Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Einstein (São Paulo) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082022000100268 |
Resumo: | ABSTRACT Objective To develop and validate a high-risk predictive model that identifies, at least, one common adverse event in older population: early readmission (up to 30 days after discharge), long hospital stays (10 days or more) or in-hospital deaths. Methods This was a retrospective cohort study including patients aged 60 years or older (n=340) admitted at a 630-beds tertiary hospital, located in the city of São Paulo, Brazil. A predictive model of high-risk indication was developed by analyzing logistical regression models. This model prognostic capacity was assessed by measuring accuracy, sensitivity, specificity, and positive and negative predictive values. Areas under the receiver operating characteristic curve with 95% confidence intervals were also obtained to assess the discriminatory power of the model. Internal validation of the prognostic model was performed in a separate sample (n=168). Results Statistically significant predictors were identified, such as current Barthel Index, number of medications in use, presence of diabetes mellitus, difficulty chewing or swallowing, extensive surgery, and dementia. The study observed discrimination model acceptance in the construction sample 0.77 (95% confidence interval: 0.71-0.83) and good calibration. The characteristics of the validation samples were similar, and the receiver operating characteristic curve area was 0.687 (95% confidence interval: 0.598-0.776). We could assess an older patient’s adverse health events during hospitalization after admission. Conclusion A predictive model with acceptable discrimination was obtained, with satisfactory results for early readmission (30 days), long hospital stays (10 days), or in-hospital death. |
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Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years oldAginglenght of stayLong-term carePatient readmissionHospital mortalityHospitalizationLogistic modelsABSTRACT Objective To develop and validate a high-risk predictive model that identifies, at least, one common adverse event in older population: early readmission (up to 30 days after discharge), long hospital stays (10 days or more) or in-hospital deaths. Methods This was a retrospective cohort study including patients aged 60 years or older (n=340) admitted at a 630-beds tertiary hospital, located in the city of São Paulo, Brazil. A predictive model of high-risk indication was developed by analyzing logistical regression models. This model prognostic capacity was assessed by measuring accuracy, sensitivity, specificity, and positive and negative predictive values. Areas under the receiver operating characteristic curve with 95% confidence intervals were also obtained to assess the discriminatory power of the model. Internal validation of the prognostic model was performed in a separate sample (n=168). Results Statistically significant predictors were identified, such as current Barthel Index, number of medications in use, presence of diabetes mellitus, difficulty chewing or swallowing, extensive surgery, and dementia. The study observed discrimination model acceptance in the construction sample 0.77 (95% confidence interval: 0.71-0.83) and good calibration. The characteristics of the validation samples were similar, and the receiver operating characteristic curve area was 0.687 (95% confidence interval: 0.598-0.776). We could assess an older patient’s adverse health events during hospitalization after admission. Conclusion A predictive model with acceptable discrimination was obtained, with satisfactory results for early readmission (30 days), long hospital stays (10 days), or in-hospital death.Instituto Israelita de Ensino e Pesquisa Albert Einstein2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082022000100268einstein (São Paulo) v.20 2022reponame:Einstein (São Paulo)instname:Instituto Israelita de Ensino e Pesquisa Albert Einstein (IIEPAE)instacron:IIEPAE10.31744/einstein_journal/2022ao8012info:eu-repo/semantics/openAccessCosta,Maria Luiza MonteiroMafra,Ana Carolina Cintra NunesCendoroglo,Maysa SeabraRodrigues,Patrícia SilveiraFerreira,Milene SilvaStudenski,Stephanie A.Franco,Fábio Gazelato de Melloeng2022-06-14T00:00:00Zoai:scielo:S1679-45082022000100268Revistahttps://journal.einstein.br/pt-br/ONGhttps://old.scielo.br/oai/scielo-oai.php||revista@einstein.br2317-63851679-4508opendoar:2022-06-14T00:00Einstein (São Paulo) - Instituto Israelita de Ensino e Pesquisa Albert Einstein (IIEPAE)false |
