Evidence map›Paper›PMID 34933855›Full record

ArticleBMJ open2021

Accuracy and usability of AcuPebble SA100 for automated diagnosis of obstructive sleep apnoea in the home environment setting: an evaluation study.

Nikesh Devani, Renard Xaviero Adhi Pramono, Syed Anas Imtiaz, Stuart Bowyer, Esther Rodriguez-Villegas, Swapna Mandal

2 registry-linked trialsAbstract readEvaluation Study
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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.

NCT03544086 completednot on this map

Clinical Evaluation of a Wearable Sleep Diagnosis Technology

TypeobservationalSponsorAcurable Ltd.Ran2018 to 2020Enrolled150ConditionsObstructive Sleep Apnea
NCT06507228 naunknown statusnot on this mapstarted 2024, after this paper: background citation

North West London Pebble Pathway: Automated OSA Diagnosis

TypeinterventionalSponsorPatrik BachtigerRan2024 to 2025Enrolled200ConditionsObstructive Sleep Apnea, Sleep ApneaArmsNWL PEBBLE STT
3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. From classic to cutting-edge: technological approaches to respiratory physiological signals in assessing sleep-disordered breathing.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Observational
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. OSA diagnosis goes wearable: are the latest devices ready to shine?Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024
    Review
  13. [Sleep Innovation].Open respiratory archives · 2024
    Review
  14. Article
  15. Article
  16. Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?European respiratory review : an official journal of the European Respiratory Society · 2024
    Review
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nikesh DevaniThoracic Medicine, Royal Free London NHS Foundation Trust, London, UK.
Renard Xaviero Adhi PramonoWearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College of Science Technology and Medicine, London, UK.
Syed Anas ImtiazWearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College of Science Technology and Medicine, London, UK.
Stuart BowyerWearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College of Science Technology and Medicine, London, UK.
Esther Rodriguez-VillegasWearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College of Science Technology and Medicine, London, UK esther@imperial.ac.uk.ORCID 0000-0003-1957-2044
Swapna MandalThoracic Medicine, Royal Free London NHS Foundation Trust, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesObstructive sleep apnoea (OSA) is a heavily underdiagnosed condition, which can lead to significant multimorbidity. Underdiagnosis is often secondary to limitations in existing diagnostic methods. We conducted a diagnostic accuracy and usability study, to evaluate the efficacy of a novel, low-cost, small, wearable medical device, AcuPebble_SA100, for automated diagnosis of OSA in the home environment. SETTINGS: Patients were recruited to a standard OSA diagnostic pathway in an UK hospital. They were trained on the use of type-III-cardiorespiratory polygraphy, which they took to use at home. They were also given AcuPebble_SA100; but they were not trained on how to use it.

participants182 consecutive patients had been referred for OSA diagnosis in which 150 successfully completed the study. PRIMARY OUTCOME MEASURES: Efficacy of AcuPebble_SA100 for automated diagnosis of moderate-severe-OSA against cardiorespiratory polygraphy (sensitivity/specificity/likelihood ratios/predictive values) and validation of usability by patients themselves in their home environment.

resultsAfter returning the systems, two expert clinicians, blinded to AcuPebble_SA100's output, manually scored the cardiorespiratory polygraphy signals to reach a diagnosis. AcuPebble_SA100 generated automated diagnosis corresponding to four, typically followed, diagnostic criteria: Apnoea Hypopnoea Index (AHI) using 3% as criteria for oxygen desaturation; Oxygen Desaturation Index (ODI) for 3% and 4% desaturation criteria and AHI using 4% as desaturation criteria. In all cases, AcuPebble_SA100 matched the experts' diagnosis with positive and negative likelihood ratios over 10 and below 0.1, respectively. Comparing against the current American Academy of Sleep Medicine's AHI-based criteria demonstrated 95.33% accuracy (95% CI (90·62% to 98·10%)), 96.84% specificity (95% CI (91·05% to 99·34%)), 92.73% sensitivity (95% CI (82·41% to 97·98%)), 94.4% positive-predictive value (95% CI (84·78% to 98·11%)) and 95.83% negative-predictive value (95% CI (89·94% to 98·34%)). All patients used AcuPebble_SA100 correctly. Over 97% reported a strong preference for AcuPebble_SA100 over cardiorespiratory polygraphy.

conclusionsThese results validate the efficacy of AcuPebble_SA100 as an automated diagnosis alternative to cardiorespiratory polygraphy; also demonstrating that AcuPebble_SA100 can be used by patients without requiring human training/assistance. This opens the doors for more efficient patient pathways for OSA diagnosis. TRIAL REGISTRATION NUMBER: NCT03544086; ClinicalTrials.gov.

Indexed as

Home EnvironmentSleep Apnea, ObstructiveHumansPredictive Value of TestsSensitivity and SpecificitySleeprespiratory physiologysleep medicinetelemedicine

Identifiers

PMID34933855
PMCPMC8693096

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.