Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
<p>Abstract</p> <p>Background</p> <p>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...
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| Format: | Article |
| Language: | English |
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BMC
2012-03-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | http://www.biomedcentral.com/1472-6947/12/19 |
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| 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 | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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).</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p> |
| first_indexed | 2024-12-17T04:48:38Z |
| format | Article |
| id | doaj.art-9247a927fa354083901a3968e64b34ea |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-17T04:48:38Z |
| publishDate | 2012-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-9247a927fa354083901a3968e64b34ea2022-12-21T22:02:59ZengBMCBMC Medical Informatics and Decision Making1472-69472012-03-011211910.1186/1472-6947-12-19Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroupsMarschollek MichaelGövercin MehmetRust StefanGietzelt MatthiasSchulze MareikeWolf Klaus-HendrikSteinhagen-Thiessen Elisabeth<p>Abstract</p> <p>Background</p> <p>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).</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>http://www.biomedcentral.com/1472-6947/12/19Accidental fallsGeriatric assessmentData mining |
| spellingShingle | 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 BMC Medical Informatics and Decision Making Accidental falls Geriatric assessment Data mining |
| 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 | Accidental falls Geriatric assessment Data mining |
| url | http://www.biomedcentral.com/1472-6947/12/19 |
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