Beyond the AHI-pulse wave analysis during sleep for recognition of cardiovascular risk in sleep apnea patients.

Recent evidence supports the use of pulse wave analysis during sleep for assessing functional aspects of the cardiovascular system. The current study compared the influence of pulse wave and sleep study-derived parameters on cardiovascular risk assessment. In a multi-centric study design, 358 sleep apnea patients (age 55 ± 13 years, 64% male, body mass index 30 ± 6 kg m-2 , apnea-hypopnea index 13 [5-26] events per hr) underwent a standard overnight sleep recording. A novel cardiac risk index was computed based on pulse wave signals derived from pulse oximetry, reflecting vascular stiffness, cardiac variability, vascular autonomic tone and nocturnal hypoxia. Cardiovascular risk was determined using the ESC/ESH cardiovascular risk matrix, and categorized to high/low added cardiovascular risk. Comparisons between cardiac risk index and sleep parameters were performed for cardiovascular risk prediction. Apnea-hypopnea index, oxygen desaturation index and cardiac risk index were associated with high cardiovascular risk after adjustment for confounders (p = .002, .001, < .001, respectively). In a nested reference model consisting of age, gender and body mass index, adding cardiac risk index but not apnea-hypopnea index or oxygen desaturation index significantly increased the area under the receiver operating characteristic curve (p = .012, .22 and .16, respectively). In a direct comparison of oxygen desaturation index and cardiac risk index, only the novel risk index had an independent effect on cardiovascular risk prediction (pCRI  < .001, pODI  = .71). These results emphasize the association between nocturnal pulse wave and overall cardiovascular risk determined by an established risk matrix. Thus, pulse wave analysis during sleep provides a powerful approach for cardiovascular risk assessment in addition to conventional sleep study parameters.

Overnight pulse wave analysis to assess autonomic changes during sleep in insomnia patients and healthy sleepers.

Insomnia has been associated with increased cardiovascular (CV) risk, which may be linked to sympathetic activation. Non-invasive overnight pulse wave analysis may be a useful tool to detect early signs of autonomic changes during sleep in insomniacs. Fifty-two participants (26 men, 37±13 years, BMI: 24±5 kg/m2, 26 insomniacs/ 26 controls) underwent overnight polysomnography with pulse oximetry and pulse wave analysis including pulse rate, vascular stiffness (pulse propagation time, PPT), and a composite cardiac risk index based on autonomic function and overnight hypoxia. We identified two subgroups of insomniacs, with and without objectively disturbed sleep (sleep efficiency SE≤80%, n = 14 vs. SE>80%, n = 12), and observed increased pulse rate and vascular stiffness in insomnia cases when diagnosis was based on both, subjective and objective criteria. Both insomnia groups were associated with higher overnight pulse rate than controls (median/ IQR: low-SE (low sleep efficiency): 67/ 58-70bpm; high-SE: 66/ 63-69bpm; controls: 58/ 52-63bpm; p = 0.01). Vascular stiffness was higher (reduction of PPT) in low-SE insomniacs compared with high-SE insomniacs and controls (169/ 147-232ms; 237/ 215-254ms; 244/ 180-284ms; p = 0.01). The cardiac risk index was increased in low-SE insomniacs (0.2/ 0.0-0.7; 0.0/ 0.0-0.4; 0.0/ 0.0-0.3; p = 0.05). Our results suggest a hyperarousal state in young and otherwise healthy insomniacs during sleep. The increased pulse rate and vascular stiffness in insomniacs with low SE suggest early signs of rigid vessels and potentially, an elevated CV risk. Overnight pulse wave analysis may be feasible for CV risk assessment in insomniacs and may provide a useful tool for phenotyping insomnia in order to provide individualized therapy.

REM Sleep Imposes a Vascular Load in COPD Patients Independent of Sleep Apnea.

