@article{ref1,
title="Falls prediction using the nursing home minimum dataset",
journal="Journal of the American Medical Informatics Association",
year="2022",
author="Boyce, Richard D. and Kravchenko, Olga V. and Perera, Subashan and Karp, Jordan F. and Kane-Gill, Sandra L. and Reynolds, Charles F. and Albert, Steven M. and Handler, Steven M.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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><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><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><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><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.<p /> <p>Language: en</p>",
language="en",
issn="1067-5027",
doi="10.1093/jamia/ocac111",
url="http://dx.doi.org/10.1093/jamia/ocac111"
}