Optimizing the Definition of Proximal Junctional Kyphosis: A Sensitivity Analysis

Diagnostic binary threshold analysis.(1) Perform a sensitivity analysis demonstrating the test performance metrics for any combination of proximal junctional angle (PJA) magnitude and change; (2) Propose a new proximal junctional kyphosis (PJK) criteria.Previous definitions of PJK have been arbitrarily selected and then tested through retrospective case series, often showing little correlation with clinical outcomes.Surgically treated adult spinal deformity patients (≥4 levels fused) enrolled into a prospective, multicenter database were evaluated at a minimum 2-year follow-up for proximal junctional failure (PJF). Using PJF as the outcome of interest, test performance metrics including sensitivity, positive predictive value, and F1 metrics (harmonic mean of precision and recall) were calculated for all combinations of PJA magnitude and change using different combinations of perijunctional vertebrae. The combination with the highest F1 score was selected as the new PJK criteria. Performance metrics of previous PJK definitions and the new PJK definition were compared.Of the total, 669 patients were reviewed. PJF rate was 10%. Overall, the highest F1 scores were achieved when the upper instrumented vertebrae -1 (UIV-1)/UIV+2 angle was measured. For lower thoracic cases, out of all the PJA and magnitude/change combinations tested, a UIV-1/UIV+2 magnitude of -28° and a change of -20° was associated with the highest F1 score. For upper thoracic cases, a UIV-1/UIV+2 magnitude of -30° and a change of -24° were associated with the highest F1 score. Using PJF as the outcome, patients meeting this new criterion (11.5%) at 6 weeks had the lowest survival rate (74.7%) at 2 years postoperative, compared with Glattes (84.4%) and Bridwell (77.4%).Out of all possible PJA magnitude and change combinations, without stratifying by upper thoracic versus lower thoracic fusions, a magnitude of ≤-28° and a change of ≤-22° provide the best test performance metrics for predicting PJF.