Hypoxic Burden Calculation in Patients with Obstructive Sleep Apnea Diagnosed by Peripheral Arterial Tonometry: Diagnostic Accuracy and Clinical Implications

Hypoxic Burden Calculation in Patients with Obstructive Sleep Apnea Diagnosed by Peripheral Arterial Tonometry: Diagnostic Accuracy and Clinical Implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hypoxic Burden Calculation in Patients with Obstructive Sleep Apnea Diagnosed by Peripheral Arterial Tonometry: Diagnostic Accuracy and Clinical Implications Diego Ignacio Vanegas, Andrés Felipe Blanco, Fernando Adolfo Vanegas, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8282627/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in Sleep Science and Practice → Version 1 posted 9 You are reading this latest preprint version Abstract Background: The apnea–hypopnea index (AHI3%) is the conventional metric for grading obstructive sleep apnea (OSA) severity but does not capture the cumulative depth and duration of oxygen desaturation. Hypoxic burden (HB), which integrates desaturation area per hour, may better reflect physiologic load. The diagnostic performance and clinical utility of HB derived from peripheral arterial tonometry home sleep apnea testing (PAT-HSAT) remain uncertain. Methods: We performed a single-center retrospective diagnostic study of consecutive adults undergoing PAT-HSAT (WatchPAT®) between 2016 and 2025. After prespecified exclusions, 1,171 patients with pAHI3% ≥5 events/h were analyzed. HB (%·min/h) was computed from ODI4%-defined, event-linked desaturations and normalized by total sleep time. Diagnostic accuracy was evaluated for moderate-to-severe (pAHI3% ≥15) and severe OSA (pAHI3% ≥30) using ROC curves, Youden-derived thresholds, and standard performance metrics. HB distributions were cross-tabulated against AASM severity categories to assess physiologic heterogeneity. Results: HB demonstrated good discrimination for pAHI3% ≥15 (AUC 0.867, 95% CI 0.846–0.888) and strong discrimination for pAHI3% ≥30 (AUC 0.897, 95% CI 0.879–0.916). A threshold of ≥16.6 %·min/h identified moderate-to-severe OSA with 90.1% sensitivity and 58.7% specificity. Substantial heterogeneity was observed within pAHI3% strata: 30% of mild, 49.2% of moderate, and 26.6% of severe OSA patients had HB values discordant with their AASM category. Overall, 35.4% of the cohort were physiologically “reclassified” by HB. Conclusion: HB derived from PAT-HSAT provides good-to-excellent diagnostic discrimination, supports rule-out for moderate-to-severe OSA and rule-in for severe OSA, and identifies clinically relevant heterogeneity within AASM categories. Incorporating HB into routine PAT-HSAT reporting may refine risk stratification and guide personalized management. External validation with PSG and clinical outcomes is warranted. Clinical Trial Registration: Not applicable. hypoxic burden obstructive sleep apnea peripheral arterial tonometry home sleep apnea testing diagnostic accuracy ROC analysis Youden index physiologic reclassification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Brief Summary Current Knowledge/Study Rationale: The AHI3% is the standard metric for grading OSA severity but does not reflect the cumulative depth and duration of oxygen desaturation. Hypoxic burden integrates desaturation area per hour and may better represent physiologic load. The clinical value of HB when derived from PAT-HSAT remains unclear. Study Impact: HB derived from PAT-HSAT demonstrated good-to-excellent discrimination for moderate-to-severe and severe OSA and physiologically reclassified 35.4% of patients within AASM categories. Thresholds near 16.6 and 29.5 %·min/h support rule-out and rule-in strategies. Routine integration of HB into PAT-HSAT reports may improve clinical risk stratification and guide personalized treatment. Background The severity of OSA has conventionally been quantified using the apnea–hypopnea index with the ≥3% desaturation criterion (AHI3%), as endorsed by the AASM [1,2]. Under this framework, apneas are defined by a reduction in ventilatory flow exceeding 90%, whereas hypopneas represent reductions between 30% and 90%, both sustained for at least 10 seconds. For hypopneas, an oxygen desaturation ≥3% or ≥4%, with or without cortical arousal, is additionally required [3,4]. The AHI3% has been extensively employed to guide therapeutic decisions [5] and to estimate both all-cause and cardiovascular mortality risk [6,7]. Nevertheless, accumulating evidence indicates that AHI3% alone may misclassify OSA severity in specific patient subgroups, given its inability to capture the cumulative depth and duration of hypoxic episodes [1,8]. This limitation has catalysed interest in novel metrics capable of providing a more physiologically integrated assessment of nocturnal hypoxaemia. In this context, the HB has recently emerged as a novel metric capturing the total area (duration × depth) of desaturation events per hour of sleep, thereby providing a more physiologically comprehensive measure of hypoxaemic stress and demonstrating prognostic significance for survival [9]. Expressed as %·min/h, this parameter quantifies the cumulative area of oxygen desaturations (specifically those associated with apnea and hypopnea events) indexed per hour of effective sleep. The PAT, a type III HSAT modality, is considered technically appropriate by the AASM and other professional societies for the evaluation of patients with suspected OSA [3,10]. The PAT-HSAT automatically classifies events as apneas or hypopneas using a surrogate for airflow and respiratory effort derived from the PAT signals. Classification occurs when oxygen desaturations ≥3% or ≥4% coincide with a >30% reduction in the amplitude of the raw PAT signal (reflecting arterial tone), together with a peri-critical increase of >10% in HR [11]. In addition, PAT-HSAT classifies as apnea or hypopnea any event with oxygen desaturations ≥4%, even when the full autonomic response described above is not completely met. All events must also satisfy a minimum duration of 10 seconds to be considered diagnostically valid. Therefore, this study aimed to evaluate the diagnostic performance of HB in patients with OSA previously diagnosed using the desaturation-based criterion (pAHI3%) derived from PAT-HSAT. Specifically, we sought to quantify the cumulative HB linked to respiratory events, characterise its distribution across standard AASM OSA severity categories, and determine its incremental utility in enhancing risk stratification beyond the conventional AHI3% based classification. Methods Study Design and Population: We performed a retrospective diagnostic accuracy study to assess HB in patients with OSA, using pAHI3% as the internal reference. The cohort included consecutive adults (≥18 years) who underwent PAT-HSAT at Fundarritmia, Fundación Cardiovascular, Bogotá, Colombia (Jan 2016–Aug 2025). Of 1,455 recordings, we excluded studies with non-diagnostic indications, missing or poor-quality signals, inconclusive interpretation, absent positional data, pacemakers, or TST <240 min. The final sample comprised 1,171 patients with confirmed pAHI3% ≥5 events/h. A STARD-compliant flow diagram is provided (Figure 1). This approach ensured a real-world cohort with standardized acquisition and data quality. A priori sample size estimation was performed using standard diagnostic accuracy formulas; however, given the retrospective design and fixed sampling frame, all consecutive eligible PAT-HSAT studies were included, resulting in a larger and more statistically precise cohort. Clinical and Anthropometric Assessment: We administered the STOP-BANG questionnaire, categorizing scores as low (0–2), intermediate (3–4), or high risk (5–8)[12], and the ESS to classify daytime sleepiness as normal (0–5), mild (6–10), moderate (11–15), or severe (16–24)[13]. Demographic and anthropometric data (age, sex, neck circumference, BMI) were systematically collected, with BMI classified per WHO criteria [14]. These variables ensured clinical context for interpreting pAHI3%-based severity and HB across risk profiles, anthropometric phenotypes, and disease severity. AASM OSA Classification Criteria: OSA severity followed AASM OSA criteria using AHI3%: mild (5–14.9 events/h), moderate (15–29.9), and severe (≥30)[10]. Reference Standard: The diagnostic reference standard was pAHI3%, obtained from the WatchPAT® system (Itamar Medical Ltd, Caesarea, Israel) using identical hardware and uniform oximetry specifications. Respiratory events (RE) were scored automatically with the zzzPAT algorithm, detecting apneas/hypopneas via oxygen desaturations (>3%) and autonomic arousals (>10% HR increase, >30% PAT amplitude drop). Events with desaturations >4% were also captured independently of complete autonomic responses [11]. All studies underwent two-stage quality control: technical verification by two trained technicians and independent review by a sleep specialist. Home Sleep Apnea Testing with Peripheral Arterial Tonometry (PAT-HSAT): The PAT-HSAT system combines a pneumo-optic finger probe with medical-grade oximetry and PAT sensors, plus actigraphy, body position, respiratory movement, and snore channels. Operating on a beat-to-beat basis (≥8–10 Hz before filtering), it ensures high temporal resolution for detecting desaturation events and vascular tone changes [11, 15–17]. WatchPAT® algorithms, validated against in-lab PSG, are endorsed by AASM and other societies as technically adequate for patients with moderate-to-high OSA risk [10,11,15,18–21]. Calculation of HB: HB was computed from encrypted binary files containing second-by-second SatO₂ data. Signals were segmented into 1-s epochs annotated for sleep stage, snoring, RE, and quality-control flags; epochs with artefact, interference, or wakefulness were excluded. RE were defined as a >30% reduction in PAT amplitude accompanied by a ≥10% rise in HR and a ≥3% fall in SatO₂ (Figure 2). For each RE (apnea or hypoapnea), the desaturation area was estimated using a triangular approximation (½ × ΔSatO₂ × duration), summed across all events, and normalised to TST to yield HB, expressed as %·min/h [9,22]. Positional HB was derived by partitioning RE according to body position (supine, lateral, prone). For the institutional implementation used in this study, the HB algorithm operated specifically on events with desaturations ≥4%, thereby anchoring HB to more pronounced and clinically meaningful oxygen. By selecting this strategy, the algorithm relies on the fully automated identification of these events by zzzPAT, without the need for manual validation. Unlike 3% desaturation (which frequently requires manual confirmation in clinical scoring systems such as COMPAS)[23], 4% drops represent a more robust and consistently detected oximetric signal that is less dependent on scorer-based interpretation. In this study, the proprietary HypoxicBurden.exe software