Patient satisfaction and patient-reported outcomes do not vary by BMI class in total hip arthroplasty.

PURPOSE: Obesity has been identified as a risk factor for postoperative complications in patients undergoing total hip arthroplasty (THA). This study aimed to investigate patient-reported outcomes, pain, and satisfaction as a function of body mass index (BMI) class in patients undergoing THA. METHODS: 1736 patients within a prospective observational study were categorized into BMI classes. Pre- and postoperative Hip disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS JR), satisfaction, and pain scores were compared by BMI class using one-way ANOVA. RESULTS: Healthy weight patients reported the highest preoperative HOOS JR (56.66 ± 13.35) compared to 45.51 ± 14.45 in Class III subjects. Healthy weight and Class III patients reported the lowest (5.65 ± 2.01) and highest (7.06 ± 1.98, p < 0.0001) preoperative pain, respectively. Changes in HOOS JR scores from baseline suggest larger improvements with increasing BMI class, where Class III patients reported an increase of 33.7 ± 15.6 points at 90 days compared to 26.1 ± 17.1 in healthy weight individuals (p = 0.002). Fewer healthy weight patients achieved the minimal clinically important difference (87.4%) for HOOS JR compared to Class II (96.5%) and III (94.7%) obesity groups at 90 days postoperatively. Changes in satisfaction and pain scores were largest in the Class III patients. Overall, no functional outcomes varied by BMI class postoperatively. CONCLUSION: Patients of higher BMI class reported greater improvements following THA. While risk/benefit shared decision-making remains a personalized requirement of THA, this study highlights that utilization of BMI cutoff may not be warranted based on pain and functional improvement.

A Retrospective Study on the Feasibility of Using Low-burden Patient-reported Pain Scores to Track Recovery and Outcomes After Total Joint Replacement.

Background: Patients undergo total joint arthroplasty to improve function and resolve pain. Patient-reported outcome measures (PROMs) are often sought to determine the success of total joint arthroplasty but are time-consuming and patient response rates are often low. This study sought to determine whether pain numeric rating scores (NRSs) were associated with PROMs and objective mobility outcomes. Methods: This is a retrospective review of data in patients who utilized a smartphone-based care management application prior to and following total joint arthroplasty. NRS, Hip Disability and Osteoarthritis Outcome Score, Joint Replacement and Knee Injury and Osteoarthritis Outcome Score, Joint Replacement, and objective mobility data (step counts, gait speed, and gait asymmetry) were collected preoperatively and at 30 and 90 days postoperatively. Quantile regression was performed to evaluate the correlations between NRS and PROMs. Results: Total knee arthroplasty patients reported higher NRS than total hip arthroplasty patients postoperatively. NRS was significantly correlated with gait speed preoperatively and at 30 and 90 days postoperatively on quantile regression. Gait asymmetry was significantly associated with NRS at 30 days postoperatively. Regression results suggested significant correlations between NRS and PROMs scores; Hip Disability and Osteoarthritis Outcome Score, Joint Replacement, -0.46 (95% confidence interval: -0.48 to -0.44, P < .001) and Knee Injury and Osteoarthritis Outcome Score, Joint Replacement, -0.38 (95% confidence interval: -0.40 to -0.36, P < .001). Conclusions: NRS is correlated with both objective and subjective measures of function in patients undergoing arthroplasty. Simple pain ratings may be a valid measurement to help predict functional outcomes when collection of traditional PROMs is not feasible.

Early post-operative walking bouts are associated with improved gait speed and symmetry at 90 days.

INTRODUCTION: There is a paucity of literature on optimal patterns of daily walking following joint arthroplasty, which are now evaluated with consumer technologies like smartphones, and can enhance our understanding of post-operative mobility. When smartphone-recorded, daily walking patterns are captured, qualities of gait-recovery such as gait speed or symmetry can be analyzed in real-world environments. RESEARCH QUESTION: Are the daily distribution of walking bouts in the early post-operative period associated with 90-day gait quality measures following hip and knee arthroplasty? METHODS: Gait data was collected passively using a smartphone-based care management platform in patients undergoing hip and knee arthroplasty. As recorded via subjects' free-living smartphone-collected gait bouts, data were investigated as a function of the walking session length and were used to create a ratio to the total time logging bouts, representing the fraction of walking performed during a single session per day (aggregation score). Quantile regression was performed to evaluate the association between early walking session lengths or aggregation score at 30 days post-operatively and the gait-sampled speed and asymmetry of walking at 90 days. RESULTS: In total, 2255 patients provided evaluable data. The walking session length at 30 days was positively associated with 90-day mean gait speed across procedure types where quantile regression coefficients ranged from 0.11 to 0.17. In contrast, aggregation score was negatively associated with gait speed at 90 days, with coefficients ranging from -0.18 to -0.12. SIGNIFICANCE: The duration and frequency of walking bouts was associated with recovery of gait speed and symmetry following lower limb arthroplasty. The findings may help clinicians design walking protocols that are associated with improved gait metrics at 3 months.

