Abstract
Objective
Wearable sensors have enabled objective functional data collection from patients before total knee replacement (TKR) and at clinical follow-ups post-surgery whereas traditional evaluation has solely relied on self-reported subjective measures. The timed-up-and-go (TUG) test has been used to evaluate function but is commonly measured using only total completion time, which does not assess joint function or test completion strategy. The current work employs machine learning techniques to distinguish patient groups based on derived functional metrics from the TUG test and expose clinically important functional parameters that are predictive of patient recovery.
Methods
Patients scheduled for TKR (n=70) were recruited and instrumented with a wearable sensor system while performing three TUG test trials. Remaining study patients (n=68) also completed three TUG trials at their 2, 6, and 13-week follow-ups. Many patients (n=36) have also participated up to their 26-week appointment. Custom developed software was used to segment recorded tests into sub-activities and extract 54 functional metrics to evaluate op/non-operative knee function. All preoperative TUG samples and their standardized metrics were clustered into two unlabelled groups using the k-means algorithm. Both groups were tracked forward to see how their early functional parameters translated to functional improvement at their three-month assessment. Test total completion time was used to estimate overall functional improvement and to relate findings to existing literature. Patients that completed their 26-week tests were tracked further to their most recent timepoint.
Results
Preoperative clustering separated two groups with different test completion times (n=46 vs. n=22 with mean times of 13s vs. 22s). Of the faster preoperative group, 63% of patients maintained their time, 26% improved, and 11% worsened whereas of the slower preoperative group, 27% maintained, 64% improved, and 9% worsened. The high improvement group improved their times by 4.9s (p<0.01) between preoperative and 13-week visits whereas the other group had no significant change. Test times were different between both groups preoperatively (p<0.001) and at 6 (p=0.01) and 13 (p=0.03) weeks but not at 26 weeks (p=0.67). The high improvement group reached an overall improvement of 9s (p<0.01) at 26 weeks whereas the low improvement group still showed no improvement greater than the TUG minimal detectable change of 2.2s (1.8s, p<0.01)[1]. Test sub-activity times for both groups at each timepoint can be seen in Figure 1.
Conclusions
This work has demonstrated that machine learning has the potential to find patterns in preoperative functional parameters that can predict functional improvement after surgery. While useful for assigning labels to the distinguished clusters, test completion time was not among the top distinguishable metrics between groups at three months which highlights the necessity for these more descriptive performance metrics when analyzing patient recovery. It is expected that these early predictions will be used to realistically adjust patient expectations or highlight opportunities for physiotherapeutic intervention to improve future outcomes.
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