Literature DB >> 27166303

Residual Events during Use of CPAP: Prevalence, Predictors, and Detection Accuracy.

Joel Reiter1,2, Bashar Zleik1,3, Mihaela Bazalakova1,4, Pankaj Mehta1,5, Robert Joseph Thomas1.   

Abstract

STUDY
OBJECTIVES: To assess the frequency, severity, and determinants of residual respiratory events during continuous positive airway therapy (CPAP) for obstructive sleep apnea (OSA) as determined by device output.
METHODS: Subjects were consecutive OSA patients at an American Academy of Sleep Medicine accredited multidisciplinary sleep center. Inclusion criteria included CPAP use for a minimum of 3 months, and a minimum nightly use of 4 hours. Compliance metrics and waveform data from 217 subjects were analyzed retrospectively. Events were scored manually when there was a clear reduction of amplitude (≥ 30%) or flow-limitation with 2-3 larger recovery breaths. Automatically detected versus manually scored events were subjected to statistical analyses included Bland-Altman plots, correlation coefficients, and logistic regression exploring predictors of residual events.
RESULTS: The mean patient age was 54.7 ± 14.2 years; 63% were males. All patients had a primary diagnosis of obstructive sleep apnea, 26% defined as complex sleep apnea. Residual flow measurement based apnea-hypopnea index (AHIFLOW) > 5, 10, and 15/h was seen in 32.3%, 9.7%, and 1.8% vs. 60.8%, 23%, and 7.8% of subjects based on automated vs. manual scoring of waveform data. Automatically detected versus manually scored average AHIFLOW was 4.4 ± 3.8 vs. 7.3 ± 5.1 per hour. In a logistic regression analysis, the only predictors for a manual AHIFLOW > 5/h were the absolute central apnea index (CAI), (odds ratio [OR]: 1.5, p: 0.01, CI: 1.1-2.0), or using a CAI threshold of 5/h of sleep (OR: 5.0, p: < 0.001, CI: 2.2-13.8). For AHIFLOW > 10/h, the OR was 1.14, p: 0.03 (CI: 1.1-1.3) per every CAI unit of 1/hour.
CONCLUSIONS: Residual respiratory events are common during CPAP treatment, may be missed by automated device detection and predicted by a high central apnea index on the baseline diagnostic study. Direct visualization of flow data is generally available and improves detection.
© 2016 American Academy of Sleep Medicine.

Entities:  

Keywords:  auto-CPAP; residual apnea; sleep

Mesh:

Year:  2016        PMID: 27166303      PMCID: PMC4957193          DOI: 10.5664/jcsm.6056

Source DB:  PubMed          Journal:  J Clin Sleep Med        ISSN: 1550-9389            Impact factor:   4.062


  11 in total

Review 1.  Improving CPAP use by patients with the sleep apnoea/hypopnoea syndrome (SAHS).

Authors:  Heather M Engleman; Matt R Wild
Journal:  Sleep Med Rev       Date:  2003-02       Impact factor: 11.609

2.  The accuracy of autotitrating CPAP-determined residual apnea-hypopnea index.

Authors:  Aykut Cilli; Rusen Uzun; Ugur Bilge
Journal:  Sleep Breath       Date:  2012-02-28       Impact factor: 2.816

3.  Respiratory event detection by a positive airway pressure device.

Authors:  Richard B Berry; Clete A Kushida; Meir H Kryger; Haideliza Soto-Calderon; Bethany Staley; Samuel T Kuna
Journal:  Sleep       Date:  2012-03-01       Impact factor: 5.849

4.  Evaluation of the apnea-hypopnea index determined by the S8 auto-CPAP, a continuous positive airway pressure device, in patients with obstructive sleep apnea-hypopnea syndrome.

Authors:  Kanako Ueno; Takatoshi Kasai; Gregory Brewer; Hisashi Takaya; Ken-ichi Maeno; Satoshi Kasagi; Fusae Kawana; Sugao Ishiwata; Koji Narui
Journal:  J Clin Sleep Med       Date:  2010-04-15       Impact factor: 4.062

5.  Comparison between the apnea-hypopnea indices determined by the REMstar Auto M series and those determined by standard in-laboratory polysomnography in patients with obstructive sleep apnea.

