Developing a personalized outcome prediction tool for knee arthroplasty. (14th September 2020)

• Record Type: Journal Article
• Authors: Anis, Hiba K.; Strnad, Gregory J.; Klika, Alison K.; Zajichek, Alexander; Spindler, Kurt P.; Barsoum, Wael K.; Higuera, Carlos A.; Piuzzi, Nicolas S.
• Is Part Of: Bone & joint journal. Volume 102B:Number 9(2020)
• Page Start: 1183
• Page End: 1193
• Publication Date: 2020-09-14
• Subjects: Knee arthroplasty -- Patient-reported outcomes -- Predictive models -- Outcome prediction tool; Bones -- Surgery -- Periodicals; Joints -- Surgery -- Periodicals; Orthopedic surgery -- Periodicals; 617.47005
• DOI: 10.1302/0301-620X.102B9.BJJ-2019-1642.R1
• Languages: English
• ISSNs: 2049-4394

Abstract

Abstract : Aims: The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods: Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5, 958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2, 391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC. Results: Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion: The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193.