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Falls prediction using the nursing home minimum dataset.
- Source :
-
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2022 Aug 16; Vol. 29 (9), pp. 1497-1507. - Publication Year :
- 2022
-
Abstract
- Objective: The purpose of the study was to develop and validate a model to predict the risk of experiencing a fall for nursing home residents utilizing data that are electronically available at the more than 15 000 facilities in the United States.<br />Materials and Methods: The fall prediction model was built and tested using 2 extracts of data (2011 through 2013 and 2016 through 2018) from the Long-term Care Minimum Dataset (MDS) combined with drug data from 5 skilled nursing facilities. The model was created using a hybrid Classification and Regression Tree (CART)-logistic approach.<br />Results: The combined dataset consisted of 3985 residents with mean age of 77 years and 64% female. The model's area under the ROC curve was 0.668 (95% confidence interval: 0.643-0.693) on the validation subsample of the merged data.<br />Discussion: Inspection of the model showed that antidepressant medications have a significant protective association where the resident has a fall history prior to admission, requires assistance to balance while walking, and some functional range of motion impairment in the lower body; even if the patient exhibits behavioral issues, unstable behaviors, and/or are exposed to multiple psychotropic drugs.<br />Conclusion: The novel hybrid CART-logit algorithm is an advance over the 22 fall risk assessment tools previously evaluated in the nursing home setting because it has a better performance characteristic for the fall prediction window of ≤90 days and it is the only model designed to use features that are easily obtainable at nearly every facility in the United States.<br /> (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Details
- Language :
- English
- ISSN :
- 1527-974X
- Volume :
- 29
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- Journal of the American Medical Informatics Association : JAMIA
- Publication Type :
- Academic Journal
- Accession number :
- 35818288
- Full Text :
- https://doi.org/10.1093/jamia/ocac111