded acceptable results among OSA patients. ECG recording can be used for the screening or diagnosis of OSA in the general population.

Sleep apnea diagnosis in children using software-generated apnea-hypopnea index (AHI) derived from data recorded with a single photoplethysmogram sensor (PPG) : Results from the Childhood Adenotonsillectomy Study (CHAT) based on cardiopulmonary coupling analysis.

OBJECTIVE: Sleep quality is vital for healthy development in children. Sleep disorders are prevalent and negatively affect sleep quality. Early identification and appropriate intervention can improve children's health and quality of life. The current reference standard, polysomnography (PSG) has limitations regarding availability, cost, and access and may not replicate normal sleep patterns in the home. Simple, accurate sleep tests, available for repeated testing should be beneficial in management of sleep disorders. METHOD: Secondary analysis of PSG data from the prospective multicenter Childhood Adenotonsillectomy Trial (CHAT) to evaluate FDA-cleared cloud-based software (Software-as-a-Medical-Device), which is based on analysis of photoplethysmogram data (PPG; plethysmogram-signal (PLETH) and oxygen saturation data (SpO2)), to automatically generate a novel apnea-hypopnea index (sAHI). sAHI is compared to manually scored AHI from PSG. RESULTS: Significant correlation is observed comparing the software-generated sAHI and manually derived AHI from the in-laboratory PSG-studies (Pearson correlation = 0.954, p < 0.0001) and receiver operating characteristics (ROC) demonstrate strong agreement in all OSA categories (mild, moderate, severe) 91.4%[CI95%89.5, 93.4]; 96.7%[CI95%95.4, 97.9]; 98.6%[CI95%97.8, 99.4], sensitivities 95.4%[CI95%93.2, 97.0]; 86.5%[CI95%80.3, 91.3]; 88.4%[CI95%78.4, 94.9] and specificities 84.4%[CI95%79.7, 88.4]; 99.2%[CI95%98.2, 99.7]; 99.6%[CI95%98.8, 99.9], respectively. CONCLUSION: sAHI is comparable to manual scoring of AHI from in-laboratory PSG studies and effective to rule-in and rule-out obstructive sleep apnea (OSA) in all disease categories, providing safe and convenient approach for diagnosis and management of OSA in children. The data is recorded with a single-sensor, making the method suitable for multi-night testing in the child's home at considerably lower cost. This technology provides a simple tool to adhere to guidelines for diagnosis and management of OSA in children. CLINICAL TRIAL REGISTRATION NAME AND NUMBER: Childhood Adenotonsillectomy Study for Children with OSA (CHAT) https://clinicaltrials.gov/ct2/show/NCT00560859.

[Cardiopulmonary coupling analysis in diagnosis of sleep disorders].

Objectives: To compare diagnostic consistency for chronic insomnia (CI) and obstructive sleep apnea (OSA) between cardiopulmonary coupling (CPC) and polysomnographm (PSG). Methods: Two hundred and twenty-one patients were enrolled from the Department of Sleep Disorders, Chaohu Hospital affiliated to Anhui Medical University from July 2018 to December 2019, and monitored with overnight CPC and PSG simultaneously. According to clinical representations and PSG results, there were 88 males and 80 females with CI and OSA, including chronic insomnia (CI group, 93 cases), OSA (OSA group, 36 cases) and comorbid OSA with CI (COI group, 39 cases). The consistency of sleep and OSA parameters measured with CPC and PSG were analyzed. Results: (1)For all patients and CI group, the total sleep time (TST), sleep efficiency (SE) and rapid eye movement (REM) sleep time measured by CPC were significantly higher than those measured with PSG, and the wake time after sleep onset (WASO) was significantly lower than that measured with PSG (the specific median comparisons were as follows 420.0 min vs 395.5 min, 93.7% vs 81.8%, 90.0 min vs 37.5 min, 18.0 min vs 63.0 min in CI group, respectively; 414.0 min vs 392.5 min, 91.9% vs 81.9%, 72.0 min vs 34.8 min, 24.0 min vs 58.4 min in all patients, respectively (all P≤0.001). However, in the OSA patients, the TST, SE, WASO, REM sleep time and NREM sleep time measured using two methods were similar (all P>0.05). (2) According to OSA criteria, the consistency between CPC and PSG was fair (κ=0.255). Only CPC has a certain degree of value for OSA screening when the AHI ≥ 20/h (κ=0.580, sensitivity: 0.85, specificity: 0.82, positive predictive value: 0.59, negative predictive value: 0.95, positive likelihood ratio: 4.72). Conclusion: CPC technology may overestimate the sleep quality of CI patients, and its consistency is fair compared with that of PSG in the diagnosis of OSA.

