Ferran Pellisé1, Alba Vila-Casademunt2, Susana Núñez-Pereira3, Sleiman Haddad3, Justin S Smith4, Michael P Kelly5, Ahmet Alanay6, Christopher Shaffrey7, Javier Pizones8, Çaglar Yilgor6, Ibrahim Obeid9, Douglas Burton10, Frank Kleinstück11, Tamas Fekete11, Shay Bess12, Munish Gupta5, Markus Loibl11, Eric O Klineberg13, Francisco J Sánchez Pérez-Grueso8, Miquel Serra-Burriel14, Christopher P Ames15. 1. Spine Surgery Unit, Vall d'Hebron University Hospital, Barcelona, Spain. 24361fpu@comb.cat. 2. Spine Research Unit, Vall d'Hebron Research Institute, Barcelona, Spain. 3. Spine Surgery Unit, Vall d'Hebron University Hospital, Barcelona, Spain. 4. Department of Neurosurgery, University of Virginia Medical Center, Charlottesville, VA, USA. 5. Department of Orthopaedic Surgery, Washington University, St Louis, MO, USA. 6. Department of Orthopedics and Traumatology, Acibadem University, Istanbul, Turkey. 7. Duke University Medical Center, Durham, NC, USA. 8. Spine Surgery Unit, La Paz University Hospital, Madrid, Spain. 9. Spine Surgery Unit, Bordeaux University Hospital, Bordeaux, France. 10. Department of Orthopaedic Surgery, University of Kansas Medical Center, Kansas City, KS, USA. 11. Spine Center Division, Schulthess Klinik, Zurich, Switzerland. 12. Denver International Spine Center, Presbyterian St. Luke's/Rocky Mountain Hospital for Children, Denver, CO, USA. 13. Department of Orthopedic Surgery, University of California Davis, Sacramento, CA, USA. 14. Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland. 15. Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA.
Abstract
BACKGROUND: Surgeons often rely on their intuition, experience and published data for surgical decision making and informed consent. Literature provides average values that do not allow for individualized assessments. Accurate validated machine learning (ML) risk calculators for adult spinal deformity (ASD) patients, based on 10 year multicentric prospective data, are currently available. The objective of this study is to assess surgeon ASD risk perception and compare it to validated risk calculator estimates. METHODS: Nine ASD complete (demographics, HRQL, radiology, surgical plan) preoperative cases were distributed online to 100 surgeons from 22 countries. Surgeons were asked to determine the risk of major complications and reoperations at 72 h, 90 d and 2 years postop, using a 0-100% risk scale. The same preoperative parameters circulated to surgeons were used to obtain ML risk calculator estimates. Concordance between surgeons' responses was analyzed using intraclass correlation coefficients (ICC) (poor < 0.5/excellent > 0.85). Distance between surgeons' and risk calculator predictions was assessed using the mean index of agreement (MIA) (poor < 0.5/excellent > 0.85). RESULTS: Thirty-nine surgeons (74.4% with > 10 years' experience), from 12 countries answered the survey. Surgeons' risk perception concordance was very low and heterogeneous. ICC ranged from 0.104 (reintervention risk at 72 h) to 0.316 (reintervention risk at 2 years). Distance between calculator and surgeon prediction was very large. MIA ranged from 0.122 to 0.416. Surgeons tended to overestimate the risk of major complications and reintervention in the first 72 h and underestimated the same risks at 2 years postop. CONCLUSIONS: This study shows that expert surgeon ASD risk perception is heterogeneous and highly discordant. Available validated ML ASD risk calculators can enable surgeons to provide more accurate and objective prognosis to adjust patient expectations, in real time, at the point of care.
BACKGROUND: Surgeons often rely on their intuition, experience and published data for surgical decision making and informed consent. Literature provides average values that do not allow for individualized assessments. Accurate validated machine learning (ML) risk calculators for adult spinal deformity (ASD) patients, based on 10 year multicentric prospective data, are currently available. The objective of this study is to assess surgeon ASD risk perception and compare it to validated risk calculator estimates. METHODS: Nine ASD complete (demographics, HRQL, radiology, surgical plan) preoperative cases were distributed online to 100 surgeons from 22 countries. Surgeons were asked to determine the risk of major complications and reoperations at 72 h, 90 d and 2 years postop, using a 0-100% risk scale. The same preoperative parameters circulated to surgeons were used to obtain ML risk calculator estimates. Concordance between surgeons' responses was analyzed using intraclass correlation coefficients (ICC) (poor < 0.5/excellent > 0.85). Distance between surgeons' and risk calculator predictions was assessed using the mean index of agreement (MIA) (poor < 0.5/excellent > 0.85). RESULTS: Thirty-nine surgeons (74.4% with > 10 years' experience), from 12 countries answered the survey. Surgeons' risk perception concordance was very low and heterogeneous. ICC ranged from 0.104 (reintervention risk at 72 h) to 0.316 (reintervention risk at 2 years). Distance between calculator and surgeon prediction was very large. MIA ranged from 0.122 to 0.416. Surgeons tended to overestimate the risk of major complications and reintervention in the first 72 h and underestimated the same risks at 2 years postop. CONCLUSIONS: This study shows that expert surgeon ASD risk perception is heterogeneous and highly discordant. Available validated ML ASD risk calculators can enable surgeons to provide more accurate and objective prognosis to adjust patient expectations, in real time, at the point of care.
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Authors: Justin K Scheer; Jessica A Tang; Justin S Smith; Eric Klineberg; Robert A Hart; Gregory M Mundis; Douglas C Burton; Richard Hostin; Michael F O'Brien; Shay Bess; Khaled M Kebaish; Vedat Deviren; Virginie Lafage; Frank Schwab; Christopher I Shaffrey; Christopher P Ames Journal: J Neurosurg Spine Date: 2013-08-23