was used exclusively, operating solely on the encrypted SatO₂ channel linked to RE in CSV file. It is important to note that the institutional algorithm used in this study was developed and implemented before the release of the most recent WatchPAT/Zoll-Itamar software update that includes an automated HB output. Systematic manual editing based on COMPASS [23] and AASM Manual Scoring rules [3] primarily removed RE associated with 3% desaturations that were automatically flagged by zzzPAT but did not fulfill scoring criteria. This step was fundamental to obtain a refined pAHI3% calculation and to prevent artificial inflation driven by spurious 3% events. Accordingly, the final pAHI3% computation was always performed after manual editing, which constituted a strict prerequisite for all analyses. For reproducibility, both an event-based HB algorithm and a second-by-second (continuous) HB algorithm were documented. However, all primary and secondary analyses in this study used the event-based HB definition only (triangular desaturation area per RE, normalised by TST). The continuous HB algorithm was not applied to the dataset and is not reported among the results. Full algorithmic and quality-control procedures are detailed in Additional file 1: Supplementary Methods Statistical Analysis: Analyses were conducted in Stata 19.0 (StataCorp, College Station, TX). The index test was the event-based HB definition. Continuous variables were summarised as medians (IQR) and categorical variables as counts (%). Baseline characteristics were reported for the entire cohort and stratified by OSA severity (pAHI3%) using Kruskal–Wallis tests for continuous variables and Pearson’s χ² or Fisher’s exact tests for categorical variables. Diagnostic accuracy of HB was evaluated for pAHI3% ≥15 and ≥30 events/hr. Optimal cut-offs were derived using Youden’s index. For each threshold, we calculated sensitivity, specificity, positive and negative predictive values (PPV, NPV), likelihood ratios (LR⁺, LR⁻), and AUC-ROC with 95% confidence intervals. In parallel, we assessed categorical HB definitions (high vs. rest; any vs. low) and created three ordinal HB bands based on cohort percentiles: low (<P25, P75, >40.1 %·min/h). HB distributions were cross-tabulated against AASM OSA severity categories (mild, moderate, severe) to identify discordance between HB and pAHI3% categories. To explore the relationship between HB and key physiological predictors, we performed correlation analyses using Pearson (linear) and Spearman (monotonic) coefficients. Scatterplots with fitted linear regression lines and 95% confidence intervals were generated to visualize associations between total HB and pAHI3%, BMI, and positional HB components (supine, right-lateral, left-lateral, prone). These analyses were exploratory and intended to quantify concordance or divergence between event-based respiratory indices and oxygenation-derived physiological load. Analyses were restricted to complete cases; missing data were minimal and not associated with outcomes. All tests were two-sided, and p<0.05 was considered statistically significant. No adjustments were made for multiple comparisons. RESULT Baseline Characteristics: Among 1,171 OSA patients (pAHI3% ≥5/h), median age was 53 years (IQR 43–64), with 43.5% females. Median BMI was 27.0 kg/m² (IQR 24.2–30.1), with 42.3% overweight and 25.9% obese per WHO classification. Median ESS was 6 (IQR 3–10), with 42.5% reporting normal, 33.0% mild, 17.2% moderate, and 7.3% severe sleepiness. STOP-BANG median score was 3 (IQR 2–4), with 37.1% low, 39.7% intermediate, and 23.2% high risk. OSA severity was mild in 31.9% (n = 373), moderate in 33.8% (n = 396), and severe in 34.3% (n = 402). POSA was identified in 39.7% (Cartwright) and 53.0% (APOC), distributed as APOC I: 13.4%, II: 29.5%, and III: 10.1%. Full cohort characteristics are presented in Table 1. PAT-HSAT Characteristics: Percentile distributions (P10–P90) for sleep architecture, respiratory indices, oxygenation, and cardiovascular metrics are detailed in Table 2. Median pAHI3% was 22 events/h (IQR 12.7–38.7), with higher rates during REM. ODI4% reached 11.9 events/h (IQR 6.2–24.0). T<90% was 22.5 min (IQR 2.6–132.2), representing 5.5% of total sleep time (IQR 0.6–30.8). HB showed wide variation: median total HB was 19.4 %·min/h (IQR 8.1–40.1), with 24.8% below P25 (40.1). Supine burden reached a median of 10.5 %·min/h, while right, left, and prone positions showed lower values. Sociodemographic and PAT-HSAT Characteristics by AASM OSA severity: Age, BMI, and neck circumference increased with OSA severity (all p<0.001): median age rose from 47.0 to 54.5 to 58.0 years; BMI from 25.62 to 27.08 to 28.40 kg/m²; and neck circumference from 36.0 to 38.0 to 39.0 cm. TST and sleep efficiency showed slight but significant increases with severity. Deep sleep decreased (18.23% to 12.00%), while light sleep increased (58.34% to 66.73%) (all p<0.001); REM sleep showed minimal variation. WASO did not differ significantly. All respiratory indices worsened with severity: pAHI3% increased from 9.90 to 46.40 events/h, and ODI4% from 4.80 to 30.30 events/h (p<0.001). T<90% rose from 2.13 to 118.86 min, while minimum SatO₂ declined from 85% to 77% (p<0.001). Mean desaturation nadir and mean SatO₂ also decreased significantly. HB increased progressively across groups (p<0.001), with median total HB rising from 6.35 to 48.77 %·min/h. Mean HR was marginally higher in severe OSA (p=0.024). All distributions and interquartile ranges are detailed in Table 3. Correlation Between Hypoxic Burden and Physiological Predictors: The pAHI3% showed a strong and statistically significant positive correlation with total HB (Figure 3), with a robust linear trend (r = 0.7008; rho = 0.761; p < 0.001). Although higher pAHI3% values were generally associated with greater HB, dispersion increased at higher event frequencies, indicating substantial inter-individual variability. These findings suggest that HB reflects physiological dimensions not fully captured by event-based indices. The BMI displayed a modest but significant association with HB, with wide variability in hypoxic burden across BMI values, reinforcing the multifactorial nature of nocturnal hypoxemia. Positional analyses demonstrated positive correlations between total HB and its supine, right-lateral, left-lateral, and prone components (Figure 4). Supine HB contributed most prominently, whereas lateral and prone burdens were lower but still aligned with total HB variability. These results highlight meaningful positional influences on the physiological expression of OSA-related hypoxemia. Diagnostic Performance of HB: For moderate-to-severe OSA (pAHI3% ≥15 events/h), the optimal HB cut-off (≥16.57 %·min/h) yielded an AUC of 0.867 (95% CI 0.846–0.888). Sensitivity was 90.1% (95% CI 87.5–92.3) and specificity 58.7% (95% CI 54.4–63.0), with PPV 72.8% (95% CI 69.6–75.9), NPV 82.8% (95% CI 78.6–86.5), (LR+) 2.18, and (LR−) 0.17, indicating value mainly as a screening tool. For severe OSA (pAHI3% ≥30 events/h), the cut-off (≥29.54 %·min/h) achieved an AUC of 0.897 (95% CI 0.879–0.916). Sensitivity was 75.3% (95% CI 70.9–79.4) and specificity 88.3% (95% CI 85.8–90.5), with PPV 78.1%, NPV 86.6%, (LR+) 6.45, and (LR−) 0.28, supporting its rule-in and rule-out utility (Table 4). Categorical HB definitions showed complementary profiles: High vs Rest maximized specificity (up to 98.7%) but reduced sensitivity, while Any vs Low achieved sensitivity up to 98.0% with more false positives. ROC curves (Figure 5) illustrate these patterns. The heatmap (Figure 6) revealed physiological heterogeneity: 30% of mild OSA patients (n=112) had higher-than-expected HB, while 26.6% of severe OSA patients (n=107) had low/intermediate HB. In moderate OSA, 29.3% (n=116) had low HB and 19.9% (n=79) had high HB. Overall, 35.4% (n=414) were reclassified into different hypoxic profiles than suggested by pAHI3%, highlighting HB’s potential for refined risk stratification. Discussion Novel Application of PAT-HSAT for HB Quantification: This study is the first large-scale report (>1,171 patients with OSA) to quantify HB using PAT-HSAT. Previous population-based investigations have relied primarily on PSG as the reference for HB assessment [9,22,24,25]. Our findings demonstrate that PAT-HSAT offers a reliable and scalable alternative through a validated automated algorithm, with optional manual verification, enabling calculation of respiratory indices and oxygenation metrics [11,23,26]. The device couples a high-definition oximeter with standardised specifications to a pneumo-optical finger probe that stabilises the distal phalanges to minimise artefacts. Compared with legacy oximeters, it offers higher root-mean-square accuracy (ARM)[27], lower noise and latency, and better preservation of rapid desaturations and successive events [16,27,28]. These characteristics support robust, automated HB estimation and align with evidence that algorithm-based approaches enhance standardisation and clinical applicability. Although recent studies suggest that airflow and SpO₂ may suffice for HB quantification [9,24,29], our findings show that PAT-HSAT (despite not being airflow-based) yields accurate HB measurements. Nevertheless, it is important to emphasise that no standardisation currently exists across the various oximeters used in clinical studies and cohort investigations. This heterogeneity can introduce systematic variation in reported oximetric values, as each device has its own technical specifications and calibration processes (which are not consistently disclosed by manufacturers). Consequently, oxygenation measurements, and therefore HB estimation, may differ according to the intrinsic characteristics of each oximeter [16, 30-32]. Comparison with Pioneering Studies : Compared with the landmark cohorts such as MrOS and SHHS [9, 24,33], our study population was substantially younger (median age 53 years; IQR 43–64 vs. 76.3 ± 5.5 in MrOS and 63.7 ± 10.9 in SHHS) and had a lower BMI (26.9 kg/m² vs. 27.3 and 28.3 kg/m², respectively). Moreover, the combined MrOS + SHHS cohorts were disproportionately male (≈65% men; SHHS 52.8% women and MrOS exclusively men), whereas our sample included 43.5% women. These differences are of great importance because older age, higher BMI, and male sex are strongly associated with greater OSA prevalence and severity [33–35] and consequently, it is plausible that the cumulative HB observed in those earlier PSG-based studies may have been higher than in our cohort. Moreover, in contrast with pioneering studies mentioned above, we reported POSA prevalence ranging from 39.71% using Cartwright’s definition to 53% under the APOC classification which is noteworthy because the overall HB in a cohort may be strongly influenced by the proportion of POSA phenotypes. Hence, studies relying solely on PSG—often performed under