TY - JOUR
T1 - 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
AU - Ames, Christopher P.
AU - Smith, Justin S.
AU - Pellisé, Ferran
AU - Kelly, Michael P.
AU - Gum, Jeffrey L.
AU - Alanay, Ahmet
AU - Acaroǧlu, Emre
AU - Pérez-Grueso, Francisco Javier Sánchez
AU - Kleinstück, Frank S.
AU - Obeid, Ibrahim
AU - Vila-Casademunt, Alba
AU - Burton, Douglas C.
AU - Lafage, Virginie
AU - Schwab, Frank J.
AU - Shaffrey, Christopher I.
AU - Bess, Shay
AU - Serra-Burriel, Miquel
N1 - Funding Information:
From the *Department of Neurosurgery, University of California San Francisco, San Francisco, CA; †Department of Neurosurgery, University of Virginia Medical Center, Charlottesville, VA; zSpine Surgery Unit, Hospital Vall d’Hebron, Barcelona, Spain; §Department of Orthopaedic Surgery, Washington University,StLouis, MO; {Norton Leatherman Spine Center, Louisville, KY; ||Department of Orthopedics and Traumatology, Acibadem University, Büyük-dere cad, Istanbul, Turkey; **Ankara ARTES Spine Center, Ankara, Turkey; ††Spine Surgery Unit, Hospital Universitario La Paz, Paseo de la Castellana, Madrid, Spain; zzSpine Center Division, Department of Orthopedics and Neurosurgery, Schulthess Klinik, Zürich, Switzerland; §§Spine Surgery Unit, Bordeaux University Hospital, Bordeaux, France; {{Vall d’Hebron Institute of Research (VHIR) Barcelona, Spain; ||||Department of Orthopaedic Surgery, University of Kansas Medical Center, Kansas City, KS; ***Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY; †††Denver Denver International Spine Center, Presbyterian St. Luke’s/Rocky Mountain Hospital for Children, Denver, CO; and zzzCenter for Research in Health and Economics, Universitat Pompeu Fabra, Barcelona, Spain. Acknowledgment date: December 3, 2018. Acceptance date: February 12, 2019. The manuscript submitted does not contain information about medical device(s)/drug(s). The International Spine Study Group Foundation receives funding support from DePuy Synthes, K2 M, Nuvasive, Orthofix, and Zimmer Biomet. The European Spine Study Group receives funding support from DePuy Synthes and Medtronic. Additional support was provided through Project PI16/ 01283, funded by Instituto de Salud Carlos III and co-funded by European Union (ERDF/ESF). Relevant financial activities outside the submitted work: board membership, consultancy, royalties, grants, stocks, payment for lecture. Address correspondence and reprint requests to Miquel Serra-Burriel, PhD, Center for Research in Health Economics, Department of Economics and Business, Universitat Pompeu Fabra, Office 23.111 Merce Rodoreda Building (Ciutadella Campus), Ramon Trias Fargas, 25-27, Barcelona 08005, Spain; E-mail: miquel.serrab@upf.edu
Publisher Copyright:
© 2019 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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 R2 values.Results.Five hundred seventy patients were included in the analysis. Models with the lowest MAE were selected; R2 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.
AB - 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 R2 values.Results.Five hundred seventy patients were included in the analysis. Models with the lowest MAE were selected; R2 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.
KW - MCID
KW - adult spinal deformity surgery
KW - predictive modeling
KW - prognosis
KW - shared decision-making
UR - http://www.scopus.com/inward/record.url?scp=85071067168&partnerID=8YFLogxK
U2 - 10.1097/BRS.0000000000003031
DO - 10.1097/BRS.0000000000003031
M3 - Article
C2 - 30896589
AN - SCOPUS:85071067168
VL - 44
SP - 1144
EP - 1153
JO - Spine
JF - Spine
SN - 0362-2436
IS - 16
ER -