Christopher P Ames1, Justin S Smith2, Ferran Pellisé3, Michael Kelly4, Jeffrey L Gum5, Ahmet Alanay6, Emre Acaroğlu7, Francisco Javier Sánchez Pérez-Grueso8, Frank S Kleinstück9, Ibrahim Obeid10, Alba Vila-Casademunt11, Christopher I Shaffrey2, Douglas C Burton12, Virginie Lafage13, Frank J Schwab13, Christopher I Shaffrey2, Shay Bess14, Miquel Serra-Burriel15. 1. Department of Neurosurgery, University of California San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA. 2. Department of Neurosurgery, University of Virginia Medical Center, PO Box 800212, Charlottesville, VA, 22908, USA. 3. Spine Surgery Unit, Hospital Vall d'Hebron, 119-129, Traumatology Building 2nd Floor, 08035, Barcelona, Spain. 4. Department of Orthopaedic Surgery, Washington University, 4400 Clayton Ave, St Louis, MO, 63110, USA. 5. Norton Leatherman Spine Center, 210 East Gray Street, Suite 900, Louisville, KY, 40205, USA. 6. Department of Orthopedics and Traumatology, Acibadem University, Büyükdere cad, 40 Maslak, 344457, Istanbul, Turkey. 7. Ankara ARTES Spine Center, Iran Caddesi 45/2, Kavaklidere, 06700, Ankara, Turkey. 8. Spine Surgery Unit, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain. 9. Spine Center Division, Department of Orthopedics and Neurosurgery, Schulthess Klinik, Lengghalde 2, 8008, Zurich, Switzerland. 10. Spine Surgery Unit, Bordeaux University Hospital, Place Amélie Raba Léon, 3300, Bordeaux, France. 11. Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain. 12. Department of Orthopaedic Surgery, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS, 66103, USA. 13. Department of Orthopaedic Surgery, Hospital for Special Surgery, 541 E 71st St 4th Floor, New York, NY, 10021, USA. 14. Denver International Spine Center, Presbyterian St. Luke's/Rocky Mountain Hospital for Children, 1601 E 19th Ave #6250, Denver, CO, 80218, USA. 15. Department of Economics and Business, Center for Research in Health and Economics, Universitat Pompeu Fabra, Office 23.111 Merce Rodoreda Building (Ciutadella Campus), Ramon Trias Fargas, 25-27, 08005, Barcelona, Spain. miquel.serrab@upf.edu.
Abstract
PURPOSE: Health-related quality of life (HRQL) instruments are essential in value-driven health care, but patients often have more specific, personal priorities when seeking surgical care. The Scoliosis Research Society-22R (SRS-22R), an HRQL instrument for spinal deformity, provides summary scores spanning several health domains, but these may be difficult for patients to utilize in planning their specific care goals. Our objective was to create preoperative predictive models for responses to individual SRS-22R questions at 1 and 2 years after adult spinal deformity (ASD) surgery to facilitate precision surgical care. METHODS: Two prospective observational cohorts were queried for ASD patients with SRS-22R data at baseline and 1 and 2 years after surgery. In total, 150 covariates were used in training machine learning models, including demographics, surgical data and perioperative complications. Validation was accomplished via an 80%/20% data split for training and testing, respectively. Goodness of fit was measured using area under receiver operating characteristic (AUROC) curves. RESULTS: In total, 561 patients met inclusion criteria. The AUROC ranged from 56.5 to 86.9%, reflecting successful fits for most questions. SRS-22R questions regarding pain, disability and social and labor function were the most accurately predicted. Models were less sensitive to questions regarding general satisfaction, depression/anxiety and appearance. CONCLUSIONS: To the best of our knowledge, this is the first study to explicitly model the prediction of individual answers to the SRS-22R questionnaire at 1 and 2 years after deformity surgery. The ability to predict individual question responses may prove useful in preoperative counseling in the age of individualized medicine. These slides can be retrieved under Electronic Supplementary Material.
PURPOSE: Health-related quality of life (HRQL) instruments are essential in value-driven health care, but patients often have more specific, personal priorities when seeking surgical care. The Scoliosis Research Society-22R (SRS-22R), an HRQL instrument for spinal deformity, provides summary scores spanning several health domains, but these may be difficult for patients to utilize in planning their specific care goals. Our objective was to create preoperative predictive models for responses to individual SRS-22R questions at 1 and 2 years after adult spinal deformity (ASD) surgery to facilitate precision surgical care. METHODS: Two prospective observational cohorts were queried for ASDpatients with SRS-22R data at baseline and 1 and 2 years after surgery. In total, 150 covariates were used in training machine learning models, including demographics, surgical data and perioperative complications. Validation was accomplished via an 80%/20% data split for training and testing, respectively. Goodness of fit was measured using area under receiver operating characteristic (AUROC) curves. RESULTS: In total, 561 patients met inclusion criteria. The AUROC ranged from 56.5 to 86.9%, reflecting successful fits for most questions. SRS-22R questions regarding pain, disability and social and labor function were the most accurately predicted. Models were less sensitive to questions regarding general satisfaction, depression/anxiety and appearance. CONCLUSIONS: To the best of our knowledge, this is the first study to explicitly model the prediction of individual answers to the SRS-22R questionnaire at 1 and 2 years after deformity surgery. The ability to predict individual question responses may prove useful in preoperative counseling in the age of individualized medicine. These slides can be retrieved under Electronic Supplementary Material.
Entities:
Keywords:
Adult spinal deformity; Individualized medicine; Outcomes; Predictive analytics; Scoliosis Research Society-22R (SRS-22R) questionnaire; Surgery
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