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Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography.
Dietz-Terjung, Sarah; Martin, Amelie Ricarda; Finnsson, Eysteinn; Ágústsson, Jón Skínir; Helgason, Snorri; Helgadóttir, Halla; Welsner, Matthias; Taube, Christian; Weinreich, Gerhard; Schöbel, Christoph.
Afiliação
  • Dietz-Terjung S; Faculty of Sleep Medicine and Telemedicine, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany. sarah.terjung@rlk.uk-essen.de.
  • Martin AR; Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany. sarah.terjung@rlk.uk-essen.de.
  • Finnsson E; Faculty of Sleep Medicine and Telemedicine, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.
  • Ágústsson JS; Nox Research, Nox Medical ehf, Reykjavík, Iceland.
  • Helgason S; Nox Research, Nox Medical ehf, Reykjavík, Iceland.
  • Helgadóttir H; Nox Research, Nox Medical ehf, Reykjavík, Iceland.
  • Welsner M; Nox Research, Nox Medical ehf, Reykjavík, Iceland.
  • Taube C; Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.
  • Weinreich G; Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.
  • Schöbel C; Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.
Sleep Breath ; 25(4): 1945-1952, 2021 12.
Article em En | MEDLINE | ID: mdl-33594617
ABSTRACT

PURPOSE:

In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleepTM 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB).

METHODS:

Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0).

RESULTS:

We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen's kappa of 0.62 was found for this 3-class classification problem.

CONCLUSION:

The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pletismografia / Síndromes da Apneia do Sono / Fases do Sono / Índice de Gravidade de Doença / Algoritmos / Polissonografia / Actigrafia Idioma: En Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pletismografia / Síndromes da Apneia do Sono / Fases do Sono / Índice de Gravidade de Doença / Algoritmos / Polissonografia / Actigrafia Idioma: En Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Alemanha