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Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain

BACKGROUND: Studies in chronic noncancer pain settings have found that opioid use increases health care utilization. Despite the key role of analgesics, specifically opioids, in the setting of cancer pain, there is no literature to our knowledge about the relationship between adherence to prescribed...

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Autores principales: Meghani, Salimah H, Knafl, George J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734825/
https://www.ncbi.nlm.nih.gov/pubmed/26869772
http://dx.doi.org/10.2147/PPA.S93726
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author Meghani, Salimah H
Knafl, George J
author_facet Meghani, Salimah H
Knafl, George J
author_sort Meghani, Salimah H
collection PubMed
description BACKGROUND: Studies in chronic noncancer pain settings have found that opioid use increases health care utilization. Despite the key role of analgesics, specifically opioids, in the setting of cancer pain, there is no literature to our knowledge about the relationship between adherence to prescribed around-the-clock (ATC) analgesics and acute health care utilization (hospitalization) among patients with cancer pain. PURPOSE: To identify adherence patterns over time for cancer patients taking ATC analgesics for pain, cluster these patterns into adherence types, combine the types into an adherence risk factor for hospitalization, identify other risk factors for hospitalization, and identify risk factors for inconsistent analgesic adherence. MATERIALS AND METHODS: Data from a 3-month prospective observational study of patients diagnosed with solid tumors or multiple myeloma, having cancer-related pain, and having at least one prescription of oral ATC analgesics were collected. Adherence data were collected electronically using the medication event-monitoring system. Analyses were conducted using adaptive modeling methods based on heuristic search through alternative models controlled by likelihood cross-validation scores. RESULTS: Six adherence types were identified and combined into the risk factor for hospitalization of inconsistent versus consistent adherence over time. Twenty other individually significant risk factors for hospitalization were identified, but inconsistent analgesic adherence was the strongest of these predictors (ie, generating the largest likelihood cross-validation score). These risk factors were adaptively combined into a model for hospitalization based on six pairwise interaction risk factors with exceptional discrimination (ie, area under the receiver-operating-characteristic curve of 0.91). Patients had from zero to five of these risk factors, with an odds ratio of 5.44 (95% confidence interval 3.09–9.58) for hospitalization, with a unit increase in the number of such risk factors. CONCLUSION: Inconsistent adherence to prescribed ATC analgesics, specifically the interaction of strong opioids and inconsistent adherence, is a strong risk factor for hospitalization among cancer outpatients with pain.
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spelling pubmed-47348252016-02-11 Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain Meghani, Salimah H Knafl, George J Patient Prefer Adherence Original Research BACKGROUND: Studies in chronic noncancer pain settings have found that opioid use increases health care utilization. Despite the key role of analgesics, specifically opioids, in the setting of cancer pain, there is no literature to our knowledge about the relationship between adherence to prescribed around-the-clock (ATC) analgesics and acute health care utilization (hospitalization) among patients with cancer pain. PURPOSE: To identify adherence patterns over time for cancer patients taking ATC analgesics for pain, cluster these patterns into adherence types, combine the types into an adherence risk factor for hospitalization, identify other risk factors for hospitalization, and identify risk factors for inconsistent analgesic adherence. MATERIALS AND METHODS: Data from a 3-month prospective observational study of patients diagnosed with solid tumors or multiple myeloma, having cancer-related pain, and having at least one prescription of oral ATC analgesics were collected. Adherence data were collected electronically using the medication event-monitoring system. Analyses were conducted using adaptive modeling methods based on heuristic search through alternative models controlled by likelihood cross-validation scores. RESULTS: Six adherence types were identified and combined into the risk factor for hospitalization of inconsistent versus consistent adherence over time. Twenty other individually significant risk factors for hospitalization were identified, but inconsistent analgesic adherence was the strongest of these predictors (ie, generating the largest likelihood cross-validation score). These risk factors were adaptively combined into a model for hospitalization based on six pairwise interaction risk factors with exceptional discrimination (ie, area under the receiver-operating-characteristic curve of 0.91). Patients had from zero to five of these risk factors, with an odds ratio of 5.44 (95% confidence interval 3.09–9.58) for hospitalization, with a unit increase in the number of such risk factors. CONCLUSION: Inconsistent adherence to prescribed ATC analgesics, specifically the interaction of strong opioids and inconsistent adherence, is a strong risk factor for hospitalization among cancer outpatients with pain. Dove Medical Press 2016-01-27 /pmc/articles/PMC4734825/ /pubmed/26869772 http://dx.doi.org/10.2147/PPA.S93726 Text en © 2016 Meghani and Knafl. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Meghani, Salimah H
Knafl, George J
Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain
title Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain
title_full Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain
title_fullStr Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain
title_full_unstemmed Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain
title_short Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain
title_sort patterns of analgesic adherence predict health care utilization among outpatients with cancer pain
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734825/
https://www.ncbi.nlm.nih.gov/pubmed/26869772
http://dx.doi.org/10.2147/PPA.S93726
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