Justin K Scheer1, Joseph A Osorio2, Justin S Smith3, Frank Schwab4, Virginie Lafage4, Robert A Hart5, Shay Bess6, Breton Line6, Bassel G Diebo4, Themistocles S Protopsaltis6, Amit Jain7, Tamir Ailon3, Douglas C Burton8, Christopher I Shaffrey3, Eric Klineberg9, Christopher P Ames2. 1. University of California, San Diego, School of Medicine, La Jolla, CA. 2. Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA. 3. Department of Neurosurgery, University of Virginia Health System, Charlottesville, VA. 4. Spine Service, Hospital for Special Surgery, New York, NY. 5. Department of Orthopaedic Surgery, Oregon Health & Science University, Portland, OR. 6. Department of Orthopaedic Surgery, NYU Hospital for Joint Diseases, New York, NY. 7. Department of Orthopaedic Surgery, Johns Hopkins University, Baltimore, MD. 8. Department of Orthopaedic Surgery, University of Kansas Medical Center, Kansas City, KS. 9. Department of Orthopaedic Surgery, University of California, Davis, CA.
Abstract
STUDY DESIGN: A retrospective review of large, multicenter adult spinal deformity (ASD) database. OBJECTIVE: The aim of this study was to build a model based on baseline demographic, radiographic, and surgical factors that can predict clinically significant proximal junctional kyphosis (PJK) and proximal junctional failure (PJF). SUMMARY OF BACKGROUND DATA: PJF and PJK are significant complications and it remains unclear what are the specific drivers behind the development of either. There exists no predictive model that could potentially aid in the clinical decision making for adult patients undergoing deformity correction. METHODS: Inclusion criteria: age ≥18 years, ASD, at least four levels fused. Variables included in the model were demographics, primary/revision, use of three-column osteotomy, upper-most instrumented vertebra (UIV)/lower-most instrumented vertebra (LIV) levels and UIV implant type (screw, hooks), number of levels fused, and baseline sagittal radiographs [pelvic tilt (PT), pelvic incidence and lumbar lordosis (PI-LL), thoracic kyphosis (TK), and sagittal vertical axis (SVA)]. PJK was defined as an increase from baseline of proximal junctional angle ≥20° with concomitant deterioration of at least one SRS-Schwab sagittal modifier grade from 6 weeks postop. PJF was defined as requiring revision for PJK. An ensemble of decision trees were constructed using the C5.0 algorithm with five different bootstrapped models, and internally validated via a 70 : 30 data split for training and testing. Accuracy and the area under a receiver operator characteristic curve (AUC) were calculated. RESULTS: Five hundred ten patients were included, with 357 for model training and 153 as testing targets (PJF: 37, PJK: 102). The overall model accuracy was 86.3% with an AUC of 0.89 indicating a good model fit. The seven strongest (importance ≥0.95) predictors were age, LIV, pre-operative SVA, UIV implant type, UIV, pre-operative PT, and pre-operative PI-LL. CONCLUSION: A successful model (86% accuracy, 0.89 AUC) was built predicting either PJF or clinically significant PJK. This model can set the groundwork for preop point of care decision making, risk stratification, and need for prophylactic strategies for patients undergoing ASD surgery. LEVEL OF EVIDENCE: 3.
STUDY DESIGN: A retrospective review of large, multicenter adult spinal deformity (ASD) database. OBJECTIVE: The aim of this study was to build a model based on baseline demographic, radiographic, and surgical factors that can predict clinically significant proximal junctional kyphosis (PJK) and proximal junctional failure (PJF). SUMMARY OF BACKGROUND DATA: PJF and PJK are significant complications and it remains unclear what are the specific drivers behind the development of either. There exists no predictive model that could potentially aid in the clinical decision making for adult patients undergoing deformity correction. METHODS: Inclusion criteria: age ≥18 years, ASD, at least four levels fused. Variables included in the model were demographics, primary/revision, use of three-column osteotomy, upper-most instrumented vertebra (UIV)/lower-most instrumented vertebra (LIV) levels and UIV implant type (screw, hooks), number of levels fused, and baseline sagittal radiographs [pelvic tilt (PT), pelvic incidence and lumbar lordosis (PI-LL), thoracic kyphosis (TK), and sagittal vertical axis (SVA)]. PJK was defined as an increase from baseline of proximal junctional angle ≥20° with concomitant deterioration of at least one SRS-Schwab sagittal modifier grade from 6 weeks postop. PJF was defined as requiring revision for PJK. An ensemble of decision trees were constructed using the C5.0 algorithm with five different bootstrapped models, and internally validated via a 70 : 30 data split for training and testing. Accuracy and the area under a receiver operator characteristic curve (AUC) were calculated. RESULTS: Five hundred ten patients were included, with 357 for model training and 153 as testing targets (PJF: 37, PJK: 102). The overall model accuracy was 86.3% with an AUC of 0.89 indicating a good model fit. The seven strongest (importance ≥0.95) predictors were age, LIV, pre-operative SVA, UIV implant type, UIV, pre-operative PT, and pre-operative PI-LL. CONCLUSION: A successful model (86% accuracy, 0.89 AUC) was built predicting either PJF or clinically significant PJK. This model can set the groundwork for preop point of care decision making, risk stratification, and need for prophylactic strategies for patients undergoing ASD surgery. LEVEL OF EVIDENCE: 3.
Authors: Renaud Lafage; Justin S Smith; Jonathan Elysee; Peter Passias; Shay Bess; Eric Klineberg; Han Jo Kim; Christopher Shaffrey; Douglas Burton; Richard Hostin; Gregory Mundis; Christopher Ames; Frank Schwab; Virginie Lafage Journal: Spine Deform Date: 2021-08-30
Authors: Sohrab Virk; Uwe Platz; Shay Bess; Douglas Burton; Peter Passias; Munish Gupta; Themistocles Protopsaltis; Han Jo Kim; Justin S Smith; Robert Eastlack; Khaled Kebaish; Gregory M Mundis; Pierce Nunley; Christopher Shaffrey; Jeffrey Gum; Virginie Lafage; Frank Schwab Journal: J Spine Surg Date: 2021-03
Authors: Rushikesh S Joshi; Darryl Lau; Justin K Scheer; Miquel Serra-Burriel; Alba Vila-Casademunt; Shay Bess; Justin S Smith; Ferran Pellise; Christopher P Ames Journal: Spine Deform Date: 2021-05-18