Multiscale Entropy Analysis: Application to Cardio-Respiratory Coupling.

It is known that in pathological conditions, physiological systems develop changes in the multiscale properties of physiological signals. However, in real life, little is known about how changes in the function of one of the two coupled physiological systems induce changes in function of the other one, especially on their multiscale behavior. Hence, in this work we aimed to examine the complexity of cardio-respiratory coupled systems control using multiscale entropy (MSE) analysis of cardiac intervals MSE (RR), respiratory time series MSE (Resp), and synchrony of these rhythms by cross multiscale entropy (CMSE) analysis, in the heart failure (HF) patients and healthy subjects. We analyzed 20 min of synchronously recorded RR intervals and respiratory signal during relaxation in the supine position in 42 heart failure patients and 14 control healthy subjects. Heart failure group was divided into three subgroups, according to the RR interval time series characteristics (atrial fibrillation (HFAF), sinus rhythm (HFSin), and sinus rhythm with ventricular extrasystoles (HFVES)). Compared with healthy control subjects, alterations in respiratory signal properties were observed in patients from the HFSin and HFVES groups. Further, mean MSE curves of RR intervals and respiratory signal were not statistically different only in the HFSin group (p = 0.43). The level of synchrony between these time series was significantly higher in HFSin and HFVES patients than in control subjects and HFAF patients (p < 0.01). In conclusion, depending on the specific pathologies, primary alterations in the regularity of cardiac rhythm resulted in changes in the regularity of the respiratory rhythm, as well as in the level of their asynchrony.

Objective sleep quality and metabolic risk in healthy weight children results from the randomized Childhood Adenotonsillectomy Trial (CHAT).

BACKGROUND: We hypothesized that cardiopulmonary coupling (CPC) sleep quality reflects cardiovascular and cardiometabolic health, in healthy weight children. METHODS: Retrospective signal analysis of existing ECG data utilizing CPC, FDA cleared, software as medical device (SaMD). ECG signals were extracted from baseline polysomnography studies in the prospective Childhood Adenotonsillectomy Trial database, multicenter, single-blind, randomized controlled trial of 5.0-9.9-year-old children identified with obstructive sleep apnea syndrome without severe hypoxemia. Healthy weight was defined as age- and gender-specific BMI in the 5th-85th percentile range and overweight above the 85th percentile. The cohort was stratified based on CPC sleep quality Index (SQI) defined as high sleep quality (SQI ≥ 80) or low sleep quality (SQI < 60). Cardiovascular, cardiometabolic, quality of life, and cognition were compared between the sleep quality groups. RESULTS: Healthy weight children with low sleep quality had more fragmented sleep with significantly higher arousal index (10.0 ± 4.3 vs.7.2 ± 3.1; p = 0.00) and eLFCBB (12.4 ± 4.9 vs. 0.9 ± 1.0; p < 0.001) CPC indicator of sleep fragmentation, higher average heart rate during sleep (84.5 ± 10.6 vs. 79.4 ± 7.1; p = 0.03) and worse insulin/glucose ratio (1.7 ± 1.6 vs. 1.1 ± 1.1; p = 0.03) and fasting insulin levels (7.9 ± 7.2 vs.5.3 ± 5.5; p = 0.05) when compared to healthy weight children with high sleep quality. SQI significantly correlates with average heart rate during sleep, insulin and triglyceride levels; for a unit increase in SQI, there is 0.154 unit decrease in average heart rate during sleep, 0.109 unit in insulin levels and 0.332 unit in triglyceride levels, respectively. CONCLUSION: CPC sleep quality offers insights into pediatric sleep and how it affects cardiovascular and cardiometabolic health. ECG is simple signal to collect, which makes this method practical for testing sleep, over multiple nights, and on multiple occasions providing information on sleep dynamics not possible before. TRIAL REGISTRATION: NCT00560859.

