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Prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using Medicare real-world data

BACKGROUND: Suboptimal maintenance medication (MM) adherence remains a clinical problem among Medicare beneficiaries with chronic obstructive pulmonary disease (COPD). OBJECTIVE: To inform risk-based personalized decision-making, this study sought to develop and validate prediction models of nonadhe...

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Autores principales: Le, Tham T, Bjarnadóttir, Margrét, Qato, Danya M, Magder, Larry, Zafari, Zafar, Simoni-Wastila, Linda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academy of Managed Care Pharmacy 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373023/
https://www.ncbi.nlm.nih.gov/pubmed/35621722
http://dx.doi.org/10.18553/jmcp.2022.28.6.631
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author Le, Tham T
Bjarnadóttir, Margrét
Qato, Danya M
Magder, Larry
Zafari, Zafar
Simoni-Wastila, Linda
author_facet Le, Tham T
Bjarnadóttir, Margrét
Qato, Danya M
Magder, Larry
Zafari, Zafar
Simoni-Wastila, Linda
author_sort Le, Tham T
collection PubMed
description BACKGROUND: Suboptimal maintenance medication (MM) adherence remains a clinical problem among Medicare beneficiaries with chronic obstructive pulmonary disease (COPD). OBJECTIVE: To inform risk-based personalized decision-making, this study sought to develop and validate prediction models of nonadherence to COPD MMs for Medicare beneficiaries. METHODS: This was a retrospective cohort study of beneficiaries aged 65 years and older with COPD and inhaled MMs. Nonadherence (proportion of days covered < 0.8) was measured in 12 months following the first MM fill after COPD diagnosis. Logistic and least absolute shrinkage selector operator regressions were implemented, and area under the receiver operating characteristic curve (AUROC) evaluated model accuracy, as well as positive predictive values and negative predictive values. Our models evaluated different sets of predictors for two cohorts: those with an MM prescription before COPD diagnosis (prevalent users) and those without (new users). RESULTS: Among 16,157 prevalent and 40,279 new users of MMs, 11,271 (69.8%) and 34,009 (84.4%), respectively, were nonadherent. The best-performing logistic models achieved AUROCs of 0.8714 and 0.881, positive predictive values of 0.881 and 0.881, and negative predictive values of 0.559 and 0.578, respectively, for prevalent and new users. The least absolute shrinkage selector operator models had similar accuracy. Models with baseline-only predictors had average performance (AUROC < 0.72). The most important predictors were initial MM adherence, short-acting bronchodilator use, and asthma. CONCLUSIONS: To our knowledge, this study is the first to develop predictive models of nonadherence to COPD MMs. Generated models achieved good discrimination and underlined the importance of early adherence. Well-performed models can be useful for care decision-making and interventions to improve COPD medication adherence after the first critical few months following a treatment episode.
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spelling pubmed-103730232023-07-31 Prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using Medicare real-world data Le, Tham T Bjarnadóttir, Margrét Qato, Danya M Magder, Larry Zafari, Zafar Simoni-Wastila, Linda J Manag Care Spec Pharm Research BACKGROUND: Suboptimal maintenance medication (MM) adherence remains a clinical problem among Medicare beneficiaries with chronic obstructive pulmonary disease (COPD). OBJECTIVE: To inform risk-based personalized decision-making, this study sought to develop and validate prediction models of nonadherence to COPD MMs for Medicare beneficiaries. METHODS: This was a retrospective cohort study of beneficiaries aged 65 years and older with COPD and inhaled MMs. Nonadherence (proportion of days covered < 0.8) was measured in 12 months following the first MM fill after COPD diagnosis. Logistic and least absolute shrinkage selector operator regressions were implemented, and area under the receiver operating characteristic curve (AUROC) evaluated model accuracy, as well as positive predictive values and negative predictive values. Our models evaluated different sets of predictors for two cohorts: those with an MM prescription before COPD diagnosis (prevalent users) and those without (new users). RESULTS: Among 16,157 prevalent and 40,279 new users of MMs, 11,271 (69.8%) and 34,009 (84.4%), respectively, were nonadherent. The best-performing logistic models achieved AUROCs of 0.8714 and 0.881, positive predictive values of 0.881 and 0.881, and negative predictive values of 0.559 and 0.578, respectively, for prevalent and new users. The least absolute shrinkage selector operator models had similar accuracy. Models with baseline-only predictors had average performance (AUROC < 0.72). The most important predictors were initial MM adherence, short-acting bronchodilator use, and asthma. CONCLUSIONS: To our knowledge, this study is the first to develop predictive models of nonadherence to COPD MMs. Generated models achieved good discrimination and underlined the importance of early adherence. Well-performed models can be useful for care decision-making and interventions to improve COPD medication adherence after the first critical few months following a treatment episode. Academy of Managed Care Pharmacy 2022-06 /pmc/articles/PMC10373023/ /pubmed/35621722 http://dx.doi.org/10.18553/jmcp.2022.28.6.631 Text en Copyright © 2022, Academy of Managed Care Pharmacy. All rights reserved. https://creativecommons.org/licenses/by/4.0/This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Research
Le, Tham T
Bjarnadóttir, Margrét
Qato, Danya M
Magder, Larry
Zafari, Zafar
Simoni-Wastila, Linda
Prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using Medicare real-world data
title Prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using Medicare real-world data
title_full Prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using Medicare real-world data
title_fullStr Prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using Medicare real-world data
title_full_unstemmed Prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using Medicare real-world data
title_short Prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using Medicare real-world data
title_sort prediction of treatment nonadherence among older adults with chronic obstructive pulmonary disease using medicare real-world data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373023/
https://www.ncbi.nlm.nih.gov/pubmed/35621722
http://dx.doi.org/10.18553/jmcp.2022.28.6.631
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