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Hypoxic Burden Based on Automatically Identified Desaturations Is Associated with Adverse Health Outcomes

2023· article· en· W4385487874 on OpenAlexafffund
Neda Esmaeili, Gonzalo Labarca, Wen-Hsin Hu, Daniel Vena, Ludovico Messineo, L. Gell, Mohammadreza Hajipour, Luigi Taranto‐Montemurro, Scott A. Sands, Susan Redline, Andrew Wellman, Mohammadreza Sehhati, Ali Azarbarzin

Bibliographic record

VenueAnnals of the American Thoracic Society · 2023
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of British Columbia
FundersYork UniversityAmerican Academy of Sleep Medicine FoundationNational Institutes of HealthCase Western Reserve UniversityUniversity of WashingtonJohns Hopkins UniversityNational Heart, Lung, and Blood InstituteUniversity of California, DavisUniversity of MinnesotaAmerican Heart Association
KeywordsMedicinePulse oximetryConfoundingConfidence intervalHypoxemiaHazard ratioLogistic regressionOxygen saturationProportional hazards modelOdds ratioApneaSleep apneaInternal medicineStatisticsAnesthesia

Abstract

fetched live from OpenAlex

Abstract Rationale Recent studies have shown that sleep apnea–specific intermittent hypoxemia quantified by the hypoxic burden (HB) predicted cardiovascular disease (CVD)–related mortality in community-based and clinical cohorts. Calculation of HB is based on manual scoring of hypopneas and apneas, which is time-consuming and prone to interscorer variability. Objectives To validate a novel method to quantify the HB that is based on automatically scored desaturations. Methods The sample included 5,655 middle-aged or older adults from the Sleep Heart Health Study (52.8% women; age, 63.2 ± 11.3 yr). The original HB method was based on a subject-specific search window obtained from an ensemble average of oxygen saturation signals (as measured by pulse oximetry) and synchronized with respect to the termination of scored respiratory events. In this study, however, the search window was obtained from ensemble average of oxygen saturation signals that synchronized with respect to the minimum of all automatically identified desaturations (⩾2% and other thresholds, including 3% and 4%, in sensitivity analyses). The time interval between the two maxima around the minimum saturation was defined as the search window. The oximetry-derived HB (HBOxi) was defined as the total area under all desaturation curves (restricted by the search window) divided by the total sleep time. Logistic and Cox regression models assessed the adjusted odds ratio (aOR)/hazard ratio of excessive daytime sleepiness (EDS), hypertension (HTN), and CVD mortality per 1–standard deviation increase in HBOxi after adjusting for several covariates and confounders. Results The Spearman’s rank correlation between HB (median [interquartile range], 34.4 [18.4–59.8] % min/h) and HBOxi (median [interquartile range], 34.5 [21.6–53.8] % min/h) was 0.81 (P < 0.001). Similar to HB, HBOxi was significantly associated with EDS (aOR [95% confidence interval (CI)], 1.17 [1.09–1.26] per standard deviation), HTN (aOR [95% CI], 1.13 [1.05–1.21]), and CVD mortality (adjusted hazard ratio [95% CI], 1.15 [1.01–1.30]) in fully adjusted models. Conclusions The HBOxi was highly correlated with the HB based on manually scored apneas and hypopneas and was associated with EDS, HTN, and CVD mortality with similar effect sizes as previously reported. This method could be incorporated into wearable technology that accurately records oxygen saturation signals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.109
GPT teacher head0.437
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations52
Published2023
Admission routes2
Has abstractyes

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