Machine learning and wearable sensors at preoperative assessments: Functional recovery prediction to set realistic expectations for knee replacements. (March 2021)

• Record Type: Journal Article
• Authors: Bloomfield, Riley A.; Broberg, Jordan S.; Williams, Harley A.; Lanting, Brent A.; McIsaac, Kenneth A.; Teeter, Matthew G.
• Is Part Of: Medical engineering & physics. Volume 89(2021)
• Page Start: 14
• Page End: 21
• Publication Date: 2021-03
• Subjects: Total knee replacement -- Machine learning -- Recovery prediction -- Wearable sensors -- Patient expectations; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2020.12.007
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Patient expectations contribute to high dissatisfaction rates after knee replacement. Wearable sensors permit objective measurement in the clinic. Function before surgery is linked to improvement after intervention. Machine learning can predict the amount of improvement to adjust patient expectations. Abstract: Unmet expectations contribute to a high patient dissatisfaction rate following total knee replacement but clinicians currently do not have the tools to confidently adjust expectations. In this study, supervised machine learning was applied to multi-variate wearable sensor data from preoperative timed-up-and-go tests. Participants ( n =82) were instrumented three months after surgery and patients showing relevant improvement were designated as "responders" while the remainder were labelled "maintainers". Support vector machine, naïve Bayes, and random forest binary classifiers were developed to distinguish patients using sensor-derived features. Accuracy, sensitivity, specificity, and area under the receiver-operator curve (AUC) were compared between models using ten-fold out-of-sample testing. A high performance using only sensor-derived functional metrics was obtained with a random forest model (accuracy = 0.76 ± 0.11, sensitivity = 0.87 ± 0.08, specificity = 0.57 ± 0.26, AUC = 0.80 ± 0.14) but highly sensitive models were observed using naïve Bayes and SVM models after including patient age, sex, and BMI into the feature set (accuracy = 0.72, 0.73 ± 0.09, 0.12; sensitivity = 0.94, 0.95 ± 0.11, 0.11; specificity = 0.35, 0.37 ± 0.20, 0.18; AUC = 0.80, 0.74 ± 0.07, 0.11; respectfully). Including select patient-reported subjective measures increased the top random forest performance slightly (accuracy = 0.80 ± 0.10, sensitivity = 0.91 ± 0.14, specificity = 0.62 ± 0.23, AUC = 0.86 ± 0.09). The current work has demonstrated that prediction models developed from preoperative sensor-derived functional metrics can reliably predict expected functional recovery following surgery and this can be used by clinicians to help set realistic patient expectations.