supine-predominant conditions—might overestimate cumulative hypoxaemia compared with real-world cohorts assessed with PAT-HSAT. It is important to note that we followed a methodological framework based on a triangular area model of desaturation events. However, two considerations are pertinent: first, our specific event HB measurement approach is anchored to the immediate baseline pre-event saturation; and second, we validate only events with desaturations ≥4%, extracted from the binary event structure of the CSV output generated automatically by the zzzPAT software. HB Distribution Across AASM OSA Categories : When analysing HB across AASM OSA severity strata, we observed a progressive increase in total mean HB values—6.3, 17.7, and 48.8 %·min/h for mild, moderate, and severe OSA, respectively (p < 0.001). This indicates a strong dose–response relationship between pAHI3% severity and cumulative nocturnal hypoxaemia (Figure 3). Analysis by cohort percentiles revealed a HB distribution as follows: 25% of patients exceeded the 75th percentile (>40.1 %·min/h), whereas another 25% fell below the 25th percentile (<8.1 %·min/h). Interestingly, within each AASM OSA severity group, HB values showed heterogeneous distributions. For example, among patients with mild OSA, a considerable proportion exhibited intermediate HB levels (P25–P75), and even a small subset exceeded P75 despite low pAHI3% scores. Conversely, some patients with severe OSA had unexpectedly low or intermediate HB. Overall, 35.4% of the cohort displayed a HB profile inconsistent with their pAHI3%-defined severity, suggesting that HB captures physiological heterogeneity beyond conventional metrics and may better reflect individual risk profiles. The considerable overlap in percentile bands indicates a dissociation between event frequency and desaturation load, reinforcing prior critiques of the pAHI3% as a sole severity index. This mismatch between metrics has been previously suggested by authors such as Linz et al., who illustrated that two individuals with an identical AHI3% may exhibit markedly different profiles in the depth and duration of their oxygen desaturations [36]. From an epidemiological perspective, this dispersion highlights the need to consider OSA severity not only by event count but also by physiological hypoxemia's weight—a concept metaphorically illustrated by Ioachimescu’s “pebble, stone, and boulder” [37]. In this framework, HB may represent the burden a patient must carry, with clinical consequences shaped by medical factors such as individual tolerance, comorbidities, and adaptive capacity among many others. Collectively, these findings advocate for the integration of HB into standard OSA phenotyping, offering a complementary (and potentially superior) dimension for subgroup stratification, personalised risk assessment, and management tailoring beyond the traditional AHI3% AASM OSA classification. Clinical Implications and Translational Use of HB in Diagnostic Decision-Making: The diagnostic performance of HB, quantified through optimised cut-offs with known sensitivity, specificity, and likelihood ratios, provides a clinically meaningful framework for risk-adapted decision-making beyond AHI3%. In our cohort, a threshold of HB ≥16.6 %·min/h for moderate-to-severe OSA achieved 90.1% sensitivity and a negative likelihood ratio (LR−) of 0.17, reducing the post-test probability to below 10% when negative—well suited to ruling out clinically significant disease. Conversely, an HB threshold of ≥29.5 %·min/h for severe OSA achieved 88.3% specificity and a positive likelihood ratio (LR⁺) of 6.45, thereby substantially increasing the post-test probability. This enhanced discriminative capacity makes the threshold suitable for confirming disease severity and for guiding timely treatment escalation. The clinical implications are illustrated by two scenarios. A patient with pAHI3% = 20 events/h but HB = 10 %·min/h (below the 16.6%·min/h cut-off) has a low likelihood of significant cumulative hypoxaemia and could be managed initially with stepwise strategies such as PT, sleep hygiene or weight reduction before committing to CPAP [38] or OAT [39]. In contrast, another patient with the same pAHI3% but HB = 45 %·min/h (above the 29.5 cut-off) exceeds the specificity threshold, strongly indicating a high desaturation load and supporting early CPAP initiation or combined therapy, particularly in the presence of cardiovascular risk factors. Thus, two patients with identical pAHI3% values but divergent HB may follow different management pathways, underscoring the value of HB in risk stratification and personalised therapy [29,40]. Limitations and strengths: This study has a number of methodological considerations that help contextualise its findings. It was conducted in a retrospective, single-center cohort, which reflects real-world clinical practice but may limit broader generalisability. Both the HB metric and the reference index (pAHI3%) were obtained from the same PAT-HSAT recording creating some methodological dependency nonetheless this ensures uniform automatic signal acquisition enhancing internal validity. All measurements originated from a single technology family (WatchPAT®). However, the HB values in this study were computed using an institutional algorithm, independently generated before the release of the manufacturer's proprietary processing algorithm of HB. Our algorithm relies on clinically meaningful desaturation events ≥4% drops from individual baselines extracted directly from the raw CSV signals, providing a physiologically grounded characterisation of HB. We deliberately focused on these automatic detected, scorer-independent events because ≥4% desaturations have been more consistently linked to clinically relevant hypoxaemia and adverse cardiovascular outcomes than milder 3% dips. Differences in sampling dynamics and other intrinsic technical characteristics related to the manufacturer’s proprietary oximeter must also be acknowledged. Given these inherent factors, over which no external control is possible, our findings cannot be extrapolated to other oximeters with differing specifications. Our study included subjects living at an altitude between 1,500–2,600 m.a.s.l., which naturally lowers baseline oxygen saturation, potentially influencing HB under 90% SatO₂ but remains relevant for populations living in similar environments (around 500 million people around the world)[41,42]. The HB thresholds were derived and assessed within the same dataset and therefore require external validation. In addition, outcome-based analyses were beyond the scope of this work, so the prognostic implications of HB remain to be established. These considerations highlight the need for prospective cohorts with PSG comparators to confirm our findings and the clinical performance of the proposed thresholds. Conclusion This first large-scale study demonstrates that PAT-HSAT can reliably quantify HB using high-resolution oximetry and validated automated algorithms, providing a scalable and clinically actionable alternative to PSG. HB showed a clear dose–response across AASM OSA severity categories but with marked intra-stratum variability, revealing that the sole pAHI3% metric is not enough to estimate the physiological impact of OSA. About one-third of patients exhibited discordance between pAHI3% and HB, underscoring HB’s value for individual risk stratification. Collectively, these findings support HB as a complementary, and potentially superior, biomarker to pAHI3% for OSA phenotyping, risk assessment, and management tailoring. Integration of HB into PAT-HSAT reporting may enhance precision diagnostics and inform treatment pathways, although multicenter validation across populations and care settings remains essential. Abbreviations Abbreviations Definition AASM American Academy of Sleep Medicine AHI 3% Apnea–Hypopnea Index BMI Body Mass Index ESS Epworth Sleepiness Scale HB Hypoxic Burden HR Heart Rate HSAT Home Sleep Apnea Testing NREM Non-Rapid Eye Movement Sleep ODI4 Oxygen Desaturation Index OSA Obstructive Sleep Apnea PAT Peripheral Arterial Tone PAT-HSAT Peripheral Arterial Tonometry - Home Sleep Apnea Testing pAHI 3% Apnea–Hypopnea Index derived from PAT pRDI Respiratory Disturbance Index derived from PAT PSG Polysomnography PT Positional Therapy RE Respiratory Event REM Rapid Eye Movement Sleep SatO₂ Oxygen Saturation STARD Standards for Reporting of Diagnostic Accuracy TST Total Sleep Time T<90% Time spent with oxygen saturation below 90% WASO Wake After Sleep Onset WHO World Health Organization Declarations Ethics approval and consent to participate: This study was approved by the Institutional Ethics Committee of Fundarritmia – Fundación Cardiovascular, Bogotá, Colombia, and was classified as “no-risk” research under Colombian Resolution 8430/1993. The requirement for informed consent was waived due to the retrospective design and full anonymization of data. Consent for publication: Not applicable. Availability of data and materials: The dataset(s) supporting the conclusions of this article are not publicly available due to institutional restrictions but are available from the corresponding author upon reasonable request. Competing interests: The authors declare no competing interests. Funding: No funding was received for this study. Authors’ contributions Diego Ignacio Vanegas: conceptualization; methodology; supervision; writing—original draft; writing—review and editing; project administration. Andrés Felipe Blanco: methodology; data curation; formal analysis; writing—review and editing; visualization. Fernando Adolfo Vanegas: engineering conceptualization; software design. Francisco Jose Hurtado: hypoxic burden calculations; data processing. Acknowledgements: The authors thank the clinical and technical team of Fundarritmia – Fundación Cardiovascular for their support. Authors’ information (optional): Not applicable. Additional files Additional file 1 — Supplementary Methods (DOCX) Title: Supplementary Methods Description: Detailed algorithmic procedures, preprocessing rules, event-based and continuous HB definitions, pseudocode, QC processes, versioning and reproducibility notes. References Malhotra A, Ayappa I, Ayas N, Collop N, Kirsch D, Mcardle N, Mehra R, Pack AI, Punjabi N, White DP, Gottlieb DJ. Metrics of sleep apnea severity: beyond the apnea-hypopnea index. Sleep. 2021 Jul 9;44(7):zsab030. doi: 10.1093/sleep/zsab030. PMID: 33693939; PMCID: PMC8271129. Ruehland WR, Rochford PD, O'Donoghue FJ, Pierce RJ, Singh P, Thornton AT. The new AASM criteria for scoring hypopneas: impact on the apnea hypopnea index. Sleep. 2009 Feb;32(2):150-7. doi: 10.1093/sleep/32.2.150. PMID: 19238801; PMCID: PMC2635578. 