Cargando…

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

BACKGROUND: Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification model...

Descripción completa

Detalles Bibliográficos
Autores principales: Marschollek, Michael, Gövercin, Mehmet, Rust, Stefan, Gietzelt, Matthias, Schulze, Mareike, Wolf, Klaus-Hendrik, Steinhagen-Thiessen, Elisabeth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3314576/
https://www.ncbi.nlm.nih.gov/pubmed/22417403
http://dx.doi.org/10.1186/1472-6947-12-19
_version_ 1782228104215789568
author Marschollek, Michael
Gövercin, Mehmet
Rust, Stefan
Gietzelt, Matthias
Schulze, Mareike
Wolf, Klaus-Hendrik
Steinhagen-Thiessen, Elisabeth
author_facet Marschollek, Michael
Gövercin, Mehmet
Rust, Stefan
Gietzelt, Matthias
Schulze, Mareike
Wolf, Klaus-Hendrik
Steinhagen-Thiessen, Elisabeth
author_sort Marschollek, Michael
collection PubMed
description BACKGROUND: Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). METHODS: A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. RESULTS: The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. CONCLUSIONS: Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.
format Online
Article
Text
id pubmed-3314576
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-33145762012-04-02 Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups Marschollek, Michael Gövercin, Mehmet Rust, Stefan Gietzelt, Matthias Schulze, Mareike Wolf, Klaus-Hendrik Steinhagen-Thiessen, Elisabeth BMC Med Inform Decis Mak Research Article BACKGROUND: Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). METHODS: A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. RESULTS: The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. CONCLUSIONS: Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting. BioMed Central 2012-03-14 /pmc/articles/PMC3314576/ /pubmed/22417403 http://dx.doi.org/10.1186/1472-6947-12-19 Text en Copyright ©2012 Marschollek et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Marschollek, Michael
Gövercin, Mehmet
Rust, Stefan
Gietzelt, Matthias
Schulze, Mareike
Wolf, Klaus-Hendrik
Steinhagen-Thiessen, Elisabeth
Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
title Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
title_full Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
title_fullStr Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
title_full_unstemmed Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
title_short Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
title_sort mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3314576/
https://www.ncbi.nlm.nih.gov/pubmed/22417403
http://dx.doi.org/10.1186/1472-6947-12-19
work_keys_str_mv AT marschollekmichael mininggeriatricassessmentdataforinpatientfallpredictionmodelsandhighrisksubgroups
AT govercinmehmet mininggeriatricassessmentdataforinpatientfallpredictionmodelsandhighrisksubgroups
AT ruststefan mininggeriatricassessmentdataforinpatientfallpredictionmodelsandhighrisksubgroups
AT gietzeltmatthias mininggeriatricassessmentdataforinpatientfallpredictionmodelsandhighrisksubgroups
AT schulzemareike mininggeriatricassessmentdataforinpatientfallpredictionmodelsandhighrisksubgroups
AT wolfklaushendrik mininggeriatricassessmentdataforinpatientfallpredictionmodelsandhighrisksubgroups
AT steinhagenthiessenelisabeth mininggeriatricassessmentdataforinpatientfallpredictionmodelsandhighrisksubgroups