Evidence map›Paper›PMID 25115886›Full record

ArticleSleep & breathing = Schlaf & Atmung2015

Accuracy of a novel auto-CPAP device to evaluate the residual apnea-hypopnea index in patients with obstructive sleep apnea.

Carlos Alberto Nigro, Sergio González, Anabella Arce, María Rosario Aragone, Luciana Nigro

Abstract readClinical Study
PubMed Publisher
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. A longitudinal study of the accuracy of positive airway pressure therapy machine-detected apnea-hypopnea events.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022
    Article
  9. Analysis of risk factors for air leakage in auto-titrating positive airway pressure users: a single-center study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022
    Article
  10. Interpreting CPAP device respiratory indices in children.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020
    Article
  11. Article
  12. Use of the WatchPAT to detect occult residual sleep-disordered breathing in patients on CPAP for obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020
    Article
  13. Article
  14. Article
  15. Article
  16. Auto-CPAP: saving money as a single tool for OSA.Sleep & breathing = Schlaf & Atmung · 2016
    Article
  17. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Carlos Alberto NigroSleep Laboratory, Hospital Alemán, Pedro Goyena 620 3 B, CP 1424, Buenos Aires, Argentina, cnigro@intramed.net.
Sergio González
Anabella Arce
María Rosario Aragone
Luciana Nigro
Hospital Alemán · ARHospital Italiano de Buenos Aires · ARHospital Ramos Mejía · AR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients under treatment with continuous positive airway pressure (CPAP) may have residual sleep apnea (RSA).

objectiveThe main objective of our study was to evaluate a novel auto-CPAP for the diagnosis of RSA.

methodsAll patients referred to the sleep laboratory to undergo CPAP polysomnography were evaluated. Patients treated with oxygen or noninvasive ventilation and split-night polysomnography (PSG), PSG with artifacts, or total sleep time less than 180 min were excluded. The PSG was manually analyzed before generating the automatic report from auto-CPAP. PSG variables (respiratory disturbance index (RDI), obstructive apnea index, hypopnea index, and central apnea index) were compared with their counterparts from auto-CPAP through Bland-Altman plots and intraclass correlation coefficient. The diagnostic accuracy of autoscoring from auto-CPAP using different cutoff points of RDI (≥5 and 10) was evaluated by the receiver operating characteristics (ROCs) curve.

resultsThe study included 114 patients (24 women; mean age and BMI, 59 years old and 33 kg/m(2); RDI and apnea/hypopnea index (AHI)-auto median, 5 and 2, respectively). The average difference between the AHI-auto and the RDI was -3.5 ± 3.9. The intraclass correlation coefficient (ICC) between the total number of central apneas, obstructive, and hypopneas between the PSG and the auto-CPAP were 0.69, 0.16, and 0.15, respectively. An AHI-auto >2 (RDI ≥ 5) or >4 (RDI ≥ 10) had an area under the ROC curve, sensitivity, specificity, positive likelihood ratio, and negative for diagnosis of residual sleep apnea of 0.84/0.89, 84/81%, 82/91%, 4.5/9.5, and 0.22/0.2, respectively.

conclusionsThe automatic analysis from auto-CPAP (S9 Autoset) showed a good diagnostic accuracy to identify residual sleep apnea. The absolute agreement between PSG and auto-CPAP to classify the respiratory events correctly varied from very low (obstructive apneas, hypopneas) to moderate (central apneas).

Indexed as

Continuous Positive Airway PressureFemaleHumansMaleMiddle AgedPolysomnographyProspective StudiesSignal Processing, Computer-AssistedSleep Apnea, ObstructiveTherapy, Computer-AssistedTreatment Outcome

Identifiers

PMID25115886
OpenAlexW1984778612

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.