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Coso, C., Solano-Pérez, E., Romero-Peralta, S., Castillo-García, M., Silgado-Martínez, L., López-Monzoni, S., Resano-Barrio, P., Cano-Pumarega, I., Sánchez-De-La-Torre, M., & Mediano, O. (2024). The Hypoxic Burden, Clinical Implication of a New Biomarker in the Cardiovascular Management of Sleep Apnea Patients: A Systematic Review. Reviews in Cardiovascular Medicine , 25. https://doi.org/10.31083/j.rcm2505172. Cohen JE, Small C. Hypsographic demography: the distribution of human population by altitude. Proc Natl Acad Sci USA. 1998; 95(24): 14009–14. DOI: 10.1073/pnas.95.24.14009 Urban Demographics. World population distribution by altitude; 2017. (updated 29 June 2017). Available from: https://urbandemographics.blogspot.com/2017/06/world-population-distribution-by.html. Accessed 4 December 2025. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1SupplementaryMethods.docx Tables.docx Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in Sleep Science and Practice → Version 1 posted Editorial decision: Revision requested 23 Dec, 2025 Reviews received at journal 23 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers invited by journal 16 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 04 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:32:11","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15232,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/318a056aeb21d511e1918b6f.png"},{"id":97966118,"identity":"fbc5a3e9-f757-4291-a469-4fb252327be6","added_by":"auto","created_at":"2025-12-11 09:51:42","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43009,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/25ab3628e84f8032716b654c.png"},{"id":97966092,"identity":"b4761400-b4c6-455b-bd50-d6d33fac2cb9","added_by":"auto","created_at":"2025-12-11 09:51:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSTARD flow diagram of patient inclusion and exclusions.\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eFlow diagram summarizing eligibility assessment, reasons for exclusion, and the final analytic sample (n = 1,171).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e OSA, obstructive sleep apnea; PAT-HSAT, peripheral arterial tonometry home sleep apnea testing; pAHI3%, PAT-derived apnea–hypopnea index (3% rule); TST, total sleep time.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/5656de44cc43efcbd15b162c.png"},{"id":98423421,"identity":"f1f99110-dff2-4d2b-8517-608ca57b8d40","added_by":"auto","created_at":"2025-12-17 16:32:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2461205,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eApnea/hypopnea detection using PAT-HSAT\u003c/strong\u003e\u003cem\u003e. \u003c/em\u003eConcurrent channels (top to bottom): PAT waveform, PAT amplitude, pulse rate, SatO₂, and respiratory movement. Obstructive events are characterised by \u0026gt;30% fall in PAT amplitude and \u0026gt;10% rise in pulse rate, followed by SatO₂ desaturation (shaded regions). This diagnostic window is used by the device to score apnea/hypopnea.\u003cbr\u003e\n \u003cstrong\u003eAbbreviations:\u003c/strong\u003e PAT-HSAT, Peripheral Arterial Tonometry - Home Sleep Apnea Testing; SatO₂, oxygen saturation.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/f4d582e779b4ae2ecde0086e.png"},{"id":98423462,"identity":"b6e2f44a-a868-4af5-b7c3-dec325d3aa93","added_by":"auto","created_at":"2025-12-17 16:32:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":546918,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between the pAHI3% and the HB.\u003c/strong\u003e A strong and statistically significant positive relationship was observed, with a robust linear trend (Pearson r = 0.7008; Spearman rho = 0.761; p \u0026lt; 0.001). Increasing dispersion at higher pAHI3% values indicates substantial inter-individual variability in HB for a given respiratory event frequency. These findings suggest that, although pAHI3% partially predicts the magnitude of accumulated intermittent nocturnal hypoxemia, HB captures additional physiological dimensions not fully reflected by event-based indices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003epAHI3%, Apnea–Hypopnea Index derived from PAT; HB, hypoxic burden.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/3a6b9b2267a50aabd9454435.png"},{"id":97966101,"identity":"f4e91ff1-821d-4e62-9d40-878333e8156c","added_by":"auto","created_at":"2025-12-11 09:51:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":560833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between HB and body position.\u003c/strong\u003e Each panel displays individual patient values, the fitted linear regression line, and the corresponding 95% confidence interval. The stronger linearity observed in the supine position aligns with its known physiological predisposition to airway collapse, whereas lateral and prone positions show weaker contributions. These findings underline the positional heterogeneity of nocturnal hypoxemia and support the relevance of posture-specific metrics in characterizing OSA physiology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eHB, hypoxic burden; CI, confidence interval; OSA, Obstructive Sleep Apnea\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/f4ff8628c24ea0cc74799832.png"},{"id":98423349,"identity":"2a31f07c-1ef9-4a95-bdb5-4be2835a2f24","added_by":"auto","created_at":"2025-12-17 16:32:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":311377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic accuracy of HB for OSA severity thresholds. \u003c/strong\u003e(A) ROC curve for moderate-to-severe OSA (pAHI3% ≥15 events/h), AUC = 0.87 (95% CI, 0.85–0.89). (B) ROC curve for severe OSA (pAHI3% ≥30 events/h), AUC = 0.90 (95% CI, 0.88 – 0.92). The diagonal line represents the non-informative reference line (AUC = 0.5). Optimal cut-offs were derived using Youden’s index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eOSA, Obstructive Sleep Apnea; HB, hypoxic burden; AUC, Area Under the Curve; ROC, Receiving Operating Curve\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/9029dd120742a638bc36af9f.png"},{"id":98423445,"identity":"2a7f1873-9150-4248-bd5f-a7198f137ba5","added_by":"auto","created_at":"2025-12-17 16:32:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2105796,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of HB categories across AASM OSA severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap illustrates HB categories defined by cohort percentiles: low (\u0026lt;P25), intermediate (P25–P75), and high (\u0026gt;P75). Each cell shows absolute counts and row percentages; colour intensity represents relative proportion within each severity group. One-third of patients were reclassified into discordant HB profiles compared with their AHI3% AASM OSA severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eOSA, Obstructive Sleep Apnea; HB, hypoxic burden; AASM, American Academy of Sleep Medicine\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/4b10f7c14cecf4a4880cdd39.png"},{"id":106343266,"identity":"84a74e93-65a6-4a87-a959-1169153e6403","added_by":"auto","created_at":"2026-04-07 16:00:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6647409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/0139f141-0ddd-4b09-90eb-b59d4cceaefb.pdf"},{"id":97966106,"identity":"d8f8c7d3-eea2-43d3-a7ac-bbd9c2775211","added_by":"auto","created_at":"2025-12-11 09:51:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2984302,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/a031b4fd32f70a02096de8cb.docx"},{"id":98423326,"identity":"5920bc57-ecaa-4c18-8dc1-efd715bac92c","added_by":"auto","created_at":"2025-12-17 16:32:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30555,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8282627/v1/8a8b8695451b7d9aeaa35dc4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHypoxic Burden Calculation in Patients with Obstructive Sleep Apnea Diagnosed by Peripheral Arterial Tonometry: Diagnostic Accuracy and Clinical Implications\u003c/p\u003e","fulltext":[{"header":"Brief Summary ","content":"\u003cp\u003e\u003cstrong\u003eCurrent Knowledge/Study Rationale:\u003c/strong\u003e The AHI3% is the standard metric for grading OSA severity but does not reflect the cumulative depth and duration of oxygen desaturation. Hypoxic burden integrates desaturation area per hour and may better represent physiologic load. The clinical value of HB when derived from PAT-HSAT remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Impact:\u003c/strong\u003e HB derived from PAT-HSAT demonstrated good-to-excellent discrimination for moderate-to-severe and severe OSA and physiologically reclassified 35.4% of patients within AASM categories. Thresholds near 16.6 and 29.5 %\u0026middot;min/h support rule-out and rule-in strategies. Routine integration of HB into PAT-HSAT reports may improve clinical risk stratification and guide personalized treatment.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eThe severity of OSA has conventionally been quantified using the apnea\u0026ndash;hypopnea index with the \u0026ge;3% desaturation criterion (AHI3%), as endorsed by the AASM [1,2]. Under this framework, apneas are defined by a reduction in ventilatory flow exceeding 90%, whereas hypopneas represent reductions between 30% and 90%, both sustained for at least 10 seconds. For hypopneas, an oxygen desaturation \u0026ge;3% or \u0026ge;4%, with or without cortical arousal, is additionally required [3,4].\u003c/p\u003e\n\u003cp\u003eThe AHI3% has been extensively employed to guide therapeutic decisions [5] and to estimate both all-cause and cardiovascular mortality risk [6,7]. Nevertheless, accumulating evidence indicates that AHI3% alone may misclassify OSA severity in specific patient subgroups, given its inability to capture the cumulative depth and duration of hypoxic episodes [1,8]. This limitation has catalysed interest in novel metrics capable of providing a more physiologically integrated assessment of nocturnal hypoxaemia.\u003c/p\u003e\n\u003cp\u003eIn this context, the HB has recently emerged as a novel metric capturing the total area (duration \u0026times; depth) of desaturation events per hour of sleep, thereby providing a more physiologically comprehensive measure of hypoxaemic stress and demonstrating prognostic significance for survival [9]. Expressed as %\u0026middot;min/h, this parameter quantifies the cumulative area of oxygen desaturations (specifically those associated with apnea and hypopnea events) indexed per hour of effective sleep.\u003c/p\u003e\n\u003cp\u003eThe PAT, a type III HSAT modality, is considered technically appropriate by the AASM and other professional societies for the evaluation of patients with suspected OSA [3,10]. The PAT-HSAT automatically classifies events as apneas or hypopneas using a surrogate for airflow and respiratory effort derived from the PAT signals. Classification occurs when oxygen desaturations \u0026ge;3% or \u0026ge;4% coincide with a \u0026gt;30% reduction in the amplitude of the raw PAT signal (reflecting arterial tone), together with a peri-critical increase of \u0026gt;10% in HR [11]. In addition, PAT-HSAT classifies as apnea or hypopnea any event with oxygen desaturations \u0026ge;4%, even when the full autonomic response described above is not completely met. All events must also satisfy a minimum duration of 10 seconds to be considered diagnostically valid.