Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review

Free AccessScientific InvestigationsAssociation of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review Mohammadreza Hajipour, MEng, PhD, Brett Baumann, MD, Ali Azarbarzin, PhD, A.J. Hirsch Allen, PhD, Yu Liu, PhD, Sidney Fels, PhD, Sebastian Goodfellow, PhD, Amrit Singh, PhD, Rachel Jen, MD, Najib T. Ayas, MD Mohammadreza Hajipour, MEng, PhD Department of Experimental Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada *Co-first authors Search for more papers by this author , Brett Baumann, MD Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada *Co-first authors Search for more papers by this author , Ali Azarbarzin, PhD Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts Search for more papers by this author , A.J. Hirsch Allen, PhD Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada Search for more papers by this author , Yu Liu, PhD Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada Department of Pharmacology, Shanxi Medical University, Taiyuan, China Search for more papers by this author , Sidney Fels, PhD Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, British Columbia, Canada Search for more papers by this author , Sebastian Goodfellow, PhD Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada Search for more papers by this author , Amrit Singh, PhD Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada Search for more papers by this author , Rachel Jen, MD Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada Search for more papers by this author , Najib T. Ayas, MD Address correspondence to: Dr. Najib T. Ayas, MD, Department of Medicine, Faculty of Medicine–University of British Columbia, 2775 Laurel Street, 7th Floor, Vancouver, BC V5Z 1M9, Canada; Email: E-mail Address: [email protected] Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada Search for more papers by this author Published Online:February 1, 2023https://doi.org/10.5664/jcsm.10298Cited by:1SectionsAbstractEpubPDFSupplemental Material ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations AboutABSTRACTStudy Objectives:Polysomnograms (PSGs) collect a plethora of physiologic signals across the night. However, few of these PSG data are incorporated into standard reports, and hence, ultimately, under-utilized in clinical decision making. Recently, there has been substantial interest regarding novel alternative PSG metrics that may help to predict obstructive sleep apnea (OSA)–related outcomes better than standard PSG metrics such as the apnea-hypopnea index. We systematically review the recent literature for studies that examined the use of alternative PSG metrics in the context of OSA and their association with health outcomes.Methods:We systematically searched EMBASE, MEDLINE, and the Cochrane Database of Systematic Reviews for studies published between 2000 and 2022 for those that reported alternative metrics derived from PSG in adults and related them to OSA-related outcomes.Results:Of the 186 initial studies identified by the original search, data from 31 studies were ultimately included in the final analysis. Numerous metrics were identified that were significantly related to a broad range of outcomes. We categorized the outcomes into 2 main subgroups: (1) cardiovascular/metabolic outcomes and mortality and (2) cognitive function– and vigilance-related outcomes. Four general categories of alternative metrics were identified based on signals analyzed: autonomic/hemodynamic metrics, electroencephalographic metrics, oximetric metrics, and respiratory event–related metrics.Conclusions:We have summarized the current landscape of literature for alternative PSG metrics relating to risk prediction in OSA. Although promising, further prospective observational studies are needed to verify findings from other cohorts, and to assess the clinical utility of these metrics.Citation:Hajipour M, Baumann B, Azarbarzin A, et al. Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review. J Clin Sleep Med. 2023;19(2):225–242.BRIEF SUMMARYCurrent Knowledge/Study Rationale: There is a need to identify which patients with obstructive sleep apnea (OSA) are at greater risk of adverse outcomes. Novel alternative metrics derived from the polysomnogram (PSG) may help to risk-stratify patients and provide a more nuanced description of their disease.Study Impact: In this systematic review, we have summarized the current landscape for alternative PSG metrics relating to risk prediction in OSA. We identified many alternative metrics that could be promising; these included autonomic/hemodynamic metrics, electroencephalogram-related metrics, oximetry metrics, and respiratory event–related metrics.INTRODUCTIONObstructive sleep apnea (OSA) is a common respiratory disease that affects approximately 1 billion adults worldwide.1 OSA is associated with multiple adverse outcomes, including daytime sleepiness, reduced quality of life, motor vehicle crashes, occupational injuries, hypertension, cancer, cardiovascular disease (CVD), arrhythmias, kidney disease, cognitive dysfunction (dementia), and all-cause mortality.2When OSA is suspected, patients often undergo a polysomnogram (PSG), an overnight sleep study in which a plethora of raw physiologic data are continuously collected including electroencephalography (EEG), electrocardiogram (ECG), oxygen saturation using photoplethysmography, airflow, snoring, chin/limb electromyography (EMG), eye movements, and chest wall/abdominal movements. These PSGs are scored visually by technicians to ascertain sleep stages and respiratory events. Since the 1990s, key metrics of OSA severity derived from the PSG include the apnea-hypopnea index (AHI) and simple indices of arterial desaturation such as the oxygen desaturation index (ODI) and percentage of time spent below an oxygen saturation threshold (eg, 88% or 90%).3However, current PSG metrics, such as AHI, are not strongly associated with OSA-related adverse outcomes including symptoms, objective daytime function, and long-term health complications. There has thus been substantial interest in alternative PSG metrics to better quantify the severity of OSA and predict the presence or incidence of adverse OSA-related outcomes.4 These types of metrics may thus provide the opportunity to risk-stratify patients for more aggressive therapy for OSA and other risk factors, contributing to a precision care approach. These metrics may help to select individuals who would be at increased risk of adverse outcomes (eg, cardiovascular [CV] events) who might then be preferentially recruited into randomized controlled trials.The objective of this study was to systematically review the recent literature for studies that examined the use of alternative PSG metrics in the context of OSA and their association with health outcomes. In this context, an alternative metric was defined as one not typically reported in clinical PSG reports.METHODSThis study was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.5Eligibility criteriaInclusion criteriaStudies were included in this review if they (1) referenced an alternative metric elicited from PSG (either attended level 1 or unattended level 2), (2) reported an outcome that was patient-centered (eg, symptoms, mortality, CV outcomes), and (3) were published in the English language as full papers (ie, not as abstracts).Exclusion criteriaStudies were excluded if they (1) referenced metrics that are typically reported from PSG (eg, AHI, rapid eye movement [REM]/non-REM [NREM] AHI, ODI, lowest oxygen saturation, arousal index, standard sleep architecture), (2) did not derive data from full PSG studies (eg, used level 3 studies or oximetry), (3) focused on pediatric patients, (4) focused on narrow populations (eg, spinal cord injuries, pregnant women, underlying lung disease), and (5) only compared the metric(s) to AHI without patient-specific outcomes. We also excluded studies that focused on advanced OSA physiologic endotyping (eg, arousal threshold, loop gain) as these are currently challenging to measure from PSG and were felt to be beyond the scope of this review.6,7 Moreover, we excluded studies that assessed the role of therapy (continuous positive airway pressure [CPAP]) on health outcomes.Search strategy and selection criteriaWe systematically searched