Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings
| dc.contributor.author | Crowe, Colum | en |
| dc.contributor.author | Naughton, Corina | en |
| dc.contributor.author | de Foubert, Marguerite | en |
| dc.contributor.author | Cummins, Helen | en |
| dc.contributor.author | McCullagh, Ruth | en |
| dc.contributor.author | Skelton, Dawn A. | en |
| dc.contributor.author | Dahly, Darren L. | en |
| dc.contributor.author | Palmer, Brendan A. | en |
| dc.contributor.author | O'Flynn, Brendan | en |
| dc.contributor.author | Tedesco, Salvatore | en |
| dc.contributor.funder | Health Research Board | en |
| dc.contributor.funder | South South-West Hospital | en |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.date.accessioned | 2025-01-08T15:45:06Z | |
| dc.date.available | 2025-01-08T15:45:06Z | |
| dc.date.issued | 2024 | en |
| dc.description.abstract | Purpose: The aim of this study is to explore the feasibility of using machine learning approaches to objectively differentiate the mobilization patterns, measured via accelerometer sensors, of patients pre- and post-intervention. Methods: The intervention tested the implementation of a Frailty Care Bundle to improve mobilization, nutrition and cognition in older orthopedic patients. The study recruited 120 participants, a sub-group analysis was undertaken on 113 patients with accelerometer data (57 pre-intervention and 56 post-intervention), the median age was 78 years and the majority were female. Physical activity data from an ankle-worn accelerometer (StepWatch 4) was collected for each patient during their hospital stay. These data contained daily aggregated gait variables. Data preprocessing included the standardization of step counts and feature computation. Subsequently, a binary classification model was trained. A systematic hyperparameter optimization approach was applied, and feature selection was performed. Two classifier models, logistic regression and Random Forest, were investigated and Shapley values were used to explain model predictions. Results: The Random Forest classifier demonstrated an average balanced accuracy of 82.3% (± 1.7%) during training and 74.7% (± 8.2%) for the test set. In comparison, the logistic regression classifier achieved a training accuracy of 79.7% (± 1.9%) and a test accuracy of 77.6% (± 5.5%). The logistic regression model demonstrated less overfitting compared to the Random Forest model and better performance on the hold-out test set. Stride length was consistently chosen as a key feature in all iterations for both models, along with features related to stride velocity, gait speed, and Lyapunov exponent, indicating their significance in the classification. Conclusion: The best performing classifier was able to distinguish between patients pre- and post-intervention with greater than 75% accuracy. The intervention showed a correlation with higher gait speed and reduced stride length. However, the question of whether these alterations are part of an adaptive process that leads to improved outcomes over time remains. | en |
| dc.description.sponsorship | Health Research Board (HRB), South South-West Hospital ((APA-2019-009) under the Applied Partnership Award) | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Crowe, C., Naughton, C., De Foubert, M., Cummins, H., McCullagh, R., Skelton, D.A., Dahly, D., Palmer, B., O’Flynn, B. and Tedesco, S. (2024) ‘Treatment effect analysis of the Frailty Care Bundle (Fcb) in a cohort of patients in acute care settings’, Aging Clinical and Experimental Research, 36(1), 187 (12pp). https://doi.org/10.1007/s40520-024-02840-5 | en |
| dc.identifier.doi | https://doi.org/10.1007/s40520-024-02840-5 | en |
| dc.identifier.endpage | 12 | en |
| dc.identifier.issued | 1 | en |
| dc.identifier.journaltitle | Aging Clinical and Experimental Research | en |
| dc.identifier.startpage | 1 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/16795 | |
| dc.identifier.volume | 36 | en |
| dc.language.iso | en | en |
| dc.publisher | Springer | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Maternity/Adoptive Leave Allowance/12/RC/2289-P2s/IE/INSIGHT Phase 2/ | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/ | en |
| dc.rights | © The Authors, 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Nursing | en |
| dc.subject | Older people | en |
| dc.subject | Mobilization | en |
| dc.subject | Hospital associated decline | en |
| dc.subject | Functional decline | en |
| dc.subject | Accelerometry | en |
| dc.subject | Machine learning | en |
| dc.title | Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings | en |
| dc.type | Article (peer-reviewed) | en |
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