\u003c/p\u003e\n\u003cp\u003eTherefore, this study aimed to evaluate the diagnostic performance of HB in patients with OSA previously diagnosed using the desaturation-based criterion (pAHI3%) derived from PAT-HSAT. Specifically, we sought to quantify the cumulative HB linked to respiratory events, characterise its distribution across standard AASM OSA severity categories, and determine its incremental utility in enhancing risk stratification beyond the conventional AHI3% based classification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Population: \u003c/strong\u003eWe performed a retrospective diagnostic accuracy study to assess HB in patients with OSA, using pAHI3% as the internal reference. The cohort included consecutive adults (\u0026ge;18 years) who underwent PAT-HSAT at Fundarritmia, Fundaci\u0026oacute;n Cardiovascular, Bogot\u0026aacute;, Colombia (Jan 2016\u0026ndash;Aug 2025). Of 1,455 recordings, we excluded studies with non-diagnostic indications, missing or poor-quality signals, inconclusive interpretation, absent positional data, pacemakers, or TST \u0026lt;240 min. The final sample comprised 1,171 patients with confirmed pAHI3% \u0026ge;5 events/h. A STARD-compliant flow diagram is provided (Figure 1). This approach ensured a real-world cohort with standardized acquisition and data quality. A priori sample size estimation was performed using standard diagnostic accuracy formulas; however, given the retrospective design and fixed sampling frame, all consecutive eligible PAT-HSAT studies were included, resulting in a larger and more statistically precise cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and Anthropometric Assessment: \u003c/strong\u003eWe administered the STOP-BANG questionnaire, categorizing scores as low (0\u0026ndash;2), intermediate (3\u0026ndash;4), or high risk (5\u0026ndash;8)[12], and the ESS to classify daytime sleepiness as normal (0\u0026ndash;5), mild (6\u0026ndash;10), moderate (11\u0026ndash;15), or severe (16\u0026ndash;24)[13]. Demographic and anthropometric data (age, sex, neck circumference, BMI) were systematically collected, with BMI classified per WHO criteria [14]. These variables ensured clinical context for interpreting pAHI3%-based severity and HB across risk profiles, anthropometric phenotypes, and disease severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAASM OSA Classification Criteria: \u003c/strong\u003eOSA severity followed AASM OSA criteria using AHI3%: mild (5\u0026ndash;14.9 events/h), moderate (15\u0026ndash;29.9), and severe (\u0026ge;30)[10]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReference Standard: \u003c/strong\u003eThe diagnostic reference standard was pAHI3%, obtained from the WatchPAT\u0026reg; system (Itamar Medical Ltd, Caesarea, Israel) using identical hardware and uniform oximetry specifications. Respiratory events (RE) were scored automatically with the zzzPAT algorithm, detecting apneas/hypopneas via oxygen desaturations (\u0026gt;3%) and autonomic arousals (\u0026gt;10% HR increase, \u0026gt;30% PAT amplitude drop). Events with desaturations \u0026gt;4% were also captured independently of complete autonomic responses [11]. All studies underwent two-stage quality control: technical verification by two trained technicians and independent review by a sleep specialist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHome Sleep Apnea Testing with Peripheral Arterial Tonometry (PAT-HSAT): \u003c/strong\u003eThe PAT-HSAT system combines a pneumo-optic finger probe with medical-grade oximetry and PAT sensors, plus actigraphy, body position, respiratory movement, and snore channels. Operating on a beat-to-beat basis (\u0026ge;8\u0026ndash;10 Hz before filtering), it ensures high temporal resolution for detecting desaturation events and vascular tone changes [11, 15\u0026ndash;17]. WatchPAT\u0026reg; algorithms, validated against in-lab PSG, are endorsed by AASM and other societies as technically adequate for patients with moderate-to-high OSA risk [10,11,15,18\u0026ndash;21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculation of HB: \u003c/strong\u003eHB was computed from encrypted binary files containing second-by-second SatO₂ data. Signals were segmented into 1-s epochs annotated for sleep stage, snoring, RE, and quality-control flags; epochs with artefact, interference, or wakefulness were excluded. RE were defined as a \u0026gt;30% reduction in PAT amplitude accompanied by a \u0026ge;10% rise in HR and a \u0026ge;3% fall in SatO₂ (Figure 2). For each RE (apnea or hypoapnea), the desaturation area was estimated using a triangular approximation (\u0026frac12; \u0026times; \u0026Delta;SatO₂ \u0026times; duration), summed across all events, and normalised to TST to yield HB, expressed as %\u0026middot;min/h [9,22]. Positional HB was derived by partitioning RE according to body position (supine, lateral, prone). For the institutional implementation used in this study, the HB algorithm operated specifically on events with desaturations \u0026ge;4%, thereby anchoring HB to more pronounced and clinically meaningful oxygen. By selecting this strategy, the algorithm relies on the fully automated identification of these events by zzzPAT, without the need for manual validation. Unlike 3% desaturation (which frequently requires manual confirmation in clinical scoring systems such as COMPAS)[23], 4% drops represent a more robust and consistently detected oximetric signal that is less dependent on scorer-based interpretation. \u003c/p\u003e\n\u003cp\u003eIn this study, the proprietary HypoxicBurden.exe software was used exclusively, operating solely on the encrypted SatO₂ channel linked to RE in CSV file. It is important to note that the institutional algorithm used in this study was developed and implemented before the release of the most recent WatchPAT/Zoll-Itamar software update that includes an automated HB output. Systematic manual editing based on COMPASS [23] and AASM Manual Scoring rules [3] primarily removed RE associated with 3% desaturations that were automatically flagged by zzzPAT but did not fulfill scoring criteria. This step was fundamental to obtain a refined pAHI3% calculation and to prevent artificial inflation driven by spurious 3% events. Accordingly, the final pAHI3% computation was always performed after manual editing, which constituted a strict prerequisite for all analyses. \u003c/p\u003e\n\u003cp\u003eFor reproducibility, both an event-based HB algorithm and a second-by-second (continuous) HB algorithm were documented. However, all primary and secondary analyses in this study used the event-based HB definition only (triangular desaturation area per RE, normalised by TST). The continuous HB algorithm was not applied to the dataset and is not reported among the results. Full algorithmic and quality-control procedures are detailed in Additional file 1: Supplementary Methods\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis: \u003c/strong\u003eAnalyses were conducted in Stata 19.0 (StataCorp, College Station, TX). The index test was the event-based HB definition. Continuous variables were summarised as medians (IQR) and categorical variables as counts (%). Baseline characteristics were reported for the entire cohort and stratified by OSA severity (pAHI3%) using Kruskal\u0026ndash;Wallis tests for continuous variables and Pearson\u0026rsquo;s \u0026chi;\u0026sup2; or Fisher\u0026rsquo;s exact tests for categorical variables.\u003c/p\u003e\n\u003cp\u003eDiagnostic accuracy of HB was evaluated for pAHI3% \u0026ge;15 and \u0026ge;30 events/hr. Optimal cut-offs were derived using Youden\u0026rsquo;s index. For each threshold, we calculated sensitivity, specificity, positive and negative predictive values (PPV, NPV), likelihood ratios (LR⁺, LR⁻), and AUC-ROC with 95% confidence intervals. In parallel, we assessed categorical HB definitions (high vs. rest; any vs. low) and created three ordinal HB bands based on cohort percentiles: low (\u0026lt;P25, \u0026lt;8.1 %\u0026middot;min/h), intermediate (P25\u0026ndash;P75, 8.1\u0026ndash;40.1 %\u0026middot;min/h), and high (\u0026gt;P75, \u0026gt;40.1 %\u0026middot;min/h). HB distributions were cross-tabulated against AASM OSA severity categories (mild, moderate, severe) to identify discordance between HB and pAHI3% categories.\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between HB and key physiological predictors, we performed correlation analyses using Pearson (linear) and Spearman (monotonic) coefficients. Scatterplots with fitted linear regression lines and 95% confidence intervals were generated to visualize associations between total HB and pAHI3%, BMI, and positional HB components (supine, right-lateral, left-lateral, prone). These analyses were exploratory and intended to quantify concordance or divergence between event-based respiratory indices and oxygenation-derived physiological load.\u003c/p\u003e\n\u003cp\u003eAnalyses were restricted to complete cases; missing data were minimal and not associated with outcomes. All tests were two-sided, and p\u0026lt;0.05 was considered statistically significant. No adjustments were made for multiple comparisons.\u003c/p\u003e"},{"header":"RESULT","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics:\u0026nbsp;\u003c/strong\u003eAmong 1,171 OSA patients (pAHI3% \u0026ge;5/h), median age was 53 years (IQR 43\u0026ndash;64), with 43.5% females. Median BMI was 27.0 kg/m\u0026sup2; (IQR 24.2\u0026ndash;30.1), with 42.3% overweight and 25.9% obese per WHO classification. Median ESS was 6 (IQR 3\u0026ndash;10), with 42.5% reporting normal, 33.0% mild, 17.2% moderate, and 7.3% severe sleepiness. STOP-BANG median score was 3 (IQR 2\u0026ndash;4), with 37.1% low, 39.7% intermediate, and 23.2% high risk. OSA severity was mild in 31.9% (n = 373), moderate in 33.8% (n = 396), and severe in 34.3% (n = 402). POSA was identified in 39.7% (Cartwright) and 53.0% (APOC), distributed as APOC I: 13.4%, II: 29.5%, and III: 10.1%. Full cohort characteristics are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePAT-HSAT Characteristics:\u0026nbsp;\u003c/strong\u003ePercentile distributions (P10\u0026ndash;P90) for sleep architecture, respiratory indices, oxygenation, and cardiovascular metrics are detailed in Table 2. Median pAHI3% was 22 events/h (IQR 12.7\u0026ndash;38.7), with higher rates during REM. ODI4% reached 11.9 events/h (IQR 6.2\u0026ndash;24.0). T\u0026lt;90% was 22.5 min (IQR 2.6\u0026ndash;132.2), representing 5.5% of total sleep time (IQR 0.6\u0026ndash;30.8). HB showed wide variation: median total HB was 19.4 %\u0026middot;min/h (IQR 8.1\u0026ndash;40.1), with 24.8% below P25 (\u0026lt;8.1), 50.2% between P25\u0026ndash;P75, and 25.0% above P75 (\u0026gt;40.1). Supine burden reached a median of 10.5 %\u0026middot;min/h, while right, left, and prone positions showed lower values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociodemographic and PAT-HSAT Characteristics by AASM OSA severity:\u0026nbsp;\u003c/strong\u003eAge, BMI, and neck circumference increased with OSA severity (all p\u0026lt;0.001): median age rose from 47.0 to 54.5 to 58.0 years; BMI from 25.62 to 27.08 to 28.40 kg/m\u0026sup2;; and neck circumference from 36.0 to 38.0 to 39.0 cm. TST and sleep efficiency showed slight but significant increases with severity. Deep sleep decreased (18.23% to 12.00%), while light sleep increased (58.34% to 66.73%) (all p\u0026lt;0.001); REM sleep showed minimal variation. WASO did not differ significantly.