Development of Validated Computer-based Preoperative Predictive Model for Proximal Junction Failure (PJF) or Clinically Significant PJK With 86% Accuracy Based on 510 ASD Patients With 2-year Follow-up.
METHODS
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).


BACKGROUND
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.


CONCLUSIONS
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.


METHODS
3.
University of California, San Diego, School of Medicine, La Jolla, CA.
Justin K
JK
Scheer
Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA.
Joseph A
JA
Osorio
Department of Neurosurgery, University of Virginia Health System, Charlottesville, VA.
Justin S
JS
Smith
Spine Service, Hospital for Special Surgery, New York, NY.
Frank
F
Schwab
Spine Service, Hospital for Special Surgery, New York, NY.
Virginie
V
Lafage
Department of Orthopaedic Surgery, Oregon Health & Science University, Portland, OR.
Robert A
RA
Hart
Department of Orthopaedic Surgery, NYU Hospital for Joint Diseases, New York, NY.
Shay
S
Bess
Department of Orthopaedic Surgery, NYU Hospital for Joint Diseases, New York, NY.
Breton
B
Line
Spine Service, Hospital for Special Surgery, New York, NY.
Bassel G
BG
Diebo
Department of Orthopaedic Surgery, NYU Hospital for Joint Diseases, New York, NY.
Themistocles S
TS
Protopsaltis
Department of Orthopaedic Surgery, Johns Hopkins University, Baltimore, MD.
Amit
A
Jain
Department of Neurosurgery, University of Virginia Health System, Charlottesville, VA.
Tamir
T
Ailon
Department of Orthopaedic Surgery, University of Kansas Medical Center, Kansas City, KS.
Douglas C
DC
Burton
Department of Neurosurgery, University of Virginia Health System, Charlottesville, VA.
Christopher I
CI
Shaffrey
Department of Orthopaedic Surgery, University of California, Davis, CA.
Eric
E
Klineberg
Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA.
Christopher P
CP
Ames
D000293:Adolescent; D000328:Adult; D000368:Aged; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D007738:Kyphosis / Q000601:surgery*; D008141:Lordosis / Q000601:surgery*; D008159:Lumbar Vertebrae / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D009141:Musculoskeletal System / Q000601:surgery*; D010027:Osteotomy / Q000379:methods; D011183:Postoperative Complications; D012189:Retrospective Studies; D012307:Risk Factors; D013123:Spinal Fusion / Q000379:methods; D013904:Thoracic Vertebrae / Q000601:surgery*; D055815:Young Adult