The Journal of Arthroplasty , Volume 34 , Issue 4 , 663 - 670

Internal Validation of a Predictive Model for Satisfaction After Primary Total Knee Arthroplasty

Kunze, Kyle N. et al.
Knee

Background

As the number of total knee arthroplasty (TKA) procedures continues to rise in the context of bundled payment models, patients dissatisfied postoperatively that require additional care will impose additional cost to the healthcare system. The purpose of this study is to internally validate a predictive model for postoperative patient satisfaction after TKA.

Methods

In total, 484 consecutive primary TKA patients between January 2014 and January 2016 were included. Patients were stratified into 4 risk tiers based on scores of a retrospectively applied, 11-component novel knee survey for postoperative satisfaction: low risk (>96.5), mild risk (75-96.4), moderate risk (60-74.9), and high risk (<60). Binary logistic and multivariate linear regression models were constructed to determine whether the survey was predictive of satisfaction. A receiver operator curve was constructed to determine a threshold score below which patients were likely to experience postoperative dissatisfaction.

Results

The mean (±standard deviation) age was 66.3 ± 9.2 years (range 31.7-100.1) and mean body mass index was 34.2 ± 8.2 kg/m2 (range 16.2-68.4). A knee survey score of 96.5 conferred a 97.5% sensitivity and 95.7% negative predictive value for satisfaction. Patients with higher knee survey scores had greater odds (odds ratio 1.03, 95% confidence interval 1.01-1.06, P = .003) of postoperative satisfaction. Increasing risk tier was significantly associated with decreased satisfaction (low risk 95.7%, mild risk 93.8%, moderate risk 86.4%, and high risk 80.4%; P = .007). The knee survey was not significantly correlated with complications (r = −0.43, P = .32).

Conclusion

This novel knee survey conferred a 97.5% sensitivity and 95.7% negative predictive value in identifying at-risk patients for postoperative dissatisfaction after primary TKA.


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