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Abstract

Patient-reported outcomes are important measures of clinical care and surgical outcomes. As these measures are increasingly integrated into clinical practice, there is also a need to translate collected data into valuable information to better understand patients’ health trajectories and improve the value of surgical care.

Furthermore, the growing evidence of quality-of-life (QoL) as an important predictor of outcomes suggests that a better understanding of patients’ QoL trajectories can reveal important information on disease progression. Using data from a registry cohort of patients undergoing total knee replacement (TKR) surgery, this study aims to identify unique QoL trajectory groups using latent class growth analysis and to examine patient characteristics of identified trajectories.

There appears to beimportant unobserved heterogeneity in QoL trajectories in patients undergoing TKR. Associating QoL trajectories with patient characteristics can help identify subgroups for whom TKR may be of higher or lower
value, thus facilitate the rational deployment of TKR to those who stand to benefit the most while targeting
others for more appropriate non-surgical intervention.

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