\u003c/p\u003e\n\u003cp\u003eAll respiratory indices worsened with severity: pAHI3% increased from 9.90 to 46.40 events/h, and ODI4% from 4.80 to 30.30 events/h (p\u0026lt;0.001). T\u0026lt;90% rose from 2.13 to 118.86 min, while minimum SatO₂ declined from 85% to 77% (p\u0026lt;0.001). Mean desaturation nadir and mean SatO₂ also decreased significantly. HB increased progressively across groups (p\u0026lt;0.001), with median total HB rising from 6.35 to 48.77 %\u0026middot;min/h. Mean HR was marginally higher in severe OSA (p=0.024). All distributions and interquartile ranges are detailed in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Between Hypoxic Burden and Physiological Predictors:\u0026nbsp;\u003c/strong\u003eThe pAHI3% showed a strong and statistically significant positive correlation with total HB (Figure 3), with a robust linear trend (r = 0.7008; rho = 0.761; p \u0026lt; 0.001). Although higher pAHI3% values were generally associated with greater HB, dispersion increased at higher event frequencies, indicating substantial inter-individual variability. These findings suggest that HB reflects physiological dimensions not fully captured by event-based indices. The BMI displayed a modest but significant association with HB, with wide variability in hypoxic burden across BMI values, reinforcing the multifactorial nature of nocturnal hypoxemia.\u003c/p\u003e\n\u003cp\u003ePositional analyses demonstrated positive correlations between total HB and its supine, right-lateral, left-lateral, and prone components (Figure 4). Supine HB contributed most prominently, whereas lateral and prone burdens were lower but still aligned with total HB variability. These results highlight meaningful positional influences on the physiological expression of OSA-related hypoxemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Performance of HB:\u0026nbsp;\u003c/strong\u003eFor moderate-to-severe OSA (pAHI3% \u0026ge;15 events/h), the optimal HB cut-off (\u0026ge;16.57 %\u0026middot;min/h) yielded an AUC of 0.867 (95% CI 0.846\u0026ndash;0.888). Sensitivity was 90.1% (95% CI 87.5\u0026ndash;92.3) and specificity 58.7% (95% CI 54.4\u0026ndash;63.0), with PPV 72.8% (95% CI 69.6\u0026ndash;75.9), NPV 82.8% (95% CI 78.6\u0026ndash;86.5), (LR+) 2.18, and (LR\u0026minus;) 0.17, indicating value mainly as a screening tool. For severe OSA (pAHI3% \u0026ge;30 events/h), the cut-off (\u0026ge;29.54 %\u0026middot;min/h) achieved an AUC of 0.897 (95% CI 0.879\u0026ndash;0.916). Sensitivity was 75.3% (95% CI 70.9\u0026ndash;79.4) and specificity 88.3% (95% CI 85.8\u0026ndash;90.5), with PPV 78.1%, NPV 86.6%, (LR+) 6.45, and (LR\u0026minus;) 0.28, supporting its rule-in and rule-out utility (Table 4).\u003c/p\u003e\n\u003cp\u003eCategorical HB definitions showed complementary profiles: High vs Rest maximized specificity (up to 98.7%) but reduced sensitivity, while Any vs Low achieved sensitivity up to 98.0% with more false positives. ROC curves (Figure 5) illustrate these patterns. The heatmap (Figure 6) revealed physiological heterogeneity: 30% of mild OSA patients (n=112) had higher-than-expected HB, while 26.6% of severe OSA patients (n=107) had low/intermediate HB. In moderate OSA, 29.3% (n=116) had low HB and 19.9% (n=79) had high HB. Overall, 35.4% (n=414) were reclassified into different hypoxic profiles than suggested by pAHI3%, highlighting HB\u0026rsquo;s potential for refined risk stratification.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eNovel Application of PAT-HSAT for HB Quantification: \u003c/strong\u003eThis study is the first large-scale report (\u0026gt;1,171 patients with OSA) to quantify HB using PAT-HSAT. Previous population-based investigations have relied primarily on PSG as the reference for HB assessment [9,22,24,25]. Our findings demonstrate that PAT-HSAT offers a reliable and scalable alternative through a validated automated algorithm, with optional manual verification, enabling calculation of respiratory indices and oxygenation metrics [11,23,26].\u003c/p\u003e\n\u003cp\u003eThe device couples a high-definition oximeter with standardised specifications to a pneumo-optical finger probe that stabilises the distal phalanges to minimise artefacts. Compared with legacy oximeters, it offers higher root-mean-square accuracy (ARM)[27], lower noise and latency, and better preservation of rapid desaturations and successive events [16,27,28]. These characteristics support robust, automated HB estimation and align with evidence that algorithm-based approaches enhance standardisation and clinical applicability. Although recent studies suggest that airflow and SpO₂ may suffice for HB quantification [9,24,29], our findings show that PAT-HSAT (despite not being airflow-based) yields accurate HB measurements. \u003c/p\u003e\n\u003cp\u003eNevertheless, it is important to emphasise that no standardisation currently exists across the various oximeters used in clinical studies and cohort investigations. This heterogeneity can introduce systematic variation in reported oximetric values, as each device has its own technical specifications and calibration processes (which are not consistently disclosed by manufacturers). Consequently, oxygenation measurements, and therefore HB estimation, may differ according to the intrinsic characteristics of each oximeter [16, 30-32]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with Pioneering Studies\u003c/strong\u003e: Compared with the landmark cohorts such as MrOS and SHHS [9, 24,33], our study population was substantially younger (median age 53 years; IQR 43\u0026ndash;64 vs. 76.3 \u0026plusmn; 5.5 in MrOS and 63.7 \u0026plusmn; 10.9 in SHHS) and had a lower BMI (26.9 kg/m\u0026sup2; vs. 27.3 and 28.3 kg/m\u0026sup2;, respectively). Moreover, the combined MrOS + SHHS cohorts were disproportionately male (\u0026asymp;65% men; SHHS 52.8% women and MrOS exclusively men), whereas our sample included 43.5% women. These differences are of great importance because older age, higher BMI, and male sex are strongly associated with greater OSA prevalence and severity [33\u0026ndash;35] and consequently, it is plausible that the cumulative HB observed in those earlier PSG-based studies may have been higher than in our cohort.\u003c/p\u003e\n\u003cp\u003eMoreover, in contrast with pioneering studies mentioned above, we reported POSA prevalence ranging from 39.71% using Cartwright\u0026rsquo;s definition to 53% under the APOC classification which is noteworthy because the overall HB in a cohort may be strongly influenced by the proportion of POSA phenotypes. Hence, studies relying solely on PSG\u0026mdash;often performed under supine-predominant conditions\u0026mdash;might overestimate cumulative hypoxaemia compared with real-world cohorts assessed with PAT-HSAT.\u003c/p\u003e\n\u003cp\u003eIt is important to note that we followed a methodological framework based on a triangular area model of desaturation events. However, two considerations are pertinent: first, our specific event HB measurement approach is anchored to the immediate baseline pre-event saturation; and second, we validate only events with desaturations \u0026ge;4%, extracted from the binary event structure of the CSV output generated automatically by the zzzPAT software. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHB Distribution Across AASM OSA Categories\u003c/strong\u003e: When analysing HB across AASM OSA severity strata, we observed a progressive increase in total mean HB values\u0026mdash;6.3, 17.7, and 48.8 %\u0026middot;min/h for mild, moderate, and severe OSA, respectively (p \u0026lt; 0.001). This indicates a strong dose\u0026ndash;response relationship between pAHI3% severity and cumulative nocturnal hypoxaemia (Figure 3). \u003c/p\u003e\n\u003cp\u003eAnalysis by cohort percentiles revealed a HB distribution as follows: 25% of patients exceeded the 75th percentile (\u0026gt;40.1 %\u0026middot;min/h), whereas another 25% fell below the 25th percentile (\u0026lt;8.1 %\u0026middot;min/h). Interestingly, within each AASM OSA severity group, HB values showed heterogeneous distributions. For example, among patients with mild OSA, a considerable proportion exhibited intermediate HB levels (P25\u0026ndash;P75), and even a small subset exceeded P75 despite low pAHI3% scores. Conversely, some patients with severe OSA had unexpectedly low or intermediate HB. Overall, 35.4% of the cohort displayed a HB profile inconsistent with their pAHI3%-defined severity, suggesting that HB captures physiological heterogeneity beyond conventional metrics and may better reflect individual risk profiles. \u003c/p\u003e\n\u003cp\u003eThe considerable overlap in percentile bands indicates a dissociation between event frequency and desaturation load, reinforcing prior critiques of the pAHI3% as a sole severity index. This mismatch between metrics has been previously suggested by authors such as Linz et al., who illustrated that two individuals with an identical AHI3% may exhibit markedly different profiles in the depth and duration of their oxygen desaturations [36]. From an epidemiological perspective, this dispersion highlights the need to consider OSA severity not only by event count but also by physiological hypoxemia\u0026apos;s weight\u0026mdash;a concept metaphorically illustrated by Ioachimescu\u0026rsquo;s \u0026ldquo;pebble, stone, and boulder\u0026rdquo; [37]. In this framework, HB may represent the burden a patient must carry, with clinical consequences shaped by medical factors such as individual tolerance, comorbidities, and adaptive capacity among many others.