Authors:  Yukiko Ikeda; Takatoshi Kasai; Fusae Kawana; Satoshi Kasagi; Hisashi Takaya; Sugao Ishiwata; Koji Narui
Journal:  Intern Med       Date:  2012-10-15       Impact factor: 1.271

6.  Air leak during CPAP titration as a risk factor for central apnea.

Authors:  Sydney B Montesi; Jessie P Bakker; Mary Macdonald; Lauren Hueser; Stephen Pittman; David P White; Atul Malhotra
Journal:  J Clin Sleep Med       Date:  2013-11-15       Impact factor: 4.062

7.  The occurrence of sleep-disordered breathing among middle-aged adults.

Authors:  T Young; M Palta; J Dempsey; J Skatrud; S Weber; S Badr
Journal:  N Engl J Med       Date:  1993-04-29       Impact factor: 91.245

8.  Accuracy of autotitrating CPAP to estimate the residual Apnea-Hypopnea Index in patients with obstructive sleep apnea on treatment with autotitrating CPAP.

Authors:  Himanshu Desai; Anil Patel; Pinal Patel; Brydon J B Grant; M Jeffery Mador
Journal:  Sleep Breath       Date:  2009-05-01       Impact factor: 2.816

9.  Continuous positive airway pressure device-based automated detection of obstructive sleep apnea compared to standard laboratory polysomnography.

Authors:  Bharati Prasad; David W Carley; James J Herdegen
Journal:  Sleep Breath       Date:  2009-10-14       Impact factor: 2.816

10.  Control of OSA during automatic positive airway pressure titration in a clinical case series: predictors and accuracy of device download data.

Authors:  Hsin-Chia Carol Huang; David R Hillman; Nigel McArdle
Journal:  Sleep       Date:  2012-09-01       Impact factor: 5.849

View more
  12 in total

1.  Utility of home sleep apnea testing devices in patients with cardiac conditions-critical manual interpretation of the raw data is important.

Authors:  Mukesh Kapoor
Journal:  Sleep Breath       Date:  2019-03-14       Impact factor: 2.816

2.  Urgent Need to Improve PAP Management: The Devil Is in Two (Fixable) Details.

Authors:  Robert J Thomas; Matt T Bianchi
Journal:  J Clin Sleep Med       Date:  2017-05-15       Impact factor: 4.062

3.  Man Versus Machine.

Authors:  Christine H Won
Journal:  J Clin Sleep Med       Date:  2017-02-15       Impact factor: 4.062

4.  DISE-PAP: a method for troubleshooting residual AHI elevation despite positive pressure therapy.

Authors:  Monika E Freiser; Amy E Schell; Ryan J Soose
Journal:  J Clin Sleep Med       Date:  2020-04-15       Impact factor: 4.062

5.  A longitudinal study of the accuracy of positive airway pressure therapy machine-detected apnea-hypopnea events.

Authors:  Yue-Nan Ni; Robert Joseph Thomas
Journal:  J Clin Sleep Med       Date:  2022-04-01       Impact factor: 4.062

6.  Use of the WatchPAT to detect occult residual sleep-disordered breathing in patients on CPAP for obstructive sleep apnea.

Authors:  Matthew Epstein; Tariq Musa; Stephanie Chiu; Jacquelyn Costanzo; Christine Dunne; Federico Cerrone; Robert Capone
Journal:  J Clin Sleep Med       Date:  2020-07-15       Impact factor: 4.062

7.  Interpreting CPAP device respiratory indices in children.

Authors:  Rebecca Mihai; Kirsten Ellis; Margot J Davey; Gillian M Nixon
Journal:  J Clin Sleep Med       Date:  2020-10-15       Impact factor: 4.062

8.  Electronic health record-derived outcomes in obstructive sleep apnea managed with positive airway pressure tracking systems.

Authors:  Brian W Locke; Sarah E Neill; Heather E Howe; Michael C Crotty; Jaewhan Kim; Krishna M Sundar
Journal:  J Clin Sleep Med       Date:  2022-03-01       Impact factor: 4.062

Review 9.  Use of polysomnography and home sleep apnea tests for the longitudinal management of obstructive sleep apnea in adults: an American Academy of Sleep Medicine clinical guidance statement.

Authors:  Sean M Caples; W McDowell Anderson; Karel Calero; Michael Howell; Sarah D Hashmi
Journal:  J Clin Sleep Med       Date:  2021-06-01       Impact factor: 4.324

Review 10.  Sleep Disturbances as a Risk Factor for Stroke.

Authors:  Dae Lim Koo; Hyunwoo Nam; Robert J Thomas; Chang-Ho Yun
Journal:  J Stroke       Date:  2018-01-31       Impact factor: 6.967

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.