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Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records
BACKGROUND: Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing electronic health records (EHRs) provide opportunities but up to now showed limited clinical value as risk stratification tool, because of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9255681/ https://www.ncbi.nlm.nih.gov/pubmed/34637510 http://dx.doi.org/10.1093/gerona/glab311 |
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author | Dormosh, Noman Schut, Martijn C Heymans, Martijn W van der Velde, Nathalie Abu-Hanna, Ameen |
author_facet | Dormosh, Noman Schut, Martijn C Heymans, Martijn W van der Velde, Nathalie Abu-Hanna, Ameen |
author_sort | Dormosh, Noman |
collection | PubMed |
description | BACKGROUND: Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing electronic health records (EHRs) provide opportunities but up to now showed limited clinical value as risk stratification tool, because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHRs and to internally validate its predictive performance. METHODS: We used EHR data of individuals aged 65 or older. Age, sex, history of falls, medications, and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration. RESULTS: Data of 36 470 eligible participants were extracted from the data set. The number of participants who fell at least once was 4 778 (13.1%). The final prediction model included age, sex, history of falls, 2 medications, and 5 medical conditions. The model had a median area under the receiver operating curve of 0.705 (interquartile range 0.700–0.714). CONCLUSIONS: Our prediction model to identify older people at high risk for falls achieved fair discrimination and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests. |
format | Online Article Text |
id | pubmed-9255681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92556812022-07-06 Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records Dormosh, Noman Schut, Martijn C Heymans, Martijn W van der Velde, Nathalie Abu-Hanna, Ameen J Gerontol A Biol Sci Med Sci THE JOURNAL OF GERONTOLOGY: Medical Sciences BACKGROUND: Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing electronic health records (EHRs) provide opportunities but up to now showed limited clinical value as risk stratification tool, because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHRs and to internally validate its predictive performance. METHODS: We used EHR data of individuals aged 65 or older. Age, sex, history of falls, medications, and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration. RESULTS: Data of 36 470 eligible participants were extracted from the data set. The number of participants who fell at least once was 4 778 (13.1%). The final prediction model included age, sex, history of falls, 2 medications, and 5 medical conditions. The model had a median area under the receiver operating curve of 0.705 (interquartile range 0.700–0.714). CONCLUSIONS: Our prediction model to identify older people at high risk for falls achieved fair discrimination and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests. Oxford University Press 2021-10-12 /pmc/articles/PMC9255681/ /pubmed/34637510 http://dx.doi.org/10.1093/gerona/glab311 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | THE JOURNAL OF GERONTOLOGY: Medical Sciences Dormosh, Noman Schut, Martijn C Heymans, Martijn W van der Velde, Nathalie Abu-Hanna, Ameen Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records |
title | Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records |
title_full | Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records |
title_fullStr | Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records |
title_full_unstemmed | Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records |
title_short | Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records |
title_sort | development and internal validation of a risk prediction model for falls among older people using primary care electronic health records |
topic | THE JOURNAL OF GERONTOLOGY: Medical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9255681/ https://www.ncbi.nlm.nih.gov/pubmed/34637510 http://dx.doi.org/10.1093/gerona/glab311 |
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