EMBASE, MEDLINE, and the Cochrane Database of Systematic Reviews from January 1, 2000, to April 1, 2022, using a broad search strategy and included keywords such as “obstructive sleep apnea” AND “polysomnogram” with alternative metrics. Details of the search strategy are presented in Table S1 in the supplemental material. We limited the search to the above dates to reflect modern OSA diagnostic practices. Potentially relevant articles were accessed for full-text review. Citations from eligible articles were also searched to identify other potentially relevant studies. A flow chart of identified studies is presented in Figure 1.Figure 1: PRISMA flow chart of identified studies, excluded and included.PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-AnalysesDownload FigureThree authors (M.H., B.B., and N.T.A.) conducted the literature search and extracted the data. The search strategy and items for data extraction were predefined and agreed upon by the authors. Variables that were extracted from each study included the following: year of publication, country of study, data sources, the metric used, mean AHI, mean body mass index (BMI), sex breakdown, mean age, study endpoints, sample size, and major results.RESULTSStudy selectionA total of 387 articles were identified. After duplicates were removed, 186 papers remained. Of the 186 unique studies identified in our search, 100 qualified for full-text review, of which 31 were included in the final data extraction (see Figure 1). The extracted data from these 31 studies are described in Table 1, Table 2, Table 3, and Table 4. Of the 31 papers included in the final review, 12 were from the United States, 5 from Australia, and the remaining countries of origin varied with representation from Finland, France, Sweden, Switzerland, Singapore, Germany, Spain, China, and Saudi Arabia. The sample size ranged from 40 participants54 to 8,001 participants.33 Additionally, JBI (Joanna Briggs Institute) checklists for analytical cross-sectional and cohort studies were used to further evaluate the studies.8 All studies met the components of checklists, except for 1 study in which the authors did not control the analysis for the confounders.46Table 1 Studies assessing autonomic/hemodynamic metrics.StudyStudy Size/TypeType of PSG (Level 1 or 2)Sex (% male)Mean BMI (kg/m2)Mean AHI (/h)Mean Age (years)% OSA ParticipantsMetricOutcomeResultsThomas (2009)28 USA5247/Retro of Pros151.628.79.162.247.6CPC (e-LFCNB)CV outcomesElevated LFCNB, was associated with greater severity of sleep apnea and fragmented sleep. After adjustment for potential confounders, an independent association with prevalent hypertension and stroke was found.Magnusdottir (2020)29 USA241/Retro of Pros2733425.563100CPCNocturnal blood pressure response to CPAPCPC-derived sleep quality impacted 24-h MAP and MDP, as well as BP during wakeBlanchard (2021)20 France7205/Retro of Pros262.329226086.7PRVAFPRV indices were independent predictors of AF incidence.Azarbarzin (2021)9 USA5970/Retro of pros1MESA: 47.5 SHHS: 47.7MESA: 28.8 SHHS: 28.3MESA: 19.3 SHHS: 14.1MESA: 68.5 SHHS: 64.2MESA: 3.9 SHHS: 29ΔHRCV disease and all-cause mortalityIn MESA, HR was associated with NT-proBNP, coronary calcium, and Framingham risk, and in SHHS, individuals with a high ΔHR were at increased risk of nonfatal/fatal CVD and all-cause mortalityKwon (2021)15 USA1407/Retro of Pros247.528.819.568.4Not referencedPAT responseCV outcomesIncrease in average PAT response was associated with LV mass, CPB score, CAC prevalence and 18% higher risk of incident CVDAlomri (2022)45 Saudi Arabia75/Retro17033.2Not referenced41.184SBP derived from PATCognitive dysfunction (Austin Maze test)Nocturnal peaks in SBP and difference between resting and nocturnal peaks of SBP in OSA were associated with visuospatial dysfunction, even after controlling for age, smoking status, depressive symptoms, hypoxia, and sleep fragmentationTrzepizur (2022)22 France5358/Retro of Pros263.66302760100PRVMACEsPRV was not associated with MACEs.Blanchard (2021)21 France3597/Retro of Pros26328205885PRVRisk of stroke incidenceNighttime sympathetic/parasympathetic tone (PRV) was associated