Validation of a portable monitoring device for the diagnosis of obstructive sleep apnea: electrocardiogram-based cardiopulmonary coupling.

PURPOSE: We aimed to evaluate the validity of the cardiopulmonary coupling (CPC) device, a limited-channel portable monitoring device for obstructive sleep apnea (OSA) screening in one single-center cohort, in particular in those with some cardiovascular diseases since the cardiopulmonary coupling might be different from those without. METHODS: Consecutive patients referred to the sleep medical center for assessment of possible OSA were enrolled in this study. Patients were examined with standard polysomnography (PSG) and CPC evaluation simultaneously. The results of the two examinations were compared in all subjects and in those with or without cardiovascular abnormalities. RESULTS: A total of 179 subjects suspected with OSA were finally analyzed. According to OSA severity degree based on AHI, the area under ROC curve for the CPC device in the whole cohort patients was 0.79 (mild), 0.79 (moderate), and 0.86 (severe OSA), respectively (all p < 0.001). For patients with cardiovascular disease with different OSA severity, the area under the ROC curve was 0.86 (mild), 0.73 (moderate), and 0.83 (severe OSA), respectively (all p < 0.0001), and 0.74 (mild), 0.85 (moderate), and 0.91 (severe OSA), respectively in patients without cardiovascular disease (all p < 0.0001). CONCLUSIONS: The overall performance of CPC technique was acceptable to assess OSA in subjects with clinical suspicion of OSA, and thus it might act as a fast tool to screen OSA patients. However, the sensitivity of CPC technology for patients with cardiovascular disease was relatively insufficient. Therefore, CPC technology should be carefully interpreted in OSA screening in those with cardiovascular disease.

Sleep apnea detection: accuracy of using automated ECG analysis compared to manually scored polysomnography (apnea hypopnea index).

INTRODUCTION: Adequate sleep is fundamental to wellness and recovery from illnesses and lack thereof is associated with disease onset and progression resulting in adverse health outcomes. Measuring sleep quality and sleep apnea (SA) at the point of care utilizing data that is already collected is feasible and cost effective, using validated methods to unlock sleep information embedded in the data. The objective of this study is to determine the utility of automated analysis of a stored, robust signal widely collected in hospital and outpatient settings, a single lead electrocardiogram (ECG), using clinically validated algorithms, cardiopulmonary coupling (CPC), to objectively and accurately identify SA. METHODS: Retrospective analysis of de-identified PSG data with expert level scoring of Apnea Hypopnea Index (AHI) dividing the cohort into severe OSA (AHI > 30), moderate (AHI 15-30), mild (AHI 5-15), and no disease (AHI < 5) was compared with automated CPC analysis of a single lead ECG collected during sleep for each subject. Statistical analysis was used to compare the two methods. RESULTS: Sixty-eight ECG recordings were analyzed. CPC identified patients with moderate to severe SA with sensitivity of 100%, specificity of 81%, and agreement of 93%, LR+ (positive likelihood ratio) 5.20, LR- (negative likelihood ratio) 0.00 and kappa 0.85 compared with manual scoring of AHI. CONCLUSION: The automated CPC analysis of stored single lead ECG data often collected during sleep in the clinical setting can accurately identify sleep apnea, providing medically actionable information that can aid clinical decisions.

