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A predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study

BACKGROUND: Despite the growing evidence base supporting intensive lifestyle and medical treatments for severe obesity, patient engagement in specialist obesity services is difficult to achieve and poorly understood. To address this knowledge gap, we aimed to develop a model for predicting non-compl...

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Autores principales: Atlantis, Evan, Lin, Fang, Anandabaskaran, Sulak, Fahey, Paul, Kormas, Nic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814104/
https://www.ncbi.nlm.nih.gov/pubmed/31651309
http://dx.doi.org/10.1186/s12913-019-4531-1
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author Atlantis, Evan
Lin, Fang
Anandabaskaran, Sulak
Fahey, Paul
Kormas, Nic
author_facet Atlantis, Evan
Lin, Fang
Anandabaskaran, Sulak
Fahey, Paul
Kormas, Nic
author_sort Atlantis, Evan
collection PubMed
description BACKGROUND: Despite the growing evidence base supporting intensive lifestyle and medical treatments for severe obesity, patient engagement in specialist obesity services is difficult to achieve and poorly understood. To address this knowledge gap, we aimed to develop a model for predicting non-completion of a specialist multidisciplinary service for clinically severe obesity, termed the Metabolic Rehabilitation Programme (MRP). METHOD: Using a case-control study design in a public hospital setting, we extracted data from medical records for all eligible patients with a body mass index (BMI) of ≥35 kg/m(2) with either type 2 diabetes or fatty liver disease referred to the MRP from 2010 through 2015. Non-completion status (case definition) was coded for patients whom started but dropped-out of the MRP within 12 months. Using multivariable logistic regression, we tested the following baseline predictors hypothesised in previous research: age, gender, BMI, waist circumference, residential distance from the clinic, blood pressure, obstructive sleep apnoea (OSA), current continuous positive airway pressure (CPAP) therapy, current depression/anxiety, diabetes status, and medications. We used receiver operating characteristics and area under the curve to test the performance of models. RESULTS: Out of the 219 eligible patient records, 78 (35.6%) non-completion cases were identified. Significant differences between non-completers versus completers were: age (47.1 versus 54.5 years, p < 0.001); residential distance from the clinic (21.8 versus 17.1 km, p = 0.018); obstructive sleep apnoea (OSA) (42.9% versus 56.7%, p = 0.050) and CPAP therapy (11.7% versus 28.4%, p = 0.005). The probability of non-completion could be independently associated with age, residential distance, and either OSA or CPAP. There was no statistically significant difference in performance between the alternate models (69.5% versus 66.4%, p = 0.57). CONCLUSIONS: Non-completion of intensive specialist obesity management services is most common among younger patients, with fewer complex care needs, and those living further away from the clinic. Clinicians should be aware of these potential risk factors for dropping out early when managing outpatients with severe obesity, whereas policy makers might consider strategies for increasing access to specialist obesity management services.
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spelling pubmed-68141042019-10-31 A predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study Atlantis, Evan Lin, Fang Anandabaskaran, Sulak Fahey, Paul Kormas, Nic BMC Health Serv Res Research Article BACKGROUND: Despite the growing evidence base supporting intensive lifestyle and medical treatments for severe obesity, patient engagement in specialist obesity services is difficult to achieve and poorly understood. To address this knowledge gap, we aimed to develop a model for predicting non-completion of a specialist multidisciplinary service for clinically severe obesity, termed the Metabolic Rehabilitation Programme (MRP). METHOD: Using a case-control study design in a public hospital setting, we extracted data from medical records for all eligible patients with a body mass index (BMI) of ≥35 kg/m(2) with either type 2 diabetes or fatty liver disease referred to the MRP from 2010 through 2015. Non-completion status (case definition) was coded for patients whom started but dropped-out of the MRP within 12 months. Using multivariable logistic regression, we tested the following baseline predictors hypothesised in previous research: age, gender, BMI, waist circumference, residential distance from the clinic, blood pressure, obstructive sleep apnoea (OSA), current continuous positive airway pressure (CPAP) therapy, current depression/anxiety, diabetes status, and medications. We used receiver operating characteristics and area under the curve to test the performance of models. RESULTS: Out of the 219 eligible patient records, 78 (35.6%) non-completion cases were identified. Significant differences between non-completers versus completers were: age (47.1 versus 54.5 years, p < 0.001); residential distance from the clinic (21.8 versus 17.1 km, p = 0.018); obstructive sleep apnoea (OSA) (42.9% versus 56.7%, p = 0.050) and CPAP therapy (11.7% versus 28.4%, p = 0.005). The probability of non-completion could be independently associated with age, residential distance, and either OSA or CPAP. There was no statistically significant difference in performance between the alternate models (69.5% versus 66.4%, p = 0.57). CONCLUSIONS: Non-completion of intensive specialist obesity management services is most common among younger patients, with fewer complex care needs, and those living further away from the clinic. Clinicians should be aware of these potential risk factors for dropping out early when managing outpatients with severe obesity, whereas policy makers might consider strategies for increasing access to specialist obesity management services. BioMed Central 2019-10-24 /pmc/articles/PMC6814104/ /pubmed/31651309 http://dx.doi.org/10.1186/s12913-019-4531-1 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Atlantis, Evan
Lin, Fang
Anandabaskaran, Sulak
Fahey, Paul
Kormas, Nic
A predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study
title A predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study
title_full A predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study
title_fullStr A predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study
title_full_unstemmed A predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study
title_short A predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study
title_sort predictive model for non-completion of an intensive specialist obesity service in a public hospital: a case-control study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814104/
https://www.ncbi.nlm.nih.gov/pubmed/31651309
http://dx.doi.org/10.1186/s12913-019-4531-1
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