Prediction Models for Patients with Arthroplasty: An Exploration of State-of-the-art

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Attar Mahay Sheetal, Dr. K. Sree Kumar


Arthroplasty is an operative procedure that involves remodeling, realignment and replacement of damaged surface of a bone with man-made, long-lasting material to reduce pain and restore function of a joint. The common arthroplasty surgery includes knee, hip and shoulder replacement. Surgical approaches used in arthroplasty are cemented, uncemented and hybrid joint prosthesis. The choice on the approach to deploy depends on the experience of the orthopedic surgeon and the patient physiology. Also, patient satisfaction after surgery, preoperative and postoperative requirements and duration of stay of a patient at the hospital depends on the deployed approach. The cost of the operation and medications depends on the approach and the duration of stay of the patient at the hospital. To ensure accuracy in the surgical approach to be deployed, pre and post operative requirements, and duration of stay of a patient in the hospital and a greater patient satisfaction after surgery, an efficient predictive model is required. Literature shows that, a number of prediction models have been proposed with the need for more ideal solutions. This paper presents the various prediction models in arthroplasty, taxonomy of arthroplasty, research gap and opportunities.

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