Social Jetlag and Chronotypes in the Chinese Population: Analysis of Data Recorded by Wearable Devices.

BACKGROUND: Chronotype is the propensity for a person to sleep at a particular time during 24 hours. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in Western countries but have never been described in China. OBJECTIVE: We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices. METHODS: We analyzed 71,176 anonymous Chinese people who were continuously recorded by wearable devices for at least one week between April and July in 2017. Chronotypes were assessed (N=49,573) by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate, and late chronotypes were defined by arbitrary cut-offs of MSFsc <3 hours, between 3-5 hours, and >5 hours. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/body mass index/MSFsc were assessed by Pearson correlation. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc. RESULTS: The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76% (13,266/49,573), 58.59% (29,045/49,573), and 14.64% (7257/49,573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772). No gender differences are found in chronotypes. We found that SJL follows a normal distribution and 17.07% (12,151/71,176) of Chinese have SJL longer than 1 hour. Nearly a third (22,442/71,176, 31.53%) of Chinese have SJL<0. The results showed that 53.72% (7127/13,266), 25.46% (7396/29,045), and 12.71% (922/7257) of the early, intermediate, and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001) but not with body mass index (r=0.004, P=.30). Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively). CONCLUSIONS: Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have less SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In the Chinese population, SJL is not associated with body mass index. People of later chronotypes and long sleepers suffer more from SJL.

[Relationship between systolic blood pressure load and sleep stability in patients with essential hypertension].

Objective: To explore the relationship between blood pressure load and sleep stability in hypertensive patients by cardiopulmonary coupling monitoring (CPC) and 24-hour ambulatory blood pressure monitoring. Methods: One hundred and fifty-five patients with essential hypertension were divided into high load group and low load group according to whether the blood pressure load was higher than 50%. The relationship between sleep stability and systolic blood pressure load was analyzed by logistic regression. Results: After adjusting for sex, age and body mass index (BMI), logistic regression analysis showed that stable sleep was the protective factor (OR=0.736,P=0.047) for the decrease of nocturnal blood pressure load, and unstable sleep was the risk factor for increasing day time blood pressure load (OR=1.336, P=0.037) in patients with hypertension. Conclusions: The effect of sleep stability on blood pressure load is different between day and night. Stable sleep is the protective factor of nocturnal systolic blood pressure load decrease, and unstable sleep is the harmful factor of daytime systolic blood pressure load increase. Increasing sleep stability helps to reduce systolic blood pressure load.

Ambulatory screening tool for sleep apnea: analyzing a single-lead electrocardiogram signal (ECG).

STUDY OBJECTIVE: The goal was to determine the utility and accuracy of automated analysis of single-lead electrocardiogram (ECG) data using two algorithms, cardiopulmonary coupling (CPC), and cyclic variation of heart rate (CVHR) to identify sleep apnea (SA). METHODS: The CPC-CVHR algorithms were applied to identify SA by analyzing ECG from diagnostic polysomnography (PSG) from 47 subjects. The studies were rescored according to updated AASM scoring rules, both manually by a certified technologist and using an FDA-approved automated scoring software, Somnolyzer (Philips Inc., Monroeville, PA). The CPC+CVHR output of Sleep Quality Index (SQI), Sleep Apnea Indicator (SAI), elevated low frequency coupling broadband (eLFCBB) and elevated low frequency coupling narrow-band (eLFCNB) were compared to the manual and automated scoring of apnea hypopnea index (AHI). RESULTS: A high degree of agreement was noted between the CPC-CVHR against both the manually rescored AHI and the computerized scored AHI to identify patients with moderate and severe sleep apnea (AHI > 15). The combined CPC+CVHR algorithms, when compared to the manually scored PSG output presents sensitivity 89%, specificity 79%, agreement 85%, PPV (positive predictive value) 0.86 and NPV (negative predictive value) 0.83, and substantial Kappa 0.70. Comparing the output of the automated scoring software to the manual scoring demonstrated sensitivity 93%, specificity 79%, agreement 87%, PPV 0.87, NPV 0.88, and substantial Kappa 0.74. CONCLUSION: The CPC+CVHR technology performed as accurately as the automated scoring software to identify patients with moderate to severe SA, demonstrating a clinically powerful tool that can be implemented in various clinical settings to identify patients at risk for SA. TRIAL REGISTRATION: NCT01234077.