with stroke risk.Hirotsu (2020)16 Switzerland2162/Cross-sectional24926.2Not referenced57Not referencedPWAHypertension, diabetes, and CV eventIndependent association of PWA-drop features (lower frequency, longer duration, and greater area under the curve) with hypertension, diabetes, and CV eventsStrassberger (2021)17 Sweden358/Retro164301355100CRICV riskPulse wave analysis during sleep provides a powerful approach for cardiovascular risk assessment in addition to conventional sleep study parametersBerger (2022)23 Switzerland1784/Retro of Pros148.226Not referenced58Not referencedHRVCVDIn a fully adjusted model, AC, DC, and HRF were the only HRV metrics significantly associated with incident CVD eventsAC = acceleration capacity, AF = atrial fibrillation, AHI = apnea-hypopnea index, BMI = body mass index, BP = blood pressure, CPB = carotid plaque burden, CPAP = continuous positive airway pressure, CPC = cardiopulmonary coupling, CRI = Cardiac Risk Index, CV = cardiovascular, CVD = cardiovascular disease, DC = deceleration capacity, e-LFCNB = narrow-band elevated low frequency coupling, HR = heart rate, HRV = heart rate variability, LV = Left Ventricular, MACE = major adverse cardiovascular event, MDP = mean diastolic blood pressure, MESA = Multi-Ethnic Study of Atherosclerosis, M/F = male/female, NT-proBNP = N-terminal prohormone BNP, ODI = oxygen desaturation index, OSA = obstructive sleep apnea, PAT = pulse arrival time, Pros = prospective, PRV = pulse rate variability, PSG = polysomnography, PWA = pulse wave amplitude, Retro = retrospective, Retro of Pros = retrospective analysis of prospective study, SBP = systolic blood pressure, SHHS = Sleep Heart Health Study.Table 2 Studies assessing EEG metrics.StudyStudy Size/TypeType of PSG (Level 1 or 2)Sex (% male)Mean BMI (kg/m2)Mean AHI (/h)Mean Age (years)% OSA ParticipantsMetricOutcomeResultsSchwartz (2006)50 Germany100/Cross-sectional1Not referencedNot referencedNot referencedNot referencedNot referencedArousal durationESSLonger arousals correlated more strongly with ESS than the frequency or time attributable to the more numerous brief arousals.Vakulin (2016)49 Australia76/Cross-sectional18132.229.842.8100EEG power Spectrum analysisDriving simulator performanceAmong clinical and quantitative EEG variables, significant predictors of worse steering deviation were total EEG power during NREM and REM sleep, beta EEG power in NREM and delta EEG power in REM and sleep onset latency.Azarbarzin (2020)46 USA1378/Retro of Pros167.6302265100LR-ORPRisk of car crashCompared to the lowest quartile of sleep depth coherence, individuals in the highest quartile had a 62% lower risk of accident.Kim (2021)31 USA2055/Cross-sectional14614.828.768.4Not referencedORPHypertensionORP was not associated with blood pressure changesLechat (2021)32 Australia5084/Retro of Pros147.328.19.963Not referencedDelta EEG powerAll-cause mortalityDisrupted delta EEG power during sleep was associated with a 32% increased risk of all-cause mortality compared with no fragmentation.Djonlagic (2021)47 USA3819/Retro of Pros2MESA:48 MrOS: 100Not referencedNot referencedNot referencedNot referencedEEG MetricsCognitive performance MESA: DSCT, CASI, DSF, DSB MrOS: Trails B, 3MS, DVTCognitive performance was related to sleep across macro architecture and multiple spectral, spindle, SO and spindle–SO coupling domains. Associated metrics fell across at least three broad classes.Shahrbabaki (2021)33 Australia8001/Retro of ProsSHHS:1 MrOS:2 SOF:262.5MrOS:27.2 SHHS:28.3 SOF:27.7MrOS:20.1 SHHS:9.5 SOF:27.6MrOS:76.6 SHHS: 64 SOF:82.9Not referencedABMortality: all cause and CVIn women, AB was associated with all-cause mortality and CV mortality. In men, it was not clear (results were reverse in SHHS and MrOS)McCloy (2021)48 Australia190/Retro16136.528.556100SBIVigilanceSBI used to model sleep spindle characteristics to PVT indices and the proposed model were able to detect patients with vigilance markerDuce (2021)51 Australia65/Retro15531.726.153100Arousal durationCognitive outcomesPVT impaired group had more EEG arousals greater than 5 s, 7 s, and 15 s in duration.AB = arousal burden, AHI = apnea-hypopnea index, BMI = body mass index, CASI = Cognitive Abilities Screening Instrument, DSB = Digit Span Test (backward), DSCT = Digit Symbol Coding Test, DSF = Digit Span Test (forward), DVT = Digit Vigilance Test, EEG = electroencephalography, ESS = Epworth Sleepiness Scale, MESA = Multi-Ethnic Study of Atherosclerosis, MrOS = Osteoporotic Fractures in Men Study, NREM = non–rapid eye movement, ODI = oxygen desaturation index, ORP = odds ratio product, OSA = obstructive sleep apnea, Pros = prospective, PSG = polysomnography, PVT = Psychomotor Vigilance Test, RDI = respiratory disturbance index, REM = rapid eye movement, Retro = retrospective, Retro of Pros = retrospective analysis of prospective study, SBI = spindle burst characteristics, SD = standard deviation, SHHS = Sleep Heart Health Study, SO = slow oscillation, SOF = Study of Osteoporotic Fractures, SWA = slow-wave activity, 3MS = Mini-Mental State Examination.Table 3 Studies assessing oximetric metrics.StudyStudy Size/TypeType of PSG (Level 1 or 2)Sex (% male)Mean BMI (kg/m2)Mean AHI (/h)Mean Age (years)% OSA ParticipantsMetricOutcomeResultsAzarbarzin (2019)36 USA7854/Retro of ProsMrOS: 2 SHHS: 1MrOS: 100 SHHS: 45.3MrOS: 27.2 SHHS: 28.3MrOS: 15.7 SHHS: 17.1MrOS: 74.3 SHHS: 61.0MrOS: 39.8 SHHS: 26HBCVD mortality and all-cause mortalityIndividuals in the MrOS study with hypoxic burden in the highest 2 quintiles had hazard ratios of 1.81 and 2.73, respectively, compared with the first quintile for CV-related mortality. The group in the SHHS with HB in the highest quintile had a hazard ratio of 1.96 for CV-related mortality.Azarbarzin (2020)37 USA7534/Retro of ProsMrOS: 2 SHHS: 1MrOS: 100 SHHS: 45.3MrOS: 27.1 SHHS:28.3MrOS: 11.4 SHHS: 8.6MrOS: 76.2 SHHS: 63.6Not referencedHBHFThe sleep HB was associated with incident HF in men in 2 independent cohorts. Moreover, HB predicted incident HF in groups with both high and low AHI levels.Azarbarzin (2021)9 USA5970/Retro of Pros1MESA: 47.5 SHHS: 47.7MESA: 28.8 SHHS: 28.3MESA: 19.3 SHHS: 14.1MESA: 68.5 SHHS: 64.2MESA: 3.9 SHHS: 28HBCVD and all-cause mortalityRelationship between delta HR and fatal CVD or all-cause mortality was strengthened in patients with a high HB. They also noted no association between a high delta HR and fatal CVD or all-cause mortality in those with a low HB.Jackson (2021)38 USA1895/Retro of Pros246.328.8Not referenced68.2Not referencedHBChronic kidney diseaseASHB was associated with moderate-to-severe CKD. Black women in highest vs lowest quantile of ASHB also had a higher CKD prevalence.Kim (2021)31 USA2055/Cross-sectional146/5428.714.868.4Not referencedHBHypertensionHigher burden was associated with higher BP.Trzepizur (2022)22 France5358/Retro of Pros263.66/36.34302760200HBMACEHB was an independent predictor of incident CV events and death.Blanchard (2021)21 France3597/Retro of Pros26328205885HBRisk of stroke incidenceHB was associated with stroke risk in OSA patients.de Chazal (2021)39 Australia4686/Retro of Pros248/52Not referencedNot referenced>40Not referencedREDTACVD mortalityHazard ratios in adjusted Cox analysis for predicting cardiovascular death using REDTA are up to 1.90 in the third quantile.Wang (2020)41 China102/Cross-sectional167/3329.56310050.3ODRHypertensionODR was more strongly associated with elevation of BP and BPV in patients with severe OSAKwon (2021)42 USA2631/Retro of Pros2100/027.21876.4Not referencedLFCtCVD and all-cause mortalityLFCt was independently associated with both CV and all-cause mortality in older men with SDB, independent of both baseline CV burden and conventional SDB metrics.Kainulainen (2020)52 Finland743/Retro158.735.123.756.8100Desaturation severity, obstruction severity, respiratory event durationPVT reaction time and the number of lapsesDesaturation severity is significantly associated with increased risk of impaired PVT performance.Muraja-Murro (2013)43 Finland226/Retro1Not referenced29.319.554.638.8Desaturation severity, obstruction severity, respiratory event durationMortalityObstruction severity was the only parameter which was related statistically significantly to mortality in the severe OSA categoryAHI = apnea-hypopnea index, BMI = body mass index, BP = blood pressure, BPV = blood pressure variability, CKD = chronic kidney disease, CV = cardiovascular, CVD = cardiovascular disease, HB = hypoxic burden, HF = heart failure, HR = heart rate, LFCt = lung to finger circulation time, MACE = major adverse cardiovascular event, MESA = Multi-Ethnic Study of Atherosclerosis, MrOS = Osteoporotic Fractures in Men Study, ODI = oxygen desaturation index, ODR = oxygen desaturation rate, OSA = obstructive sleep apnea, Pros = prospective, PSG = polysomnography, PVT = Psychomotor Vigilance Test, REDTA = Respiratory Event Desaturation Transient Area, Retro = retrospective, Retro of Pros = retrospective analysis of prospective study, SD = standard deviation, SDB = sleep-disordered breathing, SHHS = Sleep Heart Health Study.Table 4 Studies assessing respiratory event–related metrics.StudyStudy Size/TypeType of PSG (Level 1 or 2)Sex (% male)Mean BMI (kg/m2)Mean AHI (/h)Mean Age (years)% OSA ParticipantsMetricOutcomeResultsGoh (2018)53 Singapore821/Retro167282848100Apneic and Hypopneic loadESSLinear regression analysis found age (P < .001), apnea load (P = .005), REM (P = .021) and stage 1 sleep duration (P = .042) were independent factors correlated to ESS.Butler (2019)44 USA5712/Retro of Pros14828.113.863.367Respiratory event durationAll-cause mortalityIndividuals with the shortest-duration events had a significantly increased hazard ratio for all-cause mortalityKim (2021)31 USA2055/Cross-sectional14628.714.868.4Not referencedDuty cycle and IFLHypertensionHigher duty cycle and IFL were associated with lower BPMediano (2007)54 Spain40/Retro1100326150100Apnea durationEDSLonger apnea duration can be a determinant of excessive daytime sleepiness in OSA patientsAHI = apnea hypopnea index, BMI = body mass index, BP = blood pressure, EDS = excessive daytime sleepiness, ESS = Epworth Sleepiness Scale, IFL = inspiratory flow limitation, ODI = oxygen desaturation index, OSA = obstructive sleep apnea, Pros = prospective, PSG = polysomnography, RDI = respiratory disturbance index, REM = rapid eye movement, Retro = Retrospective, Retro of Pros = retrospective analysis of prospective study, SD = standard deviation.Identification of alternative metricsOf the 31 studies included in the extraction, we categorized the outcomes into 2 main subgroups (ie, 1) CV/metabolic outcomes and mortality, 2) cognitive function– and vigilance-related outcomes). Metrics were also grouped into 4 categories based on the signals used from PSG (autonomic/hemodynamic metrics, EEG-related metrics, oximetry metrics, and respiratory event-related metrics: see Table 1, Table 2, Table 3, and Table 4). In each group of outcomes, we described each metric based on its category.Cardiovascular/metabolic outcomes and mortalityTwenty-six studies reported CV/metabolic outcomes and mortality in patients with OSA. From these studies, 11 investigated autonomic/hemodynamic metrics, 11 studies investigated oximetric metrics, and 5 studies reported EEG metrics and respiratory-related metrics.Autonomic/hemodynamic metrics: 11 studiesThese metrics were derived from pulse oximetry (photoplethysmography [PPG]) for estimating blood pressure (BP) changes and heart rate variability (HRV) or derived from ECG signals combined with respiratory data. Five unique CV-related metrics were described (see Table 1).Heart rate response to respiratory eventsElevated heart rate response to respiratory events (“ΔHR”) was recently introduced by Azarbarzin and colleagues.9 This metric was shown to be associated with deleterious CV outcomes in patients with OSA.9 The risk was exclusively observed in individuals with no excessive daytime sleepiness. Activation of sympathetic activity is associated with an increase in the heart rate, the magnitude of which may also be affected by the severity of respiratory events,10 the intensity of cortical arousal,11 and the responsiveness of the autonomic nervous system.12 Therefore, it is plausible that the OSA-specific heart rate response may reflect important aspects of the autonomic response to respiratory events, useful to predict CV and metabolic outcomes. The ΔHR was defined as the difference between a maximum