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

• Published in: Aging clinical and experimental research 2024-09, Vol.36 (1), p.187, Article 187
• Main Authors: Crowe, Colum, Naughton, Corina, de Foubert, Marguerite, Cummins, Helen, McCullagh, Ruth, Skelton, Dawn A., Dahly, Darren, Palmer, Brendan, O’Flynn, Brendan, Tedesco, Salvatore
• Format: Article
• Language: English
• Subjects: Accelerometers; Accelerometry - methods; Accuracy; Aged; Aged, 80 and over; Cohort Studies; Female; Frail Elderly; Frailty - diagnosis; Gait; Gait - physiology; Geriatric Assessment - methods; Geriatrics/Gerontology; Humans; Machine Learning; Male; Medicine; Medicine & Public Health; Patient Care Bundles - methods; Regression analysis
• ISSN: 1720-8319; 1594-0667; 1720-8319
• DOI: 10.1007/s40520-024-02840-5

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.