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Forbes R, Duce B, Hukins C, Ellender C. Factors associated with noninvasive ventilation usage in patients with hypoventilation disorders. SLEEP ADVANCES : A JOURNAL OF THE SLEEP RESEARCH SOCIETY 2024; 5:zpae046. [PMID: 39099832 PMCID: PMC11296755 DOI: 10.1093/sleepadvances/zpae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 06/27/2024] [Indexed: 08/06/2024]
Abstract
Study Objectives The objective of this study was to investigate the association between demographic, clinical, and interface factors and noninvasive ventilation (NIV) usage. Methods A retrospective cohort analysis of 478 patients prescribed NIV from 2013 to 2021 was performed. Demographic factors, clinical indications for NIV, and interface factors were collected, and linear regression was conducted to evaluate the association between these variables and NIV usage (hour/night). Results The average usage of the cohort was 6.5 hour/night ± 4.6, with an average age of 57 years ± 16 and body mass index (BMI) of 40.5kg/m2 ± 14.7. The cohort was mostly male (n = 290, 60.6%). The most common indications for NIV prescription were high-pressure requirement for obstructive sleep apnea (HPR, n = 190, 39.7%), neuromuscular disease (NMD, n = 140, 29.3%), and obesity hypoventilation syndrome (OHS, n = 111, 23.2%). A diagnosis of NMD was a significant predictor of higher NIV usage (8.0 ± 6.1 hour/night) in multivariate analysis (p = .036). The HPR subcohort had the lowest usage of all indications. Age and BMI did not predict usage. A nasal interface (p < .01) and lower expiratory positive airway pressure (EPAP) setting (p < .001) were associated with increased NIV usage. Conclusions This study highlights the multifaceted nature of NIV usage. Where demographic factors were not consistent predictors of usage, interface, and clinical indication were associated with usage. These findings highlight that the HPR users are a group at risk of low usage.
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Affiliation(s)
- Riley Forbes
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- Department of Respiratory & Sleep Medicine, Princess Alexandra Hospital, Brisbane, Australia
| | - Brett Duce
- Department of Respiratory & Sleep Medicine, Princess Alexandra Hospital, Brisbane, Australia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Craig Hukins
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- Department of Respiratory & Sleep Medicine, Princess Alexandra Hospital, Brisbane, Australia
| | - Claire Ellender
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- Department of Respiratory & Sleep Medicine, Princess Alexandra Hospital, Brisbane, Australia
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McDowell G, Sumowski M, Toellner H, Karok S, O'Dwyer C, Hornsby J, Lowe DJ, Carlin CM. Assistive technologies for home NIV in patients with COPD: feasibility and positive experience with remote-monitoring and volume-assured auto-EPAP NIV mode. BMJ Open Respir Res 2021; 8:8/1/e000828. [PMID: 34782327 PMCID: PMC8593724 DOI: 10.1136/bmjresp-2020-000828] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 10/20/2021] [Indexed: 12/04/2022] Open
Abstract
Background Outcomes for patients with chronic obstructive pulmonary disease (COPD) with persistent hypercapnic respiratory failure are improved by long-term home non-invasive ventilation (NIV). Provision of home-NIV presents clinical and service challenges. The aim of this study was to evaluate outcomes of home-NIV in hypercapnic patients with COPD who had been set-up at our centre using remote-monitoring and iVAPS-autoEPAP NIV mode (Lumis device, ResMed). Methods Retrospective analysis of a data set of 46 patients with COPD who commenced remote-monitored home-NIV (AirView, ResMed) between February 2017 and January 2018. Events including time to readmission or death at 12 months were compared with a retrospectively identified cohort of 27 patients with hypercapnic COPD who had not been referred for consideration of home-NIV. Results The median time to readmission or death was significantly prolonged in patients who commenced home-NIV (median 160 days, 95% CI 69.38 to 250.63) versus the comparison cohort (66 days, 95% CI 21.9 to 110.1; p<0.01). Average time to hospital readmission was 221 days (95% CI, 47.77 to 394.23) and 70 days (95% CI, 55.31 to 84.69; p<0.05), respectively. Median decrease in bicarbonate level of 4.9 mmol/L (p<0.0151) and daytime partial pressure of carbon dioxide 2.2 kPa (p<0.032) in home-NIV patients with no required increase in nurse home visits is compatible with effectiveness of this service model. Median reduction of 14 occupied bed days per annum was observed per patient who continued home-NIV throughout the study period (N=32). Conclusion These findings demonstrate the feasibility and provide initial utility data for a technology-assisted service model for the provision of home-NIV therapy for patients with COPD.
