A multivariable prediction model to select colorectal surgical patients for co-management
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/14834 |
Resumo: | Introduction: Increased life expectancy leads to older and frailer surgical patients. Co-management between medical and surgical specialities has proven favourable in complex situations. Selection of patients for co-management is full of difficulties. The aim of this study was to develop a clinical decision support tool to select surgical patients for co-management. Material and Methods: Clinical data was collected from patient electronic health records with an ICD-9 code for colorectal surgery from January 2012 to December 2015 at a hospital in Lisbon. The outcome variable consists in co-management signalling. A dataset from 344 patients was used to develop the prediction model and a second data set from 168 patients was used for external validation. Results: Using logistic regression modelling the authors built a five variable (age, burden of comorbidities, ASA-PS status, surgical risk and recovery time) predictive referral model for co-management. This model has an area under the curve (AUC) of 0.86 (95% CI: 0.81 - 0.90), a predictive Brier score of 0.11, a sensitivity of 0.80, a specificity of 0.82 and an accuracy of 81.3%. Discussion: Early referral of high-risk patients may be valuable to guide the decision on the best level of post-operative clinical care. We developed a simple bedside decision tool with a good discriminatory and predictive performance in order to select patients for comanagement. Conclusion: A simple bed-side clinical decision support tool of patients for co-management is viable, leading to potential improvement in early recognition and management of postoperative complications and reducing the ‘failure to rescue’. Generalizability to other clinical settings requires adequate customization and validation. |
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A multivariable prediction model to select colorectal surgical patients for co-managementUm modelo de predição para seleccionar para co-gestão doentes de cirurgia colo-rectalColorectal surgery/methodsCooperative behaviorDecision support systemsClinicalFailure to rescueHealth carePatient selectionIntroduction: Increased life expectancy leads to older and frailer surgical patients. Co-management between medical and surgical specialities has proven favourable in complex situations. Selection of patients for co-management is full of difficulties. The aim of this study was to develop a clinical decision support tool to select surgical patients for co-management. Material and Methods: Clinical data was collected from patient electronic health records with an ICD-9 code for colorectal surgery from January 2012 to December 2015 at a hospital in Lisbon. The outcome variable consists in co-management signalling. A dataset from 344 patients was used to develop the prediction model and a second data set from 168 patients was used for external validation. Results: Using logistic regression modelling the authors built a five variable (age, burden of comorbidities, ASA-PS status, surgical risk and recovery time) predictive referral model for co-management. This model has an area under the curve (AUC) of 0.86 (95% CI: 0.81 - 0.90), a predictive Brier score of 0.11, a sensitivity of 0.80, a specificity of 0.82 and an accuracy of 81.3%. Discussion: Early referral of high-risk patients may be valuable to guide the decision on the best level of post-operative clinical care. We developed a simple bedside decision tool with a good discriminatory and predictive performance in order to select patients for comanagement. Conclusion: A simple bed-side clinical decision support tool of patients for co-management is viable, leading to potential improvement in early recognition and management of postoperative complications and reducing the ‘failure to rescue’. Generalizability to other clinical settings requires adequate customization and validation.Ordem dos MédicosRCIPLBayão Horta, A.Brás-Geraldes, CarlosSalgado, CátiaVieira, SusanaXavier, MiguelPapoila, Ana Luisa2022-07-14T10:19:42Z2021-022021-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/14834engHORTA, Alexandra Bayão; [et al] – A multivariable prediction model to select colorectal surgical patients for co-management. Acta Médica Portuguesa. ISSN 0870-399X. Vol. 34, N.º 2 (2021), pp. 118-127.0870-399X10.20344/amp.129961646-0758info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T10:11:32Zoai:repositorio.ipl.pt:10400.21/14834Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:22:34.104133Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A multivariable prediction model to select colorectal surgical patients for co-management Um modelo de predição para seleccionar para co-gestão doentes de cirurgia colo-rectal |
title |
A multivariable prediction model to select colorectal surgical patients for co-management |
spellingShingle |
A multivariable prediction model to select colorectal surgical patients for co-management Bayão Horta, A. Colorectal surgery/methods Cooperative behavior Decision support systems Clinical Failure to rescue Health care Patient selection |
title_short |
A multivariable prediction model to select colorectal surgical patients for co-management |
title_full |
A multivariable prediction model to select colorectal surgical patients for co-management |
title_fullStr |
A multivariable prediction model to select colorectal surgical patients for co-management |
title_full_unstemmed |
A multivariable prediction model to select colorectal surgical patients for co-management |
title_sort |
A multivariable prediction model to select colorectal surgical patients for co-management |
author |
Bayão Horta, A. |
author_facet |
Bayão Horta, A. Brás-Geraldes, Carlos Salgado, Cátia Vieira, Susana Xavier, Miguel Papoila, Ana Luisa |
author_role |
author |
author2 |
Brás-Geraldes, Carlos Salgado, Cátia Vieira, Susana Xavier, Miguel Papoila, Ana Luisa |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Bayão Horta, A. Brás-Geraldes, Carlos Salgado, Cátia Vieira, Susana Xavier, Miguel Papoila, Ana Luisa |
dc.subject.por.fl_str_mv |
Colorectal surgery/methods Cooperative behavior Decision support systems Clinical Failure to rescue Health care Patient selection |
topic |
Colorectal surgery/methods Cooperative behavior Decision support systems Clinical Failure to rescue Health care Patient selection |
description |
Introduction: Increased life expectancy leads to older and frailer surgical patients. Co-management between medical and surgical specialities has proven favourable in complex situations. Selection of patients for co-management is full of difficulties. The aim of this study was to develop a clinical decision support tool to select surgical patients for co-management. Material and Methods: Clinical data was collected from patient electronic health records with an ICD-9 code for colorectal surgery from January 2012 to December 2015 at a hospital in Lisbon. The outcome variable consists in co-management signalling. A dataset from 344 patients was used to develop the prediction model and a second data set from 168 patients was used for external validation. Results: Using logistic regression modelling the authors built a five variable (age, burden of comorbidities, ASA-PS status, surgical risk and recovery time) predictive referral model for co-management. This model has an area under the curve (AUC) of 0.86 (95% CI: 0.81 - 0.90), a predictive Brier score of 0.11, a sensitivity of 0.80, a specificity of 0.82 and an accuracy of 81.3%. Discussion: Early referral of high-risk patients may be valuable to guide the decision on the best level of post-operative clinical care. We developed a simple bedside decision tool with a good discriminatory and predictive performance in order to select patients for comanagement. Conclusion: A simple bed-side clinical decision support tool of patients for co-management is viable, leading to potential improvement in early recognition and management of postoperative complications and reducing the ‘failure to rescue’. Generalizability to other clinical settings requires adequate customization and validation. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-02 2021-02-01T00:00:00Z 2022-07-14T10:19:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.21/14834 |
url |
http://hdl.handle.net/10400.21/14834 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
HORTA, Alexandra Bayão; [et al] – A multivariable prediction model to select colorectal surgical patients for co-management. Acta Médica Portuguesa. ISSN 0870-399X. Vol. 34, N.º 2 (2021), pp. 118-127. 0870-399X 10.20344/amp.12996 1646-0758 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Ordem dos Médicos |
publisher.none.fl_str_mv |
Ordem dos Médicos |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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