[The association between blood pressure variability and sleep stability in essential hypertensive patients with sleep disorder].

Objective: To investigate the association of blood pressure variability and sleep stability in essential hypertensive patients with sleep disorder by cardiopulmonary coupling. Methods: Performed according to strict inclusion and exclusion criteria, 88 new cases of essential hypertension who came from the international department and the cardiology department of china-japan friendship hospital were enrolled. Sleep stability and 24 h ambulatory blood pressure data were collected by the portable sleep monitor based on cardiopulmonary coupling technique and 24 h ambulatory blood pressure monitor. Analysis the correlation of blood pressure variability and sleep stability. Results: In the nighttime, systolic blood pressure standard deviation, systolic blood pressure variation coefficient, the ratio of the systolic blood pressure minimum to the maximum, diastolic blood pressure standard deviation, diastolic blood pressure variation coefficient were positively correlated with unstable sleep duration (r=0.185, 0.24, 0.237, 0.43, 0.276, P<0.05). Conclusions: Blood pressure variability is associated with sleep stability, especially at night, the longer the unstable sleep duration, the greater the variability in night blood pressure.

[Diagnosis of obstructive sleep apnea using cardiopulmonary coupling analysis].

Objective: To explore the diagnostic value of cardiopulmonary coupling analysis in the screening of obstructive sleep apnea (OSA). Methods: A total of 44 participants with sleep disorders from Sleep Medicine Center of West China Hospital in May 2016 were included in the study. All participants were monitored with cardiopulmonary coupling while undergoing polysomnography (PSG). The sleep parameters detected by cardiopulmonary coupling and PSG were compared and the correlation between respiratory disturbance index (RDI) of cardiopulmonary coupling and sleep parameters of PSG were analyzed. Using PSG as "golden standard" , the sensitivity, specificity, positive predictive value, negative predictive value and the corresponding areas under the receiver operating characteristic (ROC) curves were calculated for cardiopulmonary coupling. Results: There were 44 participants included in the study (37 males and 7 females) with a mean age of (46.3±12.5) years old and a mean body mass index of (25.6±3.7) kg/m(2). And there were 33 subjects diagnosed with OSA. There were no significant difference between respiratory disturbance index (RDI) derived from cardiopulmonary coupling and apnea-hypopnea index (AHI) derived from PSG. Correlation analysis showed that the RDI, low frequency coupling monitored by cardiopulmonary coupling was positively correlated with AHI detected by PSG (r=0.849, 0.850, both P<0.001); and the high frequency coupling was significantly negatively correlated with AHI (r=-0.767, P<0.001). For AHI ≥5/h, ≥10/h, ≥15/h, ≥20/h and ≥30/h respectively according to PSG, sensitivity was 0.82, 0.93, 0.96, 0.96, 0.77, specificity was 0.50, 0.75 0.72, 0.80, 0.86, positive predictive value was 0.85, 0.87, 0.83, 0.85, 0.85, negative predictive value was 0.55, 0.86, 0.93, 0.94, 0.79 for cardiopulmonary coupling. The corresponding areas under the ROC curves were 0.868, 0.892, 0.915, 0.942, 0.921 respectively. In addition, when the RDI derived from CPC was higher than 20.4/h, the patients were more likely suffering from OSA, and the sensitivity was 0.79 and the specificity was 0.91. Conclusions: The results of cardiopulmonary coupling are consistent with simultaneous PSG parameters. Cardiopulmonary coupling has a highly diagnostic value for sleep respiratory disorder.

The effect of continuous positive airway pressure on cardiopulmonary coupling.

