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Predictive factors for CPAP failure in obstructive sleep apnea patients
OBJECTIVES: Some patients with obstructive sleep apnea (OSA) do not respond to Continuous Positive Airway Pressure (CPAP) and for these patients, Bi-level PAP is the next level modality. This study by a theory driven hierarchical approach, tries to identify the predictors for CPAP failure among OSA...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614613/ https://www.ncbi.nlm.nih.gov/pubmed/34747736 http://dx.doi.org/10.4103/lungindia.lungindia_867_20 |
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author | Goyal, Abhishek Joshi, Ankur Mitra, Arun Khurana, Alkesh Chaudhary, Poonam |
author_facet | Goyal, Abhishek Joshi, Ankur Mitra, Arun Khurana, Alkesh Chaudhary, Poonam |
author_sort | Goyal, Abhishek |
collection | PubMed |
description | OBJECTIVES: Some patients with obstructive sleep apnea (OSA) do not respond to Continuous Positive Airway Pressure (CPAP) and for these patients, Bi-level PAP is the next level modality. This study by a theory driven hierarchical approach, tries to identify the predictors for CPAP failure among OSA patients. METHODOLOGY: The potential predictors for the model were identified from a theoretical framework rooted in clinical examination, laboratory parameters, and polysomnographic variables pertaining to OSA patients. All patients of OSA who underwent manual titration with CPAP or Bi-level PAP (in case of CPAP Failure) between June 2015 and October 2017 were included in model building. This study compared five competitive models blocks deliberated by increasing order of diagnostic complexity and availability of resources. The fitting of the model was determined by both internal and external validation. RESULTS: Among the five competitive models, the selected model has the significant deviance reduction (−2LL = 121.99, X(2) = 25.55, P < 0.0001) from the baseline model (−2LL = 217.356). This logistic regression model consists of the following binary predictors – Age >60 years (odds ratio [OR] = 3.23 [1.27–8.23]), body mass index >35 Kg/m(2) (OR = 4.25 [1.78–10.13]), forced expiratory volume <60% (OR = 7.33 [2.83–18.72]), apnea-hypopnea index >75 (OR = 4.31 [1.61–11.56]) and T90 > 30% (OR = 6.67 [2.57–17.36]). CONCLUSION: These five factors (acronym as BIPAP) may aid to the clinical decision-making by predicting failure of CPAP and therefore may assist in more vigilant clinical care. |
format | Online Article Text |
id | pubmed-8614613 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-86146132021-12-13 Predictive factors for CPAP failure in obstructive sleep apnea patients Goyal, Abhishek Joshi, Ankur Mitra, Arun Khurana, Alkesh Chaudhary, Poonam Lung India Original Article OBJECTIVES: Some patients with obstructive sleep apnea (OSA) do not respond to Continuous Positive Airway Pressure (CPAP) and for these patients, Bi-level PAP is the next level modality. This study by a theory driven hierarchical approach, tries to identify the predictors for CPAP failure among OSA patients. METHODOLOGY: The potential predictors for the model were identified from a theoretical framework rooted in clinical examination, laboratory parameters, and polysomnographic variables pertaining to OSA patients. All patients of OSA who underwent manual titration with CPAP or Bi-level PAP (in case of CPAP Failure) between June 2015 and October 2017 were included in model building. This study compared five competitive models blocks deliberated by increasing order of diagnostic complexity and availability of resources. The fitting of the model was determined by both internal and external validation. RESULTS: Among the five competitive models, the selected model has the significant deviance reduction (−2LL = 121.99, X(2) = 25.55, P < 0.0001) from the baseline model (−2LL = 217.356). This logistic regression model consists of the following binary predictors – Age >60 years (odds ratio [OR] = 3.23 [1.27–8.23]), body mass index >35 Kg/m(2) (OR = 4.25 [1.78–10.13]), forced expiratory volume <60% (OR = 7.33 [2.83–18.72]), apnea-hypopnea index >75 (OR = 4.31 [1.61–11.56]) and T90 > 30% (OR = 6.67 [2.57–17.36]). CONCLUSION: These five factors (acronym as BIPAP) may aid to the clinical decision-making by predicting failure of CPAP and therefore may assist in more vigilant clinical care. Wolters Kluwer - Medknow 2021 2021-10-26 /pmc/articles/PMC8614613/ /pubmed/34747736 http://dx.doi.org/10.4103/lungindia.lungindia_867_20 Text en Copyright: © 2021 Indian Chest Society https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Original Article Goyal, Abhishek Joshi, Ankur Mitra, Arun Khurana, Alkesh Chaudhary, Poonam Predictive factors for CPAP failure in obstructive sleep apnea patients |
title | Predictive factors for CPAP failure in obstructive sleep apnea patients |
title_full | Predictive factors for CPAP failure in obstructive sleep apnea patients |
title_fullStr | Predictive factors for CPAP failure in obstructive sleep apnea patients |
title_full_unstemmed | Predictive factors for CPAP failure in obstructive sleep apnea patients |
title_short | Predictive factors for CPAP failure in obstructive sleep apnea patients |
title_sort | predictive factors for cpap failure in obstructive sleep apnea patients |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614613/ https://www.ncbi.nlm.nih.gov/pubmed/34747736 http://dx.doi.org/10.4103/lungindia.lungindia_867_20 |
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