Arterial stiffness, a marker for cardiovascular risk, is increased in patients with Chronic Obstructive Pulmonary Disease (COPD) and Obstructive Sleep Apnea (OSA). The specific influence of both on arterial stiffness during sleep is unknown. Nocturnal arterial stiffness (Pulse Propagation Time (PPT) of the finger pulse wave) was calculated in 142 individuals evaluated for sleep apnea: 27 COPD patients (64.7 ± 11y, 31.2 ± 8 kg/m2), 72 patients with cardiovascular disease (CVD group, 58.7 ± 13y, 33.6 ± 6 kg/m2) and 43 healthy controls (HC group 49.3 ± 12y, 27.6 ± 3 kg/m2). Sleep stage related PPT changes were assessed in a subsample of COPD patients and matched controls (n = 12/12). Arterial stiffness during sleep was increased in COPD patients (i.e. shortened PPT) compared to healthy controls (158.2 ± 31 vs. 173.2 ± 38 ms, p = 0.075) and to patients with CVD (161.4 ± 41 ms). Arterial stiffening was particular strong during REM sleep (145.9 ± 28 vs. 172.4 ± 43 ms, COPD vs. HC, p = 0.003). In COPD, time SaO2 < 90% was associated with reduced arterial stiffness (Beta +1.7 ms (1.1-2.3)/10 min, p < 0.001). Sleep apnea did not affect PPT. In COPD, but not in matched controls, arterial stiffness increased from wakefulness to REM-sleep (ΔPPT-8.9 ± 10% in COPD and 3.7 ± 12% in matched controls, p = 0.021). Moreover, REM-sleep related arterial stiffening was correlated with elevated daytime blood pressure (r = -0.92, p < 0.001) and increased myocardial oxygen consumption (r = -0.88, p < 0.01). Hypoxia and REM sleep modulate arterial stiffness. In contrast to healthy controls, REM sleep imposes a vascular load in COPD patients independent of sleep apnea indices, intermittent and sustained hypoxia. The link between REM-sleep, vascular stiffness and daytime cardiovascular function suggests that REM-sleep plays a role for increased cardiovascular morbidity of COPD patients.

Vascular stiffness determined from a nocturnal digital pulse wave signal: association with sleep, sleep-disordered breathing, and hypertension.

OBJECTIVES: Reflection of the finger pulse wave form is a valid measure of arterial stiffness, which may be continuously assessed during sleep. We investigated the relationships between sleep, sleep-disordered breathing, hypertension, and pulse propagation time (PPT) in patients with suspected sleep apnea. METHODS: The digital photoplethysmographic signal derived from finger pulse oximetry was recorded during overnight sleep studies in 440 patients (64% men, age 55 ±â€Š12 years, BMI 30 ±â€Š6 kg/m, apnea-hypopnea index 19 ±â€Š19 n/h). PPT, defined as the time interval between the systolic and diastolic peak of the finger pulse wave, was calculated. The influence of sleep stages on PPT were assessed in patients undergoing polysomnography. Generalized linear models were used to study predictors of PPT and hypertension. RESULTS: Mean overnight PPT was independently associated with age (ß = -1.34, P < 0.001), height (ß = 0.47, P = 0.047), history of smoking (ß = -9.44, P = 0.005), and apnea-hypopnea index (ß = -0.18, P = 0.043). PPT was shorter in hypertensive patients compared with normotensive patients (160 ±â€Š33 vs. 177 ±â€Š47 ms, P < 0.001) and independently associated with a diagnosis of hypertension (P = 0.043). PPT was influenced by sleep stage (highest PPT during slow wave sleep compared with wake and all other sleep stages, all P < 0.001) and varied across sleep apnea severity groups in normotensive but not in hypertensive patients (P = 0.028 and 0.64, respectively). CONCLUSION: Overnight PPT by oximetry was strongly associated with factors known to determine daytime vascular stiffness. In addition, PTT provides information on functional and structural vascular properties during sleep. This novel technique offers new opportunities to noninvasively monitor vascular function during the sleeping period.

Parameters of Overnight Pulse Wave under Treatment in Obstructive Sleep Apnea.