Stepping Beyond Counts in Recovery of Total Hip Arthroplasty: A Prospective Study on Passively Collected Gait Metrics.

Gait quality parameters have been used to measure recovery from total hip arthroplasty (THA) but are time-intensive and previously could only be performed in a lab. Smartphone sensor data and algorithmic advances presently allow for the passive collection of qualitative gait metrics. The purpose of this prospective study was to observe the recovery of physical function following THA by assessing passively collected pre- and post-operative gait quality metrics. This was a multicenter, prospective cohort study. From six weeks pre-operative through to a minimum 24 weeks post-operative, 612 patients used a digital care management application that collected gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-sample t-test. Recovery was defined as the post-operative week when the respective gait metric was no longer statistically inferior to the pre-operative value. To control for multiple comparison error, significance was set at p < 0.002. Walking speeds and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-post-operative (p < 0.001). Walking speed (1.00 ± 0.14 m/s, p = 0.04), step length (0.58 ± 0.06 m/s, p = 0.02), asymmetry (14.5 ± 19.4%, p = 0.046), and double support percentage (31.6 ± 1.5%, p = 0.0089) recovered at 9, 8, 7, and 10 weeks post-operative, respectively. Walking speed, step length, asymmetry, and double support all recovered beyond pre-operative values at 13, 17, 10, and 18 weeks, respectively (p < 0.002). Functional recovery following THA can be measured via passively collected gait quality metrics using a digital care management platform. The data suggest that metrics of gait quality are most negatively affected two weeks post-operative; recovery to pre-operative levels occurs at approximately 10 weeks following primary THA, and follows a slower trajectory compared to previously reported step count recovery trajectories.

Stepping beyond Counts in Recovery of Total Knee Arthroplasty: A Prospective Study on Passively Collected Gait Metrics.

Advances in algorithms developed from sensor-based technology data allow for the passive collection of qualitative gait metrics beyond step counts. The purpose of this study was to evaluate pre- and post-operative gait quality data to assess recovery following primary total knee arthroplasty. This was a multicenter, prospective cohort study. From 6 weeks pre-operative through to 24 weeks post-operative, 686 patients used a digital care management application to collect gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-samples t-test. Recovery was operationally defined as when the respective weekly average gait metric was no longer statistically different than pre-operative. Walking speed and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-operative (p < 0.0001). Walking speed recovered at 21 weeks (1.00 m/s, p = 0.063) and double support percentage recovered at week 24 (32%, p = 0.089). Asymmetry percentage was recovered at 13 weeks (14.0%, p = 0.23) and was consistently superior to pre-operative values at week 19 (11.1% vs. 12.5%, p < 0.001). Step length did not recover during the 24-week period (0.60 m vs. 0.59 m, p = 0.004); however, this difference is not likely clinically relevant. The data suggests that gait quality metrics are most negatively affected two weeks post-operatively, recover within the first 24-weeks following TKA, and follow a slower trajectory compared to previously reported step count recoveries. The ability to capture new objective measures of recovery is evident. As more gait quality data is accrued, physicians may be able to use passively collected gait quality data to help direct post-operative recovery using sensor-based care pathways.

An orthopaedic intelligence application successfully integrates data from a smartphone-based care management platform and a robotic knee system using a commercial database.

PURPOSE: To evaluate the feasibility of using a smartphone-based care management platform (sbCMP) and robotic-assisted total knee arthroplasty (raTKA) to collect data throughout the episode-of-care and assess if intra-operative measures of soft tissue laxity in raTKA were associated with post-operative outcomes. METHODS: A secondary data analysis of 131 patients in a commercial database who underwent raTKA was performed. Pre-operative through six week post-operative step counts and KOOS JR scores were collected and cross-referenced with intra-operative laxity measures. A Kruskal-Wallis test or a Wilcoxon sign-rank was used to assess outcomes. RESULTS: There were higher step counts at six weeks post-operatively in knees with increased laxity in both the lateral compartment in extension and medial compartment in flexion (p < 0.05). Knees balanced in flexion within < 0.5 mm had higher KOOS JR scores at six weeks post-operative (p = 0.034) compared to knees balanced within 0.5-1.5 mm. CONCLUSION: A smartphone-based care management platform can be integrated with raTKA to passively collect data throughout the episode-of-care. Associations between intra-operative decisions regarding laxity and post-operative outcomes were identified. However, more robust analysis is needed to evaluate these associations and ensure clinical relevance to guide machine learning algorithms.