Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings.

Autor: Crowe C; Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parad, T12R5CP, Cork, Ireland., Naughton C; School of Nursing and Midwifery, University College Cork, Cork, T12 AK54, Ireland., de Foubert M; School of Nursing and Midwifery, University College Cork, Cork, T12 AK54, Ireland., Cummins H; School of Nursing and Midwifery, University College Cork, Cork, T12 AK54, Ireland., McCullagh R; School of Clinical Therapies, University College Cork, Cork, T12 AK54, Ireland., Skelton DA; School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland, UK., Dahly D; HRB Clinical Research Facility-UCC, and School of Public Health, University College Cork, Cork, T12 XF62, Ireland., Palmer B; HRB Clinical Research Facility-UCC, and School of Public Health, University College Cork, Cork, T12 XF62, Ireland., O'Flynn B; Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parad, T12R5CP, Cork, Ireland., Tedesco S; Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parad, T12R5CP, Cork, Ireland. salvatore.tedesco@tyndall.ie.
Jazyk: angličtina
Zdroj: Aging clinical and experimental research [Aging Clin Exp Res] 2024 Sep 10; Vol. 36 (1), pp. 187. Date of Electronic Publication: 2024 Sep 10.
DOI: 10.1007/s40520-024-02840-5
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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