• Christopher P Ames, Justin S Smith, Ferran Pellisé, Michael P Kelly, Jeffrey L Gum, Ahmet Alanay, Emre Acaroğlu, Pérez-Grueso Francisco Javier Sánchez FJS Spine Surgery Unit, Hospital Universitario La Paz, Paseo de la Castellana, Madrid, Spain., Frank S Kleinstück, Ibrahim Obeid, Alba Vila-Casademunt, Douglas C Burton, Virginie Lafage, Frank J Schwab, Christopher I Shaffrey, Shay Bess, Miquel Serra-Burriel, and European Spine Study Group, International Spine Study Group.
• Department of Neurosurgery, University of California San Francisco, San Francisco, CA.
• Spine. 2019 Aug 15; 44 (16): 1144-1153.

Study DesignRetrospective analysis of prospectively-collected, multicenter adult spinal deformity (ASD) databases.ObjectiveTo predict the likelihood of reaching minimum clinically important differences in patient-reported outcomes after ASD surgery.Summary Of Background DataASD surgeries are costly procedures that do not always provide the desired benefit. In some series only 50% of patients achieve minimum clinically important differences in patient-reported outcomes (PROs). Predictive modeling may be useful in shared-decision making and surgical planning processes. The goal of this study was to model the probability of achieving minimum clinically important differences change in PROs at 1 and 2 years after surgery.MethodsTwo prospective observational ASD cohorts were queried. Patients with Scoliosis Research Society-22, Oswestry Disability Index , and Short Form-36 data at preoperative baseline and at 1 and 2 years after surgery were included. Seventy-five variables were used in the training of the models including demographics, baseline PROs, and modifiable surgical parameters. Eight predictive algorithms were trained at four-time horizons: preoperative or postoperative baseline to 1 year and preoperative or postoperative baseline to 2 years. External validation was accomplished via an 80%/20% random split. Five-fold cross validation within the training sample was performed. Precision was measured as the mean average error (MAE) and R values.ResultsFive hundred seventy patients were included in the analysis. Models with the lowest MAE were selected; R values ranged from 20% to 45% and MAE ranged from 8% to 15% depending upon the predicted outcome. Patients with worse preoperative baseline PROs achieved the greatest mean improvements. Surgeon and site were not important components of the models, explaining little variance in the predicted 1- and 2-year PROs.ConclusionWe present an accurate and consistent way of predicting the probability for achieving clinically relevant improvement after ASD surgery in the largest-to-date prospective operative multicenter cohort with 2-year follow-up. This study has significant clinical implications for shared decision making, surgical planning, and postoperative counseling.Level Of Evidence4.

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