• Mitsuru Yagi, Nobuyuki Fujita, Eijiro Okada, Osahiko Tsuji, Narihito Nagoshi, Takashi Asazuma, Ken Ishii, Masaya Nakamura, Morio Matsumoto, and Kota Watanabe.
• Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan.
• Spine. 2018 Jun 1; 43 (11): 767-773.

Study DesignMulticenter retrospective study.ObjectiveTo validate and improve the predictive model for proximal junctional failure (PJF) with or without the bone mineral density (BMD) score.Summary Of Background DataPJF is a serious complication of surgery for adult spinal deformity (ASD). A predictive model for PJF was recently reported that has good accuracy, but does not include BMD, a known PJF risk factor, as a variable.MethodsWe included 145 surgically treated ASD patients who were older than 50 at the time of surgery and had been followed up for at least 2 years. Variables included age, sex, body mass index (BMI), fusion level, upper and lower instrumented vertebral (UIV and LIV) level, primary or revision surgery, pedicle subtraction osteotomy (PSO), Schwab-SRS type, and BMD. PJF was defined as a ≥ 20° increase from baseline (immediately postoperative) of the proximal junctional angle with concomitant deterioration of at least 1 SRS-Schwab sagittal modifier grade, or any proximal junctional kyphosis requiring revision. Decision-making trees were constructed using the C5.0 algorithm with 10 different bootstrapped models, and validated by a 7:3 data split for training and testing; 112 patients were categorized as training and 33 as testing samples.ResultsPJF incidence was 20% in the training samples. Univariate analyses showed that BMD, BMI, pelvic tilt (PT), UIV level, and LIV level were PJF risk factors. Our predictive model was 100% accurate in the testing samples with an AUC of 1.0, indicating excellent fit. The best predictors were (strongest to weakest): PT, BMD, LIV level (pelvis), UIV level (lower thoracic), PSO, global alignment, BMI, pelvic incidence minus lumbar lordosis, and age.ConclusionA successful model was developed for predicting PJF that included BMD. Our model could inform physicians about patients with a high risk of developing PJF in the perioperative period.Level Of Evidence4.

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