Machine Learning–Based Identification of Distinct Risk Factors for Moderate vs Severe Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery

Dong-Ho Kang

doi:10.1016/j.spinee.2026.04.030

Machine Learning–Based Identification of Distinct Risk Factors for Moderate vs Severe Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery

2026-06-17

0

PRE

AI

D

Dong-Ho Kang

J

Jin-Sung Park

C

Chong-Suh Lee

S

Se‐Jun Park *

DOI：10.1016/j.spinee.2026.04.030

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Abstract

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Proximal junctional kyphosis (PJK) is a well-recognized complication of adult spinal deformity (ASD) surgery. However, traditional proximal junctional angle (PJA) thresholds (15°) and revision-predictive thresholds (28°) may represent distinct clinical entities with unique etiologies.

Machine Learning–Based Identification of Distinct Risk Factors for Moderate vs Severe Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery

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