PURPOSE: The aim of the study was to investigate cardiopulmonary coupling (CPC) as a tool for evaluating the efficacy of continuous positive airway pressure (CPAP) in the treatment of obstructive sleep apnea (OSA). METHOD: The medical records of patients who had undergone either standard polysomnography (PSG-only group, n = 62) or split-night CPAP titration (CPAP titration group, n = 115) due to habitual snoring or sleep apnea were analyzed. RESULT: The apnea-hypopnea index, CPC variables, and sleep stage (%) during the first 2 h of the sleep study did not differ between the PSG-only and CPAP titration groups. A comparison of data collected over the first 2 h and that collected over the rest of the sleep study revealed that high-frequency coupling (HFC) increased, and low-frequency coupling (LFC) and elevated LFC (e-LFC) decreased only for the CPAP titration group, while they did not change for the PSG-only group. The proportions of N1 and N3 stages and arousal index decreased while the proportion of R stage increased for both groups. The proportion of N2 stage and mean O2 saturation increased only for the CPAP titration group, while they did not change significantly for the PSG-only group. HFC increased while LFC and e-LFC decreased linearly as apnea-hypopnea index decreased for the CPAP titration group. CONCLUSION: CPC effectively reflects the treatment effect of CPAP when compared with an electroencephalography-based sleep stage assessment.

Cardiopulmonary Coupling Analysis Using Home Sleep Monitoring System Based on Air Mattress.

This study investigates the feasibility of cardiopulmonary coupling (CPC) using home sleep monitoring system. We have designed a system to measure respiratory signals and normal-to-normal (NN) interval series in a non-contact based on air mattress. Then, CPC analysis was conducted using extracted respiratory signals and NN interval series, and six CPC parameters were extracted (VLFC, LFC, HFC, e-LFC, e-LFCNB and e-LFCBB). To evaluate the proposed method, two statistical analyses were conducted between the CPC parameters extracted by the electrocardiogram-based conventional method and the air mattress-based proposed method for five patients with obstructive sleep apnea and hypopnea (OSAH). Wilcoxon's signed rank test on the CPC parameters of the two methods indicated no significant differences (p > 0.05) and Spearman's rank correlation analysis showed high positive correlations (r ≥ 0.7, p < 0.05) between the two methods. Therefore, the proposed method has the potential for performing CPC analysis using air mattress-based system.

The value of right ventricular pulmonary artery coupling in determining the prognosis of patients with sepsis.

The outcomes of patients with sepsis are influenced by the contractile function of the right ventricle (RV), but the impact of cardiopulmonary interaction in ICU-mortality of sepsis patients remains unclear. This study aims to investigate the ICU-mortality impact of right ventricular-pulmonary artery (RV-PA) coupling in patients with sepsis. We employed echocardiography to assess patients with sepsis within the initial 24 h of their admission to the ICU. RV-PA coupling was evaluated using the tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP) ratio. A total of 92 subjects were enrolled, with 55 survivors and 37 non-survivors. TAPSE/PASP ratio assessed mortality with an area under the curve (AUC) of 0.766 (95% CI 0.670-0.862) and the optimal cutoff value was 0.495 mm/mmHg. We constructed a nomogram depicting the TAPSE/PASP in conjunction with IL-6 and Lac for the joint prediction of sepsis prognosis, and demonstrated the highest predictive capability (AUC = 0.878, 95% CI 0.809-0.948). In conclusion, the TAPSE/PASP ratio demonstrated prognostic value for ICU mortality in sepsis patients. The nomogram, which combines the TAPSE/PASP, IL-6, and LAC, demonstrated enhanced predictive efficacy for the prognosis of sepsis patients.

Screening of preoperative obstructive sleep apnea by cardiopulmonary coupling and its risk factors in patients with plans to receive surgery under general anesthesia: a cross-sectional study.

