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Current Concepts in Predictive Modeling and Artificial Intelligence

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Surgical Management of Knee Arthritis

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Abstract

Predictive modeling promises to improve our understanding of what variables influence patient satisfaction following total knee arthroplasty (TKA). Questions remain regarding the most relevant inputs and outputs for modeling outcomes in this field. The aim was to identify the predictor strategies used for systematic data collection with the highest likelihood of success in predicting clinical outcomes. A PRISMA systematic review was conducted to identify all clinical studies that had used predictive models or that assessed predictive features for outcomes after TKA between 1996 and 2020. Preoperative predictive factors strongly associated with postoperative clinical outcomes were knee pain, knee-specific Patient-Reported Outcome Measure (PROM) scores, range of motion, the severity of osteoarthritis, and mental health scores. The outcome measures that correlated best with the predictive models were improvement of PROM scores, pain scores, and patient satisfaction. Several algorithms, based on PROM improvement, patient satisfaction, or pain after TKA, have been developed to improve decision-making regarding both indications for surgery and surgical strategy. Functional features such as preoperative pain and PROM scores were highly predictive for clinical outcomes following TKA. Some variables such as demographics data or knee alignment were less strongly correlated with TKA outcomes.

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Authors and Affiliations

  1. Orthopedic Surgery Department, Lyon North University Hospital, Croix-Rousse Hospital, Hospices Civils de Lyon, Lyon, France

    Cécile Batailler & Sébastien Lustig

  2. Université Lyon, Université Claude Bernard Lyon 1, IFSTTAR, LBMC UMR_T9406, Villeurbanne, France

    Cécile Batailler & Sébastien Lustig

  3. Melbourne Orthopaedic Group, Windsor, Australia

    Timothy Lording

  4. Stryker, Amsterdam, the Netherlands

    Daniele De Massari & Sietske Witvoet-Braam

  5. University of California, San Francisco, CA, USA

    Stefano Bini

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  1. Cécile Batailler
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  3. Daniele De Massari
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  4. Sietske Witvoet-Braam
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  5. Stefano Bini
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Editors and Affiliations

  1. Department of Orthopedic Surgery, Hospital For Joint Diseases, New York, NY, USA

    Ajit J. Deshmukh

  2. University Clinical Center of Kosovo, Pristina, Kosovo

    Bujar H. Shabani

  3. Orthopedic Clinic Paulinenhilfe, Diakonie Klinikum, Stuttgart, Germany

    Wenzel Waldstein

  4. Department of Orthopedic Surgery, Johns Hopkins Bayview Medical Center, Baltimore, MD, USA

    Julius K. Oni

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Batailler, C., Lording, T., De Massari, D., Witvoet-Braam, S., Bini, S., Lustig, S. (2023). Current Concepts in Predictive Modeling and Artificial Intelligence. In: Deshmukh, A.J., Shabani, B.H., Waldstein, W., Oni, J.K. (eds) Surgical Management of Knee Arthritis. Springer, Cham. https://doi.org/10.1007/978-3-031-47929-8_7

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