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. 2021 Mar 11:24:216-221.
doi: 10.1016/j.jor.2021.03.001. eCollection 2021 Mar-Apr.

Development of a multivariable prediction model for early revision of total knee arthroplasty - The effect of including patient-reported outcome measures

J D Andersen  1 , S Hangaard  1   2 , A A Ø Buus  3 , M Laursen  3   4 , O K Hejlesen  1 , A El-Galaly  3   4
Affiliations

Development of a multivariable prediction model for early revision of total knee arthroplasty - The effect of including patient-reported outcome measures

J D Andersen et al. J Orthop. .

Abstract

Background: Revision TKA is a serious adverse event with substantial consequences for the patient. As revision is becoming increasingly common in patients under 65 years, the need for improved preoperative patient selection is imminently needed. Therefore, this study aimed to identify the most important factors of early revision and to develop a prediction model of early revision including assessment of the effect of incorporating data on patient-reported outcome measures (PROMs).

Material and methods: A cohort of 538 patients undergoing primary TKA was included. Multiple logistic regression using forward selection of variables was applied to identify the best predictors of early revision and to develop a prediction model. The model was internally validated with stratified 5-fold cross-validation. This procedure was repeated without including data on PROMs to develop a model for comparison. The models were evaluated on their discriminative capacity using area under the receiver operating characteristic curve (AUC).

Results: The most important factors of early revision were age (OR 0.63 [0.42, 0.95]; P = 0.03), preoperative EQ-5D (OR 0.07 [0.01, 0.51]; P = 0.01), and number of comorbidities (OR 1.01 [0.97, 1.25]; P = 0.15). The AUCs of the models with and without PROMs were 0.65 and 0.61, respectively. The difference between the AUCs was not statistically significant (P = 0.32).

Conclusions: Although more work is needed in order to reach a clinically meaningful quality of the predictions, our results show that the inclusion of PROMs seems to improve the quality of the prediction model.

Keywords: Knee osteoarthritis; Machine learning; Prediction model; Revision; TKA.

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Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
Flow-chart illustrating the retrieval and exclusion process of the dataset.
Fig. 2
Fig. 2
Receiver Operating Characteristic (ROC)-curve. Area Under the Curve (AUC) with and without PROMs. With PROMs AUC = 0.65. Without PROMs AUC = 0.61. Blue line = with PROMs, red line = without PROMs, yellow line = reference line (no model).
See this image and copyright information in PMC

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