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings advocate for the integration of HB into standard OSA phenotyping, offering a complementary (and potentially superior) dimension for subgroup stratification, personalised risk assessment, and management tailoring beyond the traditional AHI3% AASM OSA classification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implications and Translational Use of HB in Diagnostic Decision-Making: \u003c/strong\u003eThe diagnostic performance of HB, quantified through optimised cut-offs with known sensitivity, specificity, and likelihood ratios, provides a clinically meaningful framework for risk-adapted decision-making beyond AHI3%. In our cohort, a threshold of HB \u0026ge;16.6 %\u0026middot;min/h for moderate-to-severe OSA achieved 90.1% sensitivity and a negative likelihood ratio (LR\u0026minus;) of 0.17, reducing the post-test probability to below 10% when negative\u0026mdash;well suited to ruling out clinically significant disease. Conversely, an HB threshold of \u0026ge;29.5 %\u0026middot;min/h for severe OSA achieved 88.3% specificity and a positive likelihood ratio (LR⁺) of 6.45, thereby substantially increasing the post-test probability. This enhanced discriminative capacity makes the threshold suitable for confirming disease severity and for guiding timely treatment escalation.\u003c/p\u003e\n\u003cp\u003eThe clinical implications are illustrated by two scenarios. A patient with pAHI3% = 20 events/h but HB = 10 %\u0026middot;min/h (below the 16.6%\u0026middot;min/h cut-off) has a low likelihood of significant cumulative hypoxaemia and could be managed initially with stepwise strategies such as PT, sleep hygiene or weight reduction before committing to CPAP [38] or OAT [39]. In contrast, another patient with the same pAHI3% but HB = 45 %\u0026middot;min/h (above the 29.5 cut-off) exceeds the specificity threshold, strongly indicating a high desaturation load and supporting early CPAP initiation or combined therapy, particularly in the presence of cardiovascular risk factors. Thus, two patients with identical pAHI3% values but divergent HB may follow different management pathways, underscoring the value of HB in risk stratification and personalised therapy [29,40].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and strengths: \u003c/strong\u003eThis study has a number of methodological considerations that help contextualise its findings. It was conducted in a retrospective, single-center cohort, which reflects real-world clinical practice but may limit broader generalisability. Both the HB metric and the reference index (pAHI3%) were obtained from the same PAT-HSAT recording creating some methodological dependency nonetheless this ensures uniform automatic signal acquisition enhancing internal validity.\u003c/p\u003e\n\u003cp\u003eAll measurements originated from a single technology family (WatchPAT\u0026reg;). However, the HB values in this study were computed using an institutional algorithm, independently generated before the release of the manufacturer\u0026apos;s proprietary processing algorithm of HB. Our algorithm relies on clinically meaningful desaturation events \u0026ge;4% drops from individual baselines extracted directly from the raw CSV signals, providing a physiologically grounded characterisation of HB. We deliberately focused on these automatic detected, scorer-independent events because \u0026ge;4% desaturations have been more consistently linked to clinically relevant hypoxaemia and adverse cardiovascular outcomes than milder 3% dips. \u003c/p\u003e\n\u003cp\u003eDifferences in sampling dynamics and other intrinsic technical characteristics related to the manufacturer\u0026rsquo;s proprietary oximeter must also be acknowledged. Given these inherent factors, over which no external control is possible, our findings cannot be extrapolated to other oximeters with differing specifications.\u003c/p\u003e\n\u003cp\u003eOur study included subjects living at an altitude between 1,500\u0026ndash;2,600 m.a.s.l., which naturally lowers baseline oxygen saturation, potentially influencing HB under 90% SatO₂ but remains relevant for populations living in similar environments (around 500 million people around the world)[41,42].\u003c/p\u003e\n\u003cp\u003eThe HB thresholds were derived and assessed within the same dataset and therefore require external validation. In addition, outcome-based analyses were beyond the scope of this work, so the prognostic implications of HB remain to be established. These considerations highlight the need for prospective cohorts with PSG comparators to confirm our findings and the clinical performance of the proposed thresholds.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis first large-scale study demonstrates that PAT-HSAT can reliably quantify HB using high-resolution oximetry and validated automated algorithms, providing a scalable and clinically actionable alternative to PSG. HB showed a clear dose\u0026ndash;response across AASM OSA severity categories but with marked intra-stratum variability, revealing that the sole pAHI3% metric is not enough to estimate the physiological impact of OSA. About one-third of patients exhibited discordance between pAHI3% and HB, underscoring HB\u0026rsquo;s value for individual risk stratification.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings support HB as a complementary, and potentially superior, biomarker to pAHI3% for OSA phenotyping, risk assessment, and management tailoring. Integration of HB into PAT-HSAT reporting may enhance precision diagnostics and inform treatment pathways, although multicenter validation across populations and care settings remains essential.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"550\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eAASM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eAmerican Academy of Sleep Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eAHI 3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eApnea\u0026ndash;Hypopnea Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eESS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eEpworth Sleepiness Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eHypoxic Burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eHeart Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eHSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eHome Sleep Apnea Testing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eNREM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eNon-Rapid Eye Movement Sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eODI4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eOxygen Desaturation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eOSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eObstructive Sleep Apnea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003ePAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003ePeripheral Arterial Tone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003ePAT-HSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003ePeripheral Arterial Tonometry - Home Sleep Apnea Testing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003epAHI 3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eApnea\u0026ndash;Hypopnea Index derived from PAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003epRDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eRespiratory Disturbance Index derived from PAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003ePSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003ePolysomnography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003ePositional Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eRespiratory Event\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eREM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eRapid Eye Movement Sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eSatO₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eOxygen Saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eSTARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eStandards for Reporting of Diagnostic Accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eTST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eTotal Sleep Time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eT\u0026lt;90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eTime spent with oxygen saturation below 90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eWASO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eWake After Sleep Onset\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2727%;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.7273%;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Institutional Ethics Committee of Fundarritmia \u0026ndash; Fundaci\u0026oacute;n Cardiovascular, Bogot\u0026aacute;, Colombia, and was classified as \u0026ldquo;no-risk\u0026rdquo; research under Colombian Resolution 8430/1993. The requirement for informed consent was waived due to the retrospective design and full anonymization of data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe dataset(s) supporting the conclusions of this article are not publicly available due to institutional restrictions but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiego Ignacio Vanegas: conceptualization; methodology; supervision; writing\u0026mdash;original draft; writing\u0026mdash;review and editing; project administration.\u003cbr\u003e\u0026nbsp;Andr\u0026eacute;s Felipe Blanco: methodology; data curation; formal analysis; writing\u0026mdash;review and editing; visualization.\u003cbr\u003e\u0026nbsp;Fernando Adolfo Vanegas: engineering conceptualization; software design.\u003cbr\u003e\u0026nbsp;Francisco Jose Hurtado: hypoxic burden calculations; data processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors thank the clinical and technical team of Fundarritmia \u0026ndash; Fundaci\u0026oacute;n Cardiovascular for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information (optional):\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1 \u0026mdash; Supplementary Methods (DOCX)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle: Supplementary Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription:\u0026nbsp;\u003c/strong\u003eDetailed algorithmic procedures, preprocessing rules, event-based and continuous HB definitions, pseudocode, QC processes, versioning and reproducibility notes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMalhotra A, Ayappa I, Ayas N, Collop N, Kirsch D, Mcardle N, Mehra R, Pack AI, Punjabi N, White DP, Gottlieb DJ. 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PMID: 32298733; PMCID: PMC7417383.\u003c/li\u003e\n \u003cli\u003eBlanchard M, Gerv\u0026egrave;s-Pinqui\u0026eacute; C, Feuilloy M, Le Vaillant M, Trzepizur W, Meslier N, Goupil F, Pigeanne T, Balusson F, Oger E, Sabil A, Girault JM, Gagnadoux F; ERMES study group. Hypoxic burden and heart rate variability predict stroke incidence in sleep apnea. Eur Respir J. 2021 Mar 25;57(3):2004022. doi: 10.1183/13993003.04022-2020. PMID: 33214210.\u003c/li\u003e\n \u003cli\u003eTschopp S, Borner U, Wimmer W, Caversaccio M, Tschopp K. Clinical impact of manual scoring of peripheral arterial tonometry in patients with sleep apnea. Sleep Breath. 