Development of Deployable Predictive Models for Minimal Clinically Important Difference Achievement Across the Commonly Used Health-related Quality of Life Instruments in Adult Spinal Deformity Surgery. Issue 16 (15th August 2019)

• Record Type: Journal Article
• Authors: Ames, Christopher P.; Smith, Justin S.; Pellisé, Ferran; Kelly, Michael P.; Gum, Jeffrey L.; Alanay, Ahmet; Acaroğlu, Emre; Pérez-Grueso, Francisco Javier Sánchez; Kleinstück, Frank S.; Obeid, Ibrahim; Vila-Casademunt, Alba; Burton, Douglas C.; Lafage, Virginie; Schwab, Frank J.; Shaffrey, Christopher I.; Bess, Shay; Serra-Burriel, Miquel
• Is Part Of: Spine. Volume 44:Issue 16(2019)
• Publication Date: 2019-08-15
• Subjects: adult spinal deformity surgery -- MCID -- predictive modeling -- prognosis -- shared decision-making; Spine -- Abnormalities -- Periodicals; Spine -- Diseases -- Periodicals; Spine -- Surgery -- Periodicals; 616.73005
• DOI: 10.1097/BRS.0000000000003031
• Languages: English
• ISSNs: 0362-2436

Abstract

Abstract : Study Design: Retrospective analysis of prospectively-collected, multicenter adult spinal deformity (ASD) databases. Objective: To predict the likelihood of reaching minimum clinically important differences in patient-reported outcomes after ASD surgery. Summary of Background Data: ASD 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. Methods: Two 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 2 values. Results: Five hundred seventy patients were included in the analysis. Models with the lowest MAE were selected; R 2 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. Conclusion: We 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 Evidence: 4 Abstract : Supplemental Digital Content is available in the textSeveral successful predictive models on the likelihood of significant improvement following ASD surgery were developed and tested. Five hundred seventy patients with 2-year follow-up were included. To our knowledge, the present study is the largest predictive effort in the study of PROs in ASD surgery.