How to Develop and Validate Prediction Models for Orthopedic Outcomes
Institution:
1. Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona;2. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota;3. Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota;1. Department of Orthopaedic Surgery, Maimonides Medical Center, Brooklyn, New York;2. Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, Ohio;3. Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopaedics, Baltimore, Maryland;4. Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Austin, Texas;5. Department of Orthopaedic Surgery, University of Virginia, Charlottesville, Virginia;6. Northwell Health Orthopaedics, Lenox Hill Hospital, New York, New York;1. Knee Surgery Center of the National Institute of Traumatology and Orthopedics (INTO), Rio de Janeiro, Brazil;2. University of São Paulo, Ribeirão Preto Medicine School, Brazil
Abstract:
Prediction models are common in medicine for predicting outcomes such as mortality, complications, or response to treatment. Despite the growing interest in these models in arthroplasty (and orthopaedics in general), few have been adopted in clinical practice. If robustly built and validated, prediction models can be excellent tools to support surgical decision making. In this paper, we provide an overview of the statistical concepts surrounding prediction models and outline practical steps for prediction model development and validation in arthroplasty research. Please visit the followinghttps://www.youtube.com/watch?v=9Yrit23Rkicfor a video that explains the highlights of the paper in practical terms.