dc.title.none.fl_str_mv |
Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old |
title |
Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old |
spellingShingle |
Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old Costa,Maria Luiza Monteiro Aging lenght of stay Long-term care Patient readmission Hospital mortality Hospitalization Logistic models |
title_short |
Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old |
title_full |
Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old |
title_fullStr |
Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old |
title_full_unstemmed |
Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old |
title_sort |
Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old |
author |
Costa,Maria Luiza Monteiro |
author_facet |
Costa,Maria Luiza Monteiro Mafra,Ana Carolina Cintra Nunes Cendoroglo,Maysa Seabra Rodrigues,Patrícia Silveira Ferreira,Milene Silva Studenski,Stephanie A. Franco,Fábio Gazelato de Mello |
author_role |
author |
author2 |
Mafra,Ana Carolina Cintra Nunes Cendoroglo,Maysa Seabra Rodrigues,Patrícia Silveira Ferreira,Milene Silva Studenski,Stephanie A. Franco,Fábio Gazelato de Mello |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Costa,Maria Luiza Monteiro Mafra,Ana Carolina Cintra Nunes Cendoroglo,Maysa Seabra Rodrigues,Patrícia Silveira Ferreira,Milene Silva Studenski,Stephanie A. Franco,Fábio Gazelato de Mello |
dc.subject.por.fl_str_mv |
Aging lenght of stay Long-term care Patient readmission Hospital mortality Hospitalization Logistic models |
topic |
Aging lenght of stay Long-term care Patient readmission Hospital mortality Hospitalization Logistic models |
description |
ABSTRACT Objective To develop and validate a high-risk predictive model that identifies, at least, one common adverse event in older population: early readmission (up to 30 days after discharge), long hospital stays (10 days or more) or in-hospital deaths. Methods This was a retrospective cohort study including patients aged 60 years or older (n=340) admitted at a 630-beds tertiary hospital, located in the city of São Paulo, Brazil. A predictive model of high-risk indication was developed by analyzing logistical regression models. This model prognostic capacity was assessed by measuring accuracy, sensitivity, specificity, and positive and negative predictive values. Areas under the receiver operating characteristic curve with 95% confidence intervals were also obtained to assess the discriminatory power of the model. Internal validation of the prognostic model was performed in a separate sample (n=168). Results Statistically significant predictors were identified, such as current Barthel Index, number of medications in use, presence of diabetes mellitus, difficulty chewing or swallowing, extensive surgery, and dementia. The study observed discrimination model acceptance in the construction sample 0.77 (95% confidence interval: 0.71-0.83) and good calibration. The characteristics of the validation samples were similar, and the receiver operating characteristic curve area was 0.687 (95% confidence interval: 0.598-0.776). We could assess an older patient’s adverse health events during hospitalization after admission. Conclusion A predictive model with acceptable discrimination was obtained, with satisfactory results for early readmission (30 days), long hospital stays (10 days), or in-hospital death. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082022000100268 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082022000100268 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.31744/einstein_journal/2022ao8012 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto Israelita de Ensino e Pesquisa Albert Einstein |
publisher.none.fl_str_mv |
Instituto Israelita de Ensino e Pesquisa Albert Einstein |
dc.source.none.fl_str_mv |
einstein (São Paulo) v.20 2022 reponame:Einstein (São Paulo) instname:Instituto Israelita de Ensino e Pesquisa Albert Einstein (IIEPAE) instacron:IIEPAE |
instname_str |
Instituto Israelita de Ensino e Pesquisa Albert Einstein (IIEPAE) |
instacron_str |
IIEPAE |
institution |
IIEPAE |
reponame_str |
Einstein (São Paulo) |
collection |
Einstein (São Paulo) |
repository.name.fl_str_mv |
Einstein (São Paulo) - Instituto Israelita de Ensino e Pesquisa Albert Einstein (IIEPAE) |
repository.mail.fl_str_mv |
||revista@einstein.br |
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1752129911103946752 |