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Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review
BACKGROUND: Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned ho...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9506609/ https://www.ncbi.nlm.nih.gov/pubmed/36149932 http://dx.doi.org/10.1371/journal.pone.0275116 |
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author | Klunder, Jet H. Panneman, Sofie L. Wallace, Emma de Vries, Ralph Joling, Karlijn J. Maarsingh, Otto R. van Hout, Hein P. J. |
author_facet | Klunder, Jet H. Panneman, Sofie L. Wallace, Emma de Vries, Ralph Joling, Karlijn J. Maarsingh, Otto R. van Hout, Hein P. J. |
author_sort | Klunder, Jet H. |
collection | PubMed |
description | BACKGROUND: Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults METHODS AND FINDINGS: We searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain. CONCLUSIONS: Models developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines. |
format | Online Article Text |
id | pubmed-9506609 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95066092022-09-24 Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review Klunder, Jet H. Panneman, Sofie L. Wallace, Emma de Vries, Ralph Joling, Karlijn J. Maarsingh, Otto R. van Hout, Hein P. J. PLoS One Research Article BACKGROUND: Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults METHODS AND FINDINGS: We searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain. CONCLUSIONS: Models developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines. Public Library of Science 2022-09-23 /pmc/articles/PMC9506609/ /pubmed/36149932 http://dx.doi.org/10.1371/journal.pone.0275116 Text en © 2022 Klunder et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Klunder, Jet H. Panneman, Sofie L. Wallace, Emma de Vries, Ralph Joling, Karlijn J. Maarsingh, Otto R. van Hout, Hein P. J. Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review |
title | Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review |
title_full | Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review |
title_fullStr | Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review |
title_full_unstemmed | Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review |
title_short | Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review |
title_sort | prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: a systematic review |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9506609/ https://www.ncbi.nlm.nih.gov/pubmed/36149932 http://dx.doi.org/10.1371/journal.pone.0275116 |
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