⚡ Quick Summary
This study evaluated the effectiveness of the Frailty Care Bundle (FCB) in enhancing mobilization, nutrition, and cognition among older orthopedic patients. Utilizing machine learning, researchers achieved a classification accuracy of over 75% in distinguishing pre- and post-intervention mobilization patterns.
🔍 Key Details
- 📊 Dataset: 120 participants, 113 with accelerometer data
- 🧩 Features used: Gait variables from accelerometer data
- ⚙️ Technology: Machine learning models (Logistic Regression, Random Forest)
- 🏆 Performance: Random Forest: 82.3% training accuracy, 74.7% test accuracy
🔑 Key Takeaways
- 📊 The Frailty Care Bundle (FCB) aims to improve outcomes for older orthopedic patients.
- 💡 Machine learning was employed to analyze mobilization patterns using accelerometer data.
- 👩🔬 A total of 120 patients were recruited, with a median age of 78 years.
- 🏆 The Random Forest model outperformed logistic regression in training accuracy.
- 🚶♂️ Key features included stride length, stride velocity, and gait speed.
- 🌍 The study highlights the importance of objective measures in assessing patient mobility.
- 🔍 Future research is needed to explore long-term outcomes of the intervention.
📚 Background
Frailty in older adults is a significant concern in healthcare, particularly in acute care settings. The Frailty Care Bundle (FCB) is designed to address multiple aspects of care, including mobilization, nutrition, and cognitive function. By leveraging technology such as accelerometers and machine learning, healthcare providers can gain valuable insights into patient mobility and overall health.
🗒️ Study
This study was conducted with a cohort of 120 older orthopedic patients, focusing on the implementation of the Frailty Care Bundle. Researchers collected data using an ankle-worn accelerometer (StepWatch 4) to monitor daily gait variables during the patients’ hospital stays. A sub-group analysis was performed on 113 patients who had complete accelerometer data.
📈 Results
The study found that the Random Forest classifier achieved an average balanced accuracy of 82.3% during training and 74.7% for the test set. In contrast, the logistic regression model had a training accuracy of 79.7% and a test accuracy of 77.6%. Notably, stride length emerged as a critical feature in both models, underscoring its relevance in assessing patient mobility.
🌍 Impact and Implications
The findings from this study have significant implications for the management of frailty in older adults. By utilizing machine learning to analyze mobilization patterns, healthcare providers can better tailor interventions to improve patient outcomes. This approach not only enhances the understanding of patient mobility but also paves the way for more personalized care strategies in acute settings.
🔮 Conclusion
The study demonstrates the potential of the Frailty Care Bundle in improving mobilization among older orthopedic patients. With machine learning techniques achieving over 75% accuracy in distinguishing pre- and post-intervention patterns, there is a promising avenue for further research into the long-term benefits of such interventions. Continued exploration in this field could lead to enhanced care models for frail older adults.
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Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings.
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.
Author: [‘Crowe C’, ‘Naughton C’, ‘de Foubert M’, ‘Cummins H’, ‘McCullagh R’, ‘Skelton DA’, ‘Dahly D’, ‘Palmer B’, “O’Flynn B”, ‘Tedesco S’]
Journal: Aging Clin Exp Res
Citation: Crowe C, et al. Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings. Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings. 2024; 36:187. doi: 10.1007/s40520-024-02840-5