BACKGROUND: Sleep-related breathing disorders may promote cardiovascular (CV) diseases. A novel and differentiated approach to overnight photoplethysmographic pulse wave analysis, which includes risk assessment and measurement of various pulse wave characteristics, has been evaluated in obstructive sleep apnea (OSA). OBJECTIVES: The purpose of this study was to assess if and which of the differentiated pulse wave characteristics might be influenced by OSA treatment with positive airway pressure (PAP). METHODS: The study included two protocols. In the case-control study (group A), pulse wave-derived CV risk indices recorded during PAP therapy were compared with those obtained in age, body mass index, and CV risk class-matched patients with untreated OSA (n = 67/67). In the prospective PAP treatment study (group B), 17 unselected patients undergoing a full-night sleep test at baseline and after 23 ± 19 weeks of treatment were analyzed. RESULTS: In untreated OSA patients (group A), the overnight hypoxic load was increased (SpO2 index 38.7 ± 17.5 vs. 24.0 ± 11.1, p < 0.001) and the pulse wave attenuation index (PWA-I) was lower (29.4 ± 9.2 vs. 33.5 ± 11.8, p = 0.022) than in treated patients. In group B, PAP therapy reduced the hypoxic load and increased the PWA-I significantly. The composite CV risk index was slightly but not significantly reduced. CONCLUSIONS: PAP therapy modified the hypoxic load and pulse wave-derived markers. The PWA-I - associated with sympathetic vascular tone - was most prominently modified by PAP. This novel approach to markers of CV function should be further evaluated in prospective studies.

Detection of cardiovascular risk from a photoplethysmographic signal using a matching pursuit algorithm.

Cardiovascular disease is the main cause of death in Europe, and early detection of increased cardiovascular risk (CR) is of clinical importance. Pulse wave analysis based on pulse oximetry has proven useful for the recognition of increased CR. The current study provides a detailed description of the pulse wave analysis technology and its clinical application. A novel matching pursuit-based feature extraction algorithm was applied for signal decomposition of the overnight photoplethysmographic pulse wave signals obtained by a single-pulse oximeter sensor. The algorithm computes nine parameters (pulse index, SpO2 index, pulse wave amplitude index, respiratory-related pulse oscillations, pulse propagation time, periodic and symmetric desaturations, time under 90 % SpO2, difference between pulse and SpO2 index, and arrhythmia). The technology was applied in 631 patients referred for a sleep study with suspected sleep apnea. The technical failure rate was 1.4 %. Anthropometric data like age and BMI correlated significantly with measures of vascular stiffness and pulse rate variability (PPT and age r = -0.54, p < 0.001, PR and age r = -0.36, p < 0.01). The composite biosignal risk score showed a dose-response relationship with the number of CR factors (p < 0.001) and was further elevated in patients with sleep apnea (AHI ≥ 15n/h; p < 0.001). The developed algorithm extracts meaningful parameters indicative of cardiorespiratory and autonomic nervous system function and dysfunction in patients suspected of SDB.

The use of overnight pulse wave analysis for recognition of cardiovascular risk factors and risk: a multicentric evaluation.

OBJECTIVES: Conventional methods for cardiovascular disease risk stratification are based on quantification of recognized risk factors or assessment of biomarkers during the wake period. We evaluated an algorithm on the basis of a photoplethysmographic pulse wave recording during sleep for cardiovascular risk assessment. METHODS: Five hundred and twenty individuals (346 men, age 55.0 ± 13.4 years, BMI 29.9 ± 6.  kg/m) with suspected sleep apnoea were randomly recruited at five sleep centres. Individual cardiovascular risk scores were calculated in accordance with established cardiovascular risk matrixes (ESH/ESC, Framingham, SCORE, PROCAM scores). A digital photoplethysmographic pulse wave signal was continuously recorded during the night using an oximeter sensor. An algorithm based on eight separate hypoxic and pulse wave derived parameters was trained in 130 individuals and validated in 390 individuals for low/high cardiovascular risk classification. RESULTS: All derived parameters were associated with elevated ESH/ESC risk in univariate analysis and five in the multiple logistic regression model [discrimination index C = 0.8, Chi-square (7) = 69, P < 0.0001]. The combined algorithm detected high-risk patients (validation set, ESH/ESC risk classes 4 and 5) with a sensitivity, specificity, positive predictive value and negative predictive value of 74.5, 76.4, 69.0 and 81.0%, respectively. Significant associations were also found for the Framingham, SCORE and PROCAM scores. The computed risk scores in individuals with/without (n = 34/356) a previous history of cardiovascular event (myocardial infarction, transitory ischemic attack or stroke) were 0.71 ± 0.27 and 0.42 ± 0.34 (P < 0.001), respectively. CONCLUSION: Parameters derived from modified pulse oximetry during sleep may provide information on cardiovascular function. Combined signal analysis may be used for recognition of individuals with established cardiovascular risk in a sleep laboratory cohort.