pulse rate (derived from the oximetry signal) during a subject-specific search window and the minimum pulse rate during apneas and hypopneas. Individuals with OSA who demonstrated an elevated ΔHR were at increased risk of nonfatal and fatal CVD and all-cause mortality (hazard ratio [95% confidence interval] = 1.60 [1.28–2.00], 1.68 [1.22–2.30], and 1.29 [1.07–1.55], respectively) in the Sleep Heart Health Study (SHHS). Of note, this cohort predominantly included middle-aged or older individuals (mean age = 64.2 ± 11 years). Further studies are needed to prospectively replicate these findings in younger individuals.Pulse arrival timeAnother alternative metric extracted from PPG, pulse arrival time (PAT), has been a widely used surrogate of pulse transit time, and has been used to estimate BP.13 Arterial stiffening from increased BP leads to a rise in pulse wave velocity and a fall in PAT.13 Since nocturnal sleep BP is an important prognostic marker of CV health, PAT assessment during sleep may provide useful information related to CV health in patients with OSA. PAT is calculated from the time interval between ECG R-waves and the pulse wave detected by pulse oximetry. An increase in PAT is indicative of an increase in BP.14 Kwon and colleagues15 calculated the PAT response to respiratory events using the area under the PAT waveform (first derivate of PAT) following respiratory events. Cross-sectional analyses revealed that higher PAT response was associated with higher left ventricular mass (5.7 g/m2 higher in the fourth compared with the first quartile, P < .007) and a higher carotid plaque burden score (0.37 higher in the fourth compared with the first quartile, P = .02). A nonsignificant association with greater odds of coronary artery calcification was also observed (P = .06). Finally, they showed that a 1-standard-deviation increase in average PAT response was associated with 18% higher risk of incident CVD. While these findings may help better identify high-risk individuals with OSA, further studies are needed to prospectively confirm them in younger individuals. Furthermore, the calculation of PAT requires more sophisticated signal processing techniques that may hinder its utility in routine clinical care.Pulse wave characteristicsIn a study in 2020, Hirotsu and colleagues16 examined the clinical significance of pulse wave amplitude (PWA) drops, extracted from PPG signal during sleep, as a biomarker for cardiometabolic disorders. The amplitude of the PPG signal was considered as a surrogate of the PWA. PWA variations seem to be proportional to the sympathetic outflow directed to the vessels, reflecting sympathovagal balance. Thus, they hypothesized that PWA-drop features during sleep would be independently associated with hypertension, diabetes, and previous CV events as these conditions are associated with impairments in the autonomic nervous system and vascular function. After preprocessing of PPG signal and derivation of peaks and nadirs, time course and first derivate of PWA-variation were extracted. PWA-drops with an amplitude > 30% and a duration > 4 heart beats, their frequency, duration, and area under the curve (AUC) were calculated. They showed that lower PWA-drop index, longer duration, and greater AUC were associated with increased risk of hypertension, diabetes, or CV events. Participants in the lowest quartile compared with those in the highest quartile of mean duration-normalized PWA-drop index had a significantly higher odds ratio (OR) of hypertension (OR = 1.60 [1.19–2.16]), CV event (OR = 3.26 [1.33–8.03]), and diabetes (OR = 1.71 [1.06–2.76]). Similar results were reported for mean duration- and mean AUC-normalized PWA-drop indexes. In another study by Strassberger and colleagues,17 a novel cardiac risk index (CRI) was computed based on pulse wave signals derived from pulse oximetry, reflecting vascular stiffness, cardiac variability, vascular autonomic tone, and nocturnal hypoxia. CRI calculated using an algorithm that computed 9 parameters (pulse index, oxygen saturation [SpO2] index, PWA index, respiratory-related pulse oscillations, pulse propagation time, periodic and symmetric de