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

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.<br/

Implementation of a Frailty Care Bundle (FCB) to reduce hospital associated decline in older orthopaedic trauma patients: pretest-posttest intervention study

BackgroundHospital associated decline (HAD) in older patients is an under-recognised and under-treated hospital harm. Fundamental care prioritising mobilisation, nutrition and cognitive well-being is protective against HAD, but it is inconsistently priortised in busy clinical settings. ObjectiveThe study aimed to implement and evaluate a frailty care bundle (FCB) for orthopaedic trauma patients in acute and rehabilitation settings to increase mobilisation, nutrition and cognitive well-being to reduce HAD. The intervention was delivered during the COVID-19 pandemic. DesignThe implementation science study used a step wedge pre-post design with multi-methods evaluation. Setting: Four wards across two hospitals: two acute trauma orthopaedic wards (n=62 beds) and two orthopaedic rehabilitation wards (n=33 beds). Participants: We enrolled 120 participants (pre n=60 and post n=60 implementation of the FCB across sites and wards), and at post-discharge follow-up there were 74 participants (pre n=43, post n=36).MethodsThe intervention implementation was underpinned by behaviour change theory COM-B and Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS). All wards received the intervention. A clinical facilitator worked with ward teams to prioritise and implement changes. The main changes were: setting a daily patient mobility goal, promoting assisted meal times, additional snacks, provision of distraction resources, and communication. Implementation strategies included establishing a multdisciplinary team local implementation group, staff education, audit and feedback, coaching, and role modelling. The main outcomes were participants' return to pre-trauma baseline functional capability at 6-8 weeks post-hospital discharge measured using the modified Bartels Index (mBI) and median hospital step count measured using accelerometers. Descriptive statistics included medians, interquartile ranges (IQR), proportions and percentages. Pre versus post group differences were estimated using log-linear models for step count and ordinal regression models for mBI and other outcomes. Odds ratios (OR) and 95% confidence intervals (CI) are reported. ResultsParticipants median age was 78 years, 83% were female and the post intervention group tended to be more frail. During hospitalisation, accelerometer data indicated a non-significant 11% (OR 1.11 95% 0.72-1.7) increase in post-intervention step count compared to pre. Post-intervention participants were more likely than pre-intervention participants to report higher post-discharge mBI scores relative to pre-admission scores OR 2.29( 95% CI 0.98-5.36), but it was not statistically significant (p=0.056). ConclusionIt was feasible to implement aspects of the FCB that ward teams had influence over, but system barriers persisted in addition to COVID-19 challenges. The changes remain tentative and require ongoing facilitation and monitoring for sustainability. Improved consistency in fundamental care, especially mobilisation may accelerate functional recovery<br/

Implementation of the Frailty Care Bundle (FCB) to promote mobilisation, nutrition and cognitive engagement in older people in acute care settings: protocol for an implementation science study

Background: Older people are among the most vulnerable patients in acute care hospitals. The hospitalisation process can result in newly acquired functional or cognitive deficits termed hospital associated decline (HAD). Prioritising fundamental care including mobilisation, nutrition, and cognitive engagement can reduce HAD risk. Aim: The Frailty Care Bundle (FCB) intervention aims to implement and evaluate evidence-based principles on early mobilisation, enhanced nutrition and increased cognitive engagement to prevent functional decline and HAD in older patients. Methods: A hybrid implementation science study will use a pragmatic prospective cohort design with a pre-post mixed methods evaluation to test the effect of the FCB on patient, staff, and health service outcomes. The evaluation will include a description of the implementation process, intervention adaptations, and economic costs analysis. The protocol follows the Standards for Reporting Implementation Studies (StaRI). The intervention design and implementation strategy will utilise the behaviour change theory COM-B (capability, motivation, opportunity) and the Promoting Action on Research Implementation in Health Services (i-PARIHS). A clinical facilitator will use a co-production approach with staff. All patients will receive care as normal, the intervention is delivered at ward level and focuses on nurses and health care assistants (HCA) normative clinical practices. The intervention will be delivered in three hospitals on six wards including rehabilitation, acute trauma, medical and older adult wards. Evaluation: The evaluation will recruit a volunteer sample of 180 patients aged 65 years or older (pre 90; post 90 patients). The primary outcomes are measures of functional status (modified Barthel Index (MBI)) and mobilisation measured as average daily step count using accelerometers. Process data will include ward activity mapping, staff surveys and interviews and an economic cost-impact analysis. Conclusions: This is a complex intervention that involves ward and system level changes and has the potential to improve outcomes for older patients