Falls prediction using the nursing home minimum dataset. (11th July 2022)

• Record Type: Journal Article
• Authors: Boyce, Richard D; Kravchenko, Olga V; Perera, Subashan; Karp, Jordan F; Kane-Gill, Sandra L; Reynolds, Charles F; Albert, Steven M; Handler, Steven M
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 9(2022)
• Page Start: 1497
• Page End: 1507
• Publication Date: 2022-07-11
• Subjects: falls -- skilled nursing facilities -- long-term care minimum dataset -- fall prevention intervention; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac111
• Languages: English
• ISSNs: 1067-5027

Abstract

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. 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. 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. 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. 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.