Objective: Preoperative obstructive sleep apnea (OSA) is supposed to be the abnormally high occurrence of OSA the night before surgery under general anesthesia. This study aimed to evaluate the prevalence preoperative OSA using cardiopulmonary coupling (CPC) and its correlation with imbalance of sympathetic/parasympathetic nervous system. Methods: A total of 550 patients with plans to receive surgery under general anesthesia were enrolled. All patients were assigned to wear CPC on the night before surgery until the next day. Sleep quality characteristics, heart rate variation parameters, and apnea-hypopnea index were acquired. The diagnosis of pre-existing OSA was not considered in the current study. Results: According to apnea-hypopnea index, 28.4%, 32.2%, 26.2%, and 13.3% patients were assessed as no, mild, moderate, and severe operative OSA, respectively. Multivariate logistic regression model revealed that higher age [p < 0.001, odds ratio (OR) = 1.043] was independently and positively associated with preoperative OSA; heart rate variation parameters representing the imbalance of sympathetic/parasympathetic nervous system, such as higher low-frequency (p < 0.001, OR = 1.004), higher low-frequency/high-frequency ratio (p = 0.028, OR = 1.738), lower NN20 count divided by the total number of all NN intervals (pNN20; p < 0.001, OR = 0.950), and lower high-frequency (p < 0.001, OR = 0.998), showed independent relationships with a higher probability of preoperative OSA. Higher age (p = 0.005, OR = 1.024), higher very-low-frequency (p < 0.001, OR = 1.001), and higher low-frequency/high-frequency ratio (p = 0.003, OR = 1.655) were associated with a higher probability of moderate-to-severe preoperative OSA, but higher pNN10 (p < 0.001, OR = 0.951) was associated with a lower probability of moderate-to-severe preoperative OSA. Conclusion: Preoperative OSA is prevalent. Higher age and imbalance of sympathetic/parasympathetic nervous system are independently and positively associated with a higher occurrence of preoperative OSA. CPC screening may promote the management of preoperative OSA.

Association of self-reported and objective sleep disturbance with the spectrum of gastroesophageal reflux disease.

STUDY OBJECTIVES: The relationship between obstructive sleep apnea (OSA) and gastroesophageal reflux disease (GERD) is complex. We aimed to determine the association of self-reported and objective sleep parameters with diverse manifestations of the GERD spectrum. METHODS: We prospectively recruited 561 individuals who underwent an electrocardiogram-based cardiopulmonary coupling for OSA screening during a health check-up. All participants received the Reflux Disease Questionnaire and an upper endoscopy to determine the presence of troublesome reflux symptoms and erosive esophagitis (EE). Sleep quality was evaluated by the Pittsburgh Sleep Quality Index and sleep dysfunction was defined as a Pittsburgh Sleep Quality Index score > 5. OSA was defined as a cardiopulmonary coupling-derived apnea-hypopnea index exceeding 15 events/h. Comparisons were made between participants on the GERD spectrum with respect to their various self-reported and objective sleep parameters. RESULTS: Among the 277 patients with GERD (49.4%), 198 (35.3%) had EE. Patients with GERD had higher PSQI scores (6.99 ± 3.97 vs 6.07 ± 3.73, P = .005) and a higher prevalence of sleep dysfunction (60.6% vs 49.6%, P = .009). Patients with EE had a higher prevalence of OSA (42.9% vs 33.9%, P = .034). Along the GERD spectrum, symptomatic patients with EE had the highest PSQI scores and prevalence of sleep dysfunction (70.7%), while asymptomatic patients with EE had the highest prevalence of OSA (44%). CONCLUSIONS: Our findings indicate a high prevalence of sleep dysfunction among individuals with GERD. Furthermore, patients on the GERD spectrum are prone to experiencing a range of self-reported and objective sleep disturbances. CITATION: Hu K-Y, Tseng P-H, Hsu W-C, et al. Association of self-reported and objective sleep disturbance with the spectrum of gastroesophageal reflux disease. J Clin Sleep Med. 2024;20(6):911-920.