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Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old

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 incl...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Instituto Israelita de Ensino e Pesquisa Albert Einstein 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239538/
https://www.ncbi.nlm.nih.gov/pubmed/35730807
http://dx.doi.org/10.31744/einstein_journal/2022AO8012
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author 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_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_sort Costa, Maria Luiza Monteiro
collection PubMed
description 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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spelling pubmed-92395382022-07-01 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 Mafra, Ana Carolina Cintra Nunes Cendoroglo, Maysa Seabra Rodrigues, Patrícia Silveira Ferreira, Milene Silva Studenski, Stephanie A. Franco, Fábio Gazelato de Mello Einstein (Sao Paulo) Original Article 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 Einstein 2022-06-14 /pmc/articles/PMC9239538/ /pubmed/35730807 http://dx.doi.org/10.31744/einstein_journal/2022AO8012 Text en https://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
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
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
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_short 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
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239538/
https://www.ncbi.nlm.nih.gov/pubmed/35730807
http://dx.doi.org/10.31744/einstein_journal/2022AO8012
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