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Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines
BACKGROUND: Risk-stratified treatment recommendations facilitate treatment decision-making that balances patient-specific risks and preferences. It is unclear if and how such recommendations are developed in clinical practice guidelines (CPGs). Our aim was to assess if and how CPGs develop risk-stra...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3565912/ https://www.ncbi.nlm.nih.gov/pubmed/23302096 http://dx.doi.org/10.1186/1741-7015-11-7 |
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author | Yu, Tsung Vollenweider, Daniela Varadhan, Ravi Li, Tianjing Boyd, Cynthia Puhan, Milo A |
author_facet | Yu, Tsung Vollenweider, Daniela Varadhan, Ravi Li, Tianjing Boyd, Cynthia Puhan, Milo A |
author_sort | Yu, Tsung |
collection | PubMed |
description | BACKGROUND: Risk-stratified treatment recommendations facilitate treatment decision-making that balances patient-specific risks and preferences. It is unclear if and how such recommendations are developed in clinical practice guidelines (CPGs). Our aim was to assess if and how CPGs develop risk-stratified treatment recommendations for the prevention or treatment of common chronic diseases. METHODS: We searched the United States National Guideline Clearinghouse for US, Canadian and National Institute for Health and Clinical Excellence (United Kingdom) CPGs for heart disease, stroke, cancer, chronic obstructive pulmonary disease and diabetes that make risk-stratified treatment recommendations. We included only those CPGs that made risk-stratified treatment recommendations based on risk assessment tools. Two reviewers independently identified CPGs and extracted information on recommended risk assessment tools; type of evidence about treatment benefits and harms; methods for linking risk estimates to treatment evidence and for developing treatment thresholds; and consideration of patient preferences. RESULTS: We identified 20 CPGs that made risk-stratified treatment recommendations out of 133 CPGs that made any type of treatment recommendations for the chronic diseases considered in this study. Of the included 20 CPGs, 16 (80%) used evidence about treatment benefits from randomized controlled trials, meta-analyses or other guidelines, and the source of evidence was unclear in the remaining four (20%) CPGs. Nine CPGs (45%) used evidence on harms from randomized controlled trials or observational studies, while 11 CPGs (55%) did not clearly refer to harms. Nine CPGs (45%) explained how risk prediction and evidence about treatments effects were linked (for example, applying estimates of relative risk reductions to absolute risks), but only one CPG (5%) assessed benefit and harm quantitatively and three CPGs (15%) explicitly reported consideration of patient preferences. CONCLUSIONS: Only a small proportion of CPGs for chronic diseases make risk-stratified treatment recommendations with a focus on heart disease and stroke prevention, diabetes and breast cancer. For most CPGs it is unclear how risk-stratified treatment recommendations were developed. As a consequence, it is uncertain if CPGs support patients and physicians in finding an acceptable benefit- harm balance that reflects both profile-specific outcome risks and preferences. |
format | Online Article Text |
id | pubmed-3565912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35659122013-02-11 Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines Yu, Tsung Vollenweider, Daniela Varadhan, Ravi Li, Tianjing Boyd, Cynthia Puhan, Milo A BMC Med Research Article BACKGROUND: Risk-stratified treatment recommendations facilitate treatment decision-making that balances patient-specific risks and preferences. It is unclear if and how such recommendations are developed in clinical practice guidelines (CPGs). Our aim was to assess if and how CPGs develop risk-stratified treatment recommendations for the prevention or treatment of common chronic diseases. METHODS: We searched the United States National Guideline Clearinghouse for US, Canadian and National Institute for Health and Clinical Excellence (United Kingdom) CPGs for heart disease, stroke, cancer, chronic obstructive pulmonary disease and diabetes that make risk-stratified treatment recommendations. We included only those CPGs that made risk-stratified treatment recommendations based on risk assessment tools. Two reviewers independently identified CPGs and extracted information on recommended risk assessment tools; type of evidence about treatment benefits and harms; methods for linking risk estimates to treatment evidence and for developing treatment thresholds; and consideration of patient preferences. RESULTS: We identified 20 CPGs that made risk-stratified treatment recommendations out of 133 CPGs that made any type of treatment recommendations for the chronic diseases considered in this study. Of the included 20 CPGs, 16 (80%) used evidence about treatment benefits from randomized controlled trials, meta-analyses or other guidelines, and the source of evidence was unclear in the remaining four (20%) CPGs. Nine CPGs (45%) used evidence on harms from randomized controlled trials or observational studies, while 11 CPGs (55%) did not clearly refer to harms. Nine CPGs (45%) explained how risk prediction and evidence about treatments effects were linked (for example, applying estimates of relative risk reductions to absolute risks), but only one CPG (5%) assessed benefit and harm quantitatively and three CPGs (15%) explicitly reported consideration of patient preferences. CONCLUSIONS: Only a small proportion of CPGs for chronic diseases make risk-stratified treatment recommendations with a focus on heart disease and stroke prevention, diabetes and breast cancer. For most CPGs it is unclear how risk-stratified treatment recommendations were developed. As a consequence, it is uncertain if CPGs support patients and physicians in finding an acceptable benefit- harm balance that reflects both profile-specific outcome risks and preferences. BioMed Central 2013-01-09 /pmc/articles/PMC3565912/ /pubmed/23302096 http://dx.doi.org/10.1186/1741-7015-11-7 Text en Copyright ©2013 Yu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yu, Tsung Vollenweider, Daniela Varadhan, Ravi Li, Tianjing Boyd, Cynthia Puhan, Milo A Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines |
title | Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines |
title_full | Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines |
title_fullStr | Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines |
title_full_unstemmed | Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines |
title_short | Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines |
title_sort | support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3565912/ https://www.ncbi.nlm.nih.gov/pubmed/23302096 http://dx.doi.org/10.1186/1741-7015-11-7 |
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