Detection of sleep disordered breathing and its central/obstructive character using nasal cannula and finger pulse oximeter.

STUDY OBJECTIVE: To assess the accuracy of novel algorithms using an oximeter-based finger plethysmographic signal in combination with a nasal cannula for the detection and differentiation of central and obstructive apneas. The validity of single pulse oximetry to detect respiratory disturbance events was also studied. METHODS: Patients recruited from four sleep laboratories underwent an ambulatory overnight cardiorespiratory polygraphy recording. The nasal flow and photoplethysmographic signals of the recording were analyzed by automated algorithms. The apnea hypopnea index (AHI(auto)) was calculated using both signals, and a respiratory disturbance index (RDI(auto)) was calculated from photoplethysmography alone. Apnea events were classified into obstructive and central types using the oximeter derived pulse wave signal and compared with manual scoring. RESULTS: Sixty-six subjects (42 males, age 54 ± 14 yrs, body mass index 28.5 ± 5.9 kg/m(2)) were included in the analysis. AHI(manual) (19.4 ± 18.5 events/h) correlated highly significantly with AHI(auto) (19.9 ± 16.5 events/h) and RDI(auto) (20.4 ± 17.2 events/h); the correlation coefficients were r = 0.94 and 0.95, respectively (p < 0.001) with a mean difference of -0.5 ± 6.6 and -1.0 ± 6.1 events/h. The automatic analysis of AHI(auto) and RDI(auto) detected sleep apnea (cutoff AHI(manual) ≥ 15 events/h) with a sensitivity/specificity of 0.90/0.97 and 0.86/0.94, respectively. The automated obstructive/central apnea indices correlated closely with manually scoring (r = 0.87 and 0.95, p < 0.001) with mean difference of -4.3 ± 7.9 and 0.3 ± 1.5 events/h, respectively. CONCLUSIONS: Automatic analysis based on routine pulse oximetry alone may be used to detect sleep disordered breathing with accuracy. In addition, the combination of photoplethysmographic signals with a nasal flow signal provides an accurate distinction between obstructive and central apneic events during sleep.

Oximeter-based autonomic state indicator algorithm for cardiovascular risk assessment.

BACKGROUND: Cardiovascular (CV) risk assessment is important in clinical practice. An autonomic state indicator (ASI) algorithm based on pulse oximetry was developed and validated for CV risk assessment. METHODS: One hundred forty-eight sleep clinic patients (98 men, mean age 50 ± 13 years) underwent an overnight study using a novel photoplethysmographic sensor. CV risk was classified according to the European Society of Hypertension/European Society of Cardiology (ESH/ESC) risk factor matrix. Five signal components reflecting cardiac and vascular activity (pulse wave attenuation, pulse rate acceleration, pulse propagation time, respiration-related pulse oscillation, and oxygen desaturation) extracted from 99 randomly selected subjects were used to train the classification algorithm. The capacity of the algorithm for CV risk prediction was validated in 49 additional patients. RESULTS: Each signal component contributed independently to CV risk prediction. The sensitivity and specificity of the algorithm to distinguish high/low CV risk in the validation group were 80% and 77%, respectively. The area under the receiver operating characteristic curve for high CV risk classification was 0.84. ß-Blocker treatment was identified as an important factor for classification that was not in line with the ESH/ESC reference matrix. CONCLUSIONS: Signals derived from overnight oximetry recording provide a novel potential tool for CV risk classification. Prospective studies are warranted to establish the value of the ASI algorithm for prediction of outcome in CV disease.