2023 Mar;27(1):229-237. doi: 10.1007/s11325-021-02531-9. Epub 2022 Apr 2. PMID: 35366204; PMCID: PMC9992081.\u003c/li\u003e\n \u003cli\u003eElmankabadi S, Dove J, Behnke E, Chou YC, Ortiz L, Leeb G, Auchus I, Chen D, Feiner J, Law TJ, Bickler PE, Hashi S, Zamora RV, Negussie F, Bisegerwa R, Bernstein M, Lipnick MS. Comparing pulse oximeter performance using a common functional tester versus controlled desaturation studies on healthy participants. J Clin Monit Comput. 2025 Nov 14. doi: 10.1007/s10877-025-01381-0. Epub ahead of print. PMID: 41236607.\u003c/li\u003e\n \u003cli\u003eCampbell CD, Sulaiman I. The role of the WatchPAT device in the diagnosis and management of obstructive sleep apnea. Frontiers in Sleep. 2023 Aug 16;2.\u003c/li\u003e\n \u003cli\u003eMartinez-Garcia MA, S\u0026aacute;nchez-de-la-Torre M, White DP, Azarbarzin A. Hypoxic Burden in Obstructive Sleep Apnea: Present and Future. Arch Bronconeumol. 2023 Jan;59(1):36-43. English, Spanish. doi: 10.1016/j.arbres.2022.08.005. Epub 2022 Sep 5. PMID: 36115739.\u003c/li\u003e\n \u003cli\u003eCommissioner O of the. FDA In Brief: FDA warns about limitations and accuracy of pulse oximeters. FDA [Internet]. 2021 Feb 25. Available from: https://www.fda.gov/news-events/fda-brief/fda-brief-fda-warns-about-limitations-and-accuracy-pulseoximeters. Accessed 4 December 2025.\u003c/li\u003e\n \u003cli\u003eRyals S, Chiang A, Schutte-Rodin S, Chandrakantan A, Verma N, Holfinger S, Abbasi-Feinberg F, Bandyopadhyay A, Baron K, Bhargava S, He K, Kern J, Miller J, Patel R, Ratnasoma D, Deak MC. Photoplethysmography-new applications for an old technology: a sleep technology review. J Clin Sleep Med. 2023 Jan 1;19(1):189-195. doi: 10.5664/jcsm.10300. PMID: 36123954; PMCID: PMC9806792.\u003c/li\u003e\n \u003cli\u003eAbay TY, Kyriacou PA. Photoplethysmography in oxygenation and blood volume measurements. In: Kyriacou PA, Allen J, editors. Photoplethysmography: Technology, Signal Analysis and Applications. 1st ed. London: Elsevier; 2022. p. 147\u0026ndash;170.Ryals S, Chiang A, Schutte-Rodin S, Chandrakantan A, Verma N, Holfinger S, Abbasi-Feinberg F, Bandyopadhyay A, Baron K, Bhargava S, He K, Kern J, Miller J, Patel R, Ratnasoma D, Deak MC. Photoplethysmography-new applications for an old technology: a sleep technology review. J Clin Sleep Med. 2023 Jan 1;19(1):189-195. doi: 10.5664/jcsm.10300. PMID: 36123954; PMCID: PMC9806792.\u003c/li\u003e\n \u003cli\u003eAzarbarzin A, Sands SA, Taranto-Montemurro L, Redline S, Wellman A. Hypoxic burden captures sleep apnea-specific nocturnal hypoxaemia. Eur Heart J. 2019 Sep 14;40(35):2989-2990. doi: 10.1093/eurheartj/ehz274. PMID: 31071210; PMCID: PMC8599917.\u003c/li\u003e\n \u003cli\u003ePunjabi NM. The epidemiology of adult obstructive sleep apnea. Proc Am Thorac Soc. 2008 Feb 15;5(2):136-43. doi: 10.1513/pats.200709-155MG. PMID: 18250205; PMCID: PMC2645248.\u003c/li\u003e\n \u003cli\u003eLin CM, Davidson TM, Ancoli-Israel S. Gender differences in obstructive sleep apnea and treatment implications. Sleep Med Rev. 2008 Dec;12(6):481-96. doi: 10.1016/j.smrv.2007.11.003. Epub 2008 Oct 31. PMID: 18951050; PMCID: PMC2642982.\u003c/li\u003e\n \u003cli\u003eLinz D, Baumert M, Catcheside P, Floras J, Sanders P, L\u0026eacute;vy P, Cowie MR, Doug McEvoy R. Assessment and interpretation of sleep disordered breathing severity in cardiology: Clinical implications and perspectives. Int J Cardiol. 2018 Nov 15;271:281-288. doi: 10.1016/j.ijcard.2018.04.076. Epub 2018 Jul 23. PMID: 30049491.\u003c/li\u003e\n \u003cli\u003eIoachimescu OC. Obstructive sleep apnea, oxygen desaturation and hypoxic burden: pebble, rock or boulder? Respirology. 2024 Sep;29(9):761-764. doi: 10.1111/resp.14801. Epub 2024 Jul 23. PMID: 39041296.\u003c/li\u003e\n \u003cli\u003ePinilla, L., Esmaeili, N., Labarca, G., Mart\u0026iacute;nez-Garc\u0026iacute;a, M., Torres, G., Gracia-Laved\u0026aacute;n, E., M\u0026iacute;nguez, O., Mart\u0026iacute;nez, D., Abad, J., Masdeu, M., Mediano, O., Mu\u0026ntilde;oz, C., Cabriada, V., Dur\u0026aacute;n-Cantolla, J., Mayos, M., Coloma, R., Montserrat, J., De La Pe\u0026ntilde;a, M., Hu, W., Messineo, L., Sehhati, M., Wellman, A., Redline, S., Sands, S., Barb\u0026eacute;, F., S\u0026aacute;nchez-De-La-Torre, M., \u0026amp; Azarbarzin, A. (2023). Hypoxic burden to guide CPAP treatment allocation in patients with obstructive sleep apnoea: a post hoc study of the ISAACC trial. \u003cem\u003eThe European Respiratory Journal\u003c/em\u003e, 62. https://doi.org/10.1183/13993003.00828-2023.\u003c/li\u003e\n \u003cli\u003eMosca, E., Grosse, J., \u0026amp; Remmers, J. (2025). Oral appliance therapy is highly efficacious at reducing sleep apnea-specific hypoxic burden, a metric predictive of cardiovascular morbidity and mortality.. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine. https://doi.org/10.5664/jcsm.11622.\u003c/li\u003e\n \u003cli\u003eCoso, C., Solano-P\u0026eacute;rez, E., Romero-Peralta, S., Castillo-Garc\u0026iacute;a, M., Silgado-Mart\u0026iacute;nez, L., L\u0026oacute;pez-Monzoni, S., Resano-Barrio, P., Cano-Pumarega, I., S\u0026aacute;nchez-De-La-Torre, M., \u0026amp; Mediano, O. (2024). The Hypoxic Burden, Clinical Implication of a New Biomarker in the Cardiovascular Management of Sleep Apnea Patients: A Systematic Review. \u003cem\u003eReviews in Cardiovascular Medicine\u003c/em\u003e, 25. https://doi.org/10.31083/j.rcm2505172.\u003c/li\u003e\n \u003cli\u003eCohen JE, Small C. Hypsographic demography: the distribution of human population by altitude. Proc Natl Acad Sci USA. 1998; 95(24): 14009\u0026ndash;14. DOI: 10.1073/pnas.95.24.14009\u003c/li\u003e\n \u003cli\u003eUrban Demographics. World population distribution by altitude; 2017. (updated 29 June 2017). Available from: https://urbandemographics.blogspot.com/2017/06/world-population-distribution-by.html. Accessed 4 December 2025.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sleep-science-and-practice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssap","sideBox":"Learn more about [Sleep Science and Practice](http://sleep.biomedcentral.com)","snPcode":"41606","submissionUrl":"https://submission.nature.com/new-submission/41606/3","title":"Sleep Science and Practice","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hypoxic burden, obstructive sleep apnea, peripheral arterial tonometry, home sleep apnea testing, diagnostic accuracy, ROC analysis, Youden index, physiologic reclassification","lastPublishedDoi":"10.21203/rs.3.rs-8282627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8282627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe apnea–hypopnea index (AHI3%) is the conventional metric for grading obstructive sleep apnea (OSA) severity but does not capture the cumulative depth and duration of oxygen desaturation. Hypoxic burden (HB), which integrates desaturation area per hour, may better reflect physiologic load. The diagnostic performance and clinical utility of HB derived from peripheral arterial tonometry home sleep apnea testing (PAT-HSAT) remain uncertain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe performed a single-center retrospective diagnostic study of consecutive adults undergoing PAT-HSAT (WatchPAT®) between 2016 and 2025. After prespecified exclusions, 1,171 patients with pAHI3% ≥5 events/h were analyzed. HB (%·min/h) was computed from ODI4%-defined, event-linked desaturations and normalized by total sleep time. Diagnostic accuracy was evaluated for moderate-to-severe (pAHI3% ≥15) and severe OSA (pAHI3% ≥30) using ROC curves, Youden-derived thresholds, and standard performance metrics. HB distributions were cross-tabulated against AASM severity categories to assess physiologic heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e HB demonstrated good discrimination for pAHI3% ≥15 (AUC 0.867, 95% CI 0.846–0.888) and strong discrimination for pAHI3% ≥30 (AUC 0.897, 95% CI 0.879–0.916). A threshold of ≥16.6 %·min/h identified moderate-to-severe OSA with 90.1% sensitivity and 58.7% specificity. Substantial heterogeneity was observed within pAHI3% strata: 30% of mild, 49.2% of moderate, and 26.6% of severe OSA patients had HB values discordant with their AASM category. Overall, 35.4% of the cohort were physiologically “reclassified” by HB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHB derived from PAT-HSAT provides good-to-excellent diagnostic discrimination, supports rule-out for moderate-to-severe OSA and rule-in for severe OSA, and identifies clinically relevant heterogeneity within AASM categories. Incorporating HB into routine PAT-HSAT reporting may refine risk stratification and guide personalized management. External validation with PSG and clinical outcomes is warranted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration:\u003c/strong\u003e Not applicable.\u003c/p\u003e","manuscriptTitle":"Hypoxic Burden Calculation in Patients with Obstructive Sleep Apnea Diagnosed by Peripheral Arterial Tonometry: Diagnostic Accuracy and Clinical Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 09:51:35","doi":"10.21203/rs.3.rs-8282627/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-23T14:19:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T13:51:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T16:10:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301233304051014270252497251687660205225","date":"2025-12-18T11:37:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30365767692793971401251847830577722608","date":"2025-12-17T08:27:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-16T17:33:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T03:46:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-10T03:46:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sleep Science and Practice","date":"2025-12-04T21:04:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sleep-science-and-practice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssap","sideBox":"Learn more about [Sleep Science and Practice](http://sleep.biomedcentral.com)","snPcode":"41606","submissionUrl":"https://submission.nature.com/new-submission/41606/3","title":"Sleep Science and Practice","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33e446db-6e4d-4c86-a529-54a01a2e8b9f","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:00:22+00:00","versionOfRecord":{"articleIdentity":"rs-8282627","link":"https://doi.org/10.1186/s41606-026-00174-x","journal":{"identity":"sleep-science-and-practice","isVorOnly":false,"title":"Sleep Science and Practice"},"publishedOn":"2026-04-02 15:57:21","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2025-12-11 09:51:35","video":"","vorDoi":"10.1186/s41606-026-00174-x","vorDoiUrl":"https://doi.org/10.1186/s41606-026-00174-x","workflowStages":[]},"version":"v1","identity":"rs-8282627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8282627","identity":"rs-8282627","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}