Predicting Recovery Following Total Hip and Knee Arthroplasty Using a Clustering Algorithm

Abstract

Background: Recovery following total joint arthroplasty is patient-specific, yet groups of patients tend to fall into certain similar patterns of recovery. The purpose of this study was to identify and characterize recovery patterns following total hip arthroplasty (THA) and total knee arthroplasty (TKA) using patient-reported outcomes that represent distinct health domains. We hypothesized that recovery patterns could be defined and predicted using preoperative data. Methods: Adult patients were recruited from a large, urban academic center. To model postoperative responses to THA and TKA across domains such as physical health, mental health, and joint-specific measures, we employed a longitudinal clustering algorithm that incorporates each of these health domains. The clustering algorithm from multiple health domains allows the ability to define distinct recovery trajectories, which could then be predicted from preoperative and perioperative factors using a multinomial regression. Results: Four hundred forty-one of 1134 patients undergoing THA and 346 of 921 undergoing TKA met eligibility criteria and were used to define distinct patterns of recovery. The clustering algorithm was optimized for 3 distinct patterns of recovery that were observed in THA and TKA patients. Patients recovering from THA were divided into 3 groups: standard responders (50.8%), late mental responders (13.2%), and substandard responders (36.1%). Multivariable, multinomial regression suggested that these 3 groups had defined characteristics. Late mental responders tended to be obese (P = .05) and use more opioids (P = .01). Substandard responders had a larger number of comorbidities (P = .02) and used more opioids (P = .001). Patients recovering from TKA were divided among standard responders (55.8%), poor mental responders (24%), and poor physical responders (20.2%). Poor mental responders were more likely to be female (P = .04) and American Society of Anesthesiologists class III/IV (P = .004). Poor physical responders were more likely to be female (P = .03), younger (P = .04), American Society of Anesthesiologists III/IV (P = .04), use more opioids (P = .02), and be discharged to a nursing facility (P = .001). The THA and TKA models demonstrated areas under the curve of 0.67 and 0.72. Conclusions: This multidomain, longitudinal clustering analysis defines 3 distinct patterns in the recovery of THA and TKA patients, with most patients in both cohorts experiencing robust improvement, while others had equally well defined yet less optimal recovery trajectories that were either delayed in recovery or failed to achieve a desired outcome. Patients in the delayed recovery and poor outcome groups were slightly different between THA and TKA. These groups of patients with similar recovery patterns were defined by patient characteristics that include potentially modifiable comorbid factors. This research suggests that there are multiple defined recovery trajectories after THA and TKA, which provides a new perspective on THA and TKA recovery. Level of Evidence: III.

Keywords

• Predictive modeling
• Postoperative phenotype
• Recovery trajectory