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Affiliation(s)
- Grace McDowell
- Respiratory Medicine, Queen Elizabeth University Hospital, Glasgow, UK
| | | | - Hannah Toellner
- Respiratory Medicine, Queen Elizabeth University Hospital, Glasgow, UK
| | - Sophia Karok
- ResMed Data Solutions, ResMed Science Centre, Dublin, Ireland
| | - Ciara O'Dwyer
- ResMed Data Solutions, ResMed Science Centre, Dublin, Ireland
| | - James Hornsby
- Respiratory Medicine, Queen Elizabeth University Hospital, Glasgow, UK
| | - David J Lowe
- Respiratory Medicine, Queen Elizabeth University Hospital, Glasgow, UK
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Peng Q, Zhang N, Yu H, Shao Y, Ji Y, Jin Y, Zhong P, Zhang Y, Jiang H, Li C, Shi Y, Zheng Y, Xiong Y, Wang Z, Jiang F, Chen Y, Jiang Q, Zhou Y. Geographical Variation of COPD Mortality and Related Risk Factors in Jiading District, Shanghai. Front Public Health 2021; 9:627312. [PMID: 33614588 PMCID: PMC7888271 DOI: 10.3389/fpubh.2021.627312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 01/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in China. Although numerous studies have been conducted to determine the risk factors for COPD mortality such as ambient air pollution, the results are not fully consistent. Methods: This study included mortality analysis and a case-control design by using the data extracted from the Mortality Registration System in Jiading District, Shanghai. Traditional logistic regression, geographically weighted logistic regression (GWLR), and spatial scan statistical analysis were performed to explore the geographic variation of COPD mortality and the possible influencing factors. Results: Traditional logistic regression showed that extreme lower temperature in the month prior to death, shorter distance to highway, lower GDP level were associated with increased COPD mortality. GWRL model further demonstrated obvious geographical discrepancies for the above associations. We additionally identified a significant cluster of low COPD mortality (OR = 0.36, P = 0.002) in the southwest region of Jiading District with a radius of 3.55 km by using the Bernoulli model. The geographical variation in age-standardized mortality rate for COPD in Jiading District was explained to a certain degree by these factors. Conclusion: The risk of COPD mortality in Jiading District showed obvious geographical variation, which were partially explained by the geographical variations in effects of the extreme low temperature in the month prior to death, residential proximity to highway, and GDP level.
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Affiliation(s)
- Qian Peng
- Jiading District Center for Disease Control and Prevention, Shanghai, China
| | - Na Zhang
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Hongjie Yu
- Jiading District Center for Disease Control and Prevention, Shanghai, China
| | - Yueqin Shao
- Jiading District Center for Disease Control and Prevention, Shanghai, China
| | - Ying Ji
- Jiading District Center for Disease Control and Prevention, Shanghai, China
| | - Yaqing Jin
- Jiading District Center for Disease Control and Prevention, Shanghai, China
| | - Peisong Zhong
- Jiading District Center for Disease Control and Prevention, Shanghai, China
| | - Yiying Zhang
- Jiading District Center for Disease Control and Prevention, Shanghai, China
| | - Honglin Jiang
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Chunlin Li
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Ying Shi
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Yingyan Zheng
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Ying Xiong
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Zhengzhong Wang
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Feng Jiang
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Yue Chen
- Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Qingwu Jiang
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
| | - Yibiao Zhou
- Fudan University School of Public Health, Shanghai, China.,Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.,Fudan University Center for Tropical Disease Research, Shanghai, China
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