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Predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment

To expand our prior statewide analysis of care distribution for locally advanced cervical cancer in Virginia to include 2 more states and to develop a tool for predicting quality of care. Complete treatment was defined as receiving chemotherapy (CT), brachytherapy (BT), and external beam radiotherap...

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Autores principales: Schad, Michael D., Moore, Joanna, Camacho, Fabian, Anderson, Roger T., Cantrell, Leigh A., Showalter, Timothy N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6831444/
https://www.ncbi.nlm.nih.gov/pubmed/31415427
http://dx.doi.org/10.1097/MD.0000000000016874
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author Schad, Michael D.
Moore, Joanna
Camacho, Fabian
Anderson, Roger T.
Cantrell, Leigh A.
Showalter, Timothy N.
author_facet Schad, Michael D.
Moore, Joanna
Camacho, Fabian
Anderson, Roger T.
Cantrell, Leigh A.
Showalter, Timothy N.
author_sort Schad, Michael D.
collection PubMed
description To expand our prior statewide analysis of care distribution for locally advanced cervical cancer in Virginia to include 2 more states and to develop a tool for predicting quality of care. Complete treatment was defined as receiving chemotherapy (CT), brachytherapy (BT), and external beam radiotherapy. State cancer registry databases yielded a three-state cohort of 3197 women diagnosed with locally advanced cervical cancer from 2000 to 2013. A logistic regression evaluated predictors for receipt of BT, CT, and high (2–3 modalities received) versus low (0–1 modalities received) quality care. A Cox proportional hazards models determined predictors of survival. Finally, a predictive model was developed and preliminarily validated using our cohort. Only 35.3% of the cohort received complete treatment and only 57.3% received BT. Significant predictors of lower odds of receiving high quality care varied by state but included: 66+ age at diagnosis as compared to 18 to 42, 42 to 53, or 53 to 66; cancer stage IVA as compared to IIIx, IIx, or IB2; public insurance with supplement as compared to private; treatment at a low volume facility; and closer distance quintiles to a high volume treatment center as compared to the furthest quintile. Significant predictors of worse survival varied by state but included: low quality score (0–1 modalities received); 2000 to 2004 or 2005 to 2009 year of diagnosis as compared to 2010 to 2013; 66+ age at diagnosis as compared to 18 to 42, 42 to 53, or 53 to 66; cancer stage IVA as compared to IIIx, IIx, or IB2; treatment at a low volume facility; and unmarried/unknown marital status as compared to married. Our treatment quality prediction tool included age, age(2), treatment at high volume facility, and cancer stage and demonstrated 78.2% sensitivity and a 62.9% specificity. Only 35.3% of patients received complete guidelines-concordant treatment. Additionally, in 2/3 states it appeared that BT usage may have decreased during the study period. Our predictive model may help identify patients/regions at risk of receiving low quality care to target interventions aimed at improving cervical cancer treatment quality and survival.
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spelling pubmed-68314442019-11-19 Predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment Schad, Michael D. Moore, Joanna Camacho, Fabian Anderson, Roger T. Cantrell, Leigh A. Showalter, Timothy N. Medicine (Baltimore) 5700 To expand our prior statewide analysis of care distribution for locally advanced cervical cancer in Virginia to include 2 more states and to develop a tool for predicting quality of care. Complete treatment was defined as receiving chemotherapy (CT), brachytherapy (BT), and external beam radiotherapy. State cancer registry databases yielded a three-state cohort of 3197 women diagnosed with locally advanced cervical cancer from 2000 to 2013. A logistic regression evaluated predictors for receipt of BT, CT, and high (2–3 modalities received) versus low (0–1 modalities received) quality care. A Cox proportional hazards models determined predictors of survival. Finally, a predictive model was developed and preliminarily validated using our cohort. Only 35.3% of the cohort received complete treatment and only 57.3% received BT. Significant predictors of lower odds of receiving high quality care varied by state but included: 66+ age at diagnosis as compared to 18 to 42, 42 to 53, or 53 to 66; cancer stage IVA as compared to IIIx, IIx, or IB2; public insurance with supplement as compared to private; treatment at a low volume facility; and closer distance quintiles to a high volume treatment center as compared to the furthest quintile. Significant predictors of worse survival varied by state but included: low quality score (0–1 modalities received); 2000 to 2004 or 2005 to 2009 year of diagnosis as compared to 2010 to 2013; 66+ age at diagnosis as compared to 18 to 42, 42 to 53, or 53 to 66; cancer stage IVA as compared to IIIx, IIx, or IB2; treatment at a low volume facility; and unmarried/unknown marital status as compared to married. Our treatment quality prediction tool included age, age(2), treatment at high volume facility, and cancer stage and demonstrated 78.2% sensitivity and a 62.9% specificity. Only 35.3% of patients received complete guidelines-concordant treatment. Additionally, in 2/3 states it appeared that BT usage may have decreased during the study period. Our predictive model may help identify patients/regions at risk of receiving low quality care to target interventions aimed at improving cervical cancer treatment quality and survival. Wolters Kluwer Health 2019-08-16 /pmc/articles/PMC6831444/ /pubmed/31415427 http://dx.doi.org/10.1097/MD.0000000000016874 Text en Copyright © 2019 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0
spellingShingle 5700
Schad, Michael D.
Moore, Joanna
Camacho, Fabian
Anderson, Roger T.
Cantrell, Leigh A.
Showalter, Timothy N.
Predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment
title Predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment
title_full Predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment
title_fullStr Predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment
title_full_unstemmed Predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment
title_short Predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment
title_sort predictors of quality of care and survival in a three-state cohort of locally advanced cervical cancer patients and development of a predictive model to identify women at risk of incomplete treatment
topic 5700
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6831444/
https://www.ncbi.nlm.nih.gov/pubmed/31415427
http://dx.doi.org/10.1097/MD.0000000000016874
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