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Record W4362602054 · doi:10.1164/rccm.202206-1223oc

Pulse Wave Amplitude Drops Index: A Biomarker of Cardiovascular Risk in Obstructive Sleep Apnea

2023· article· en· W4362602054 on OpenAlexfundno aff
Geoffroy Solelhac, Manuel Sánchez‐de‐la‐Torre, Margaux Blanchard, Mathieu Berger, Camila Hirotsu, Théo Imler, José Haba‐Rubio, Nicola Andrea Marchi, Virginie Bayon, Sébastien Bailly, François Goupil, Adrien Waeber, Grégory Heiniger, Thierry Pigeanne, Esther Gràcia‐Lavedan, Andrea Zapater, Jorge Abad, Estrella Ordax, María José Masdeu, Valentín Cabriada Nuño, Carlos Egea, Sandra Van Den Broecke, Péter Vollenweider, Pedro Marques‐Vidal, Julien Vaucher, Giulio Bernardi, Monica Betta, Francesca Siclari, Ferrán Barbé, Frédéric Gagnadoux, Raphaël Heinzer

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsnot available
FundersFaculté de Biologie et de Médecine, Université de LausanneEuropean Social FundResMedChinese Society of Clinical OncologySociedad Española de Neumología y Cirugía TorácicaUniversité de LausanneAllerGenGlaxoSmithKlineCentre Hospitalier Universitaire VaudoisSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean Regional Development FundFederación Española de Enfermedades RarasNational Science Foundation
KeywordsMedicineObstructive sleep apneaCardiologySleep apneaInternal medicinePulse Wave AnalysisBiomarkerSleep (system call)Pulse (music)Pulse wave velocityBlood pressure

Abstract

fetched live from OpenAlex

Abstract Rationale It is currently unclear which patients with obstructive sleep apnea (OSA) are at increased cardiovascular risk. Objective To investigate the value of pulse wave amplitude drops (PWADs), reflecting sympathetic activations and vasoreactivity, as a biomarker of cardiovascular risk in OSA. Methods PWADs were derived from pulse oximetry–based photoplethysmography signals in three prospective cohorts: HypnoLaus (N = 1,941), the Pays-de-la-Loire Sleep Cohort (PLSC; N = 6,367), and “Impact of Sleep Apnea syndrome in the evolution of Acute Coronary syndrome. Effect of intervention with CPAP” (ISAACC) (N = 692). The PWAD index was the number of PWADs (>30%) per hour during sleep. All participants were divided into subgroups according to the presence or absence of OSA (defined as ≥15 or more events per hour or <15/h, respectively, on the apnea–hypopnea index) and the median PWAD index. Primary outcome was the incidence of composite cardiovascular events. Measurements and Main Results Using Cox models adjusted for cardiovascular risk factors (hazard ratio; HR [95% confidence interval]), patients with a low PWAD index and OSA had a higher incidence of cardiovascular events compared with the high-PWAD and OSA group and those without OSA in the HypnoLaus cohort (HR, 2.16 [1.07–4.34], P = 0.031; and 2.35 [1.12–4.93], P = 0.024) and in the PLSC (1.36 [1.13–1.63], P = 0.001; and 1.44 [1.06–1.94], P = 0.019), respectively. In the ISAACC cohort, the low-PWAD and OSA untreated group had a higher cardiovascular event recurrence rate than that of the no-OSA group (2.03 [1.08–3.81], P = 0.028). In the PLSC and HypnoLaus cohorts, every increase of 10 events per hour in the continuous PWAD index was negatively associated with incident cardiovascular events exclusively in patients with OSA (HR, 0.85 [0.73–0.99], P = 0.031; and HR, 0.91 [0.86–0.96], P < 0.001, respectively). This association was not significant in the no-OSA group and the ISAACC cohort. Conclusions In patients with OSA, a low PWAD index reflecting poor autonomic and vascular reactivity was independently associated with a higher cardiovascular risk.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.325
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations80
Published2023
Admission routes1
Has abstractyes

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