Predictive model for distal junctional kyphosis after cervical deformity surgery

Publication date: December 2018

Source: The Spine Journal, Volume 18, Issue 12

Author(s): Peter G. Passias, Dennis Vasquez-Montes, Gregory W. Poorman, Themistocles Protopsaltis, Samantha R. Horn, Cole A. Bortz, Frank Segreto, Bassel Diebo, Chris Ames, Justin Smith, Virginie LaFage, Renaud LaFage, Eric Klineberg, Chris Shaffrey, Shay Bess, Frank Schwab, for the ISSG

Abstract

Background Context

Distal junctional kyphosis (DJK) is a primary concern of surgeons correcting cervical deformity. Identifying patients and procedures at higher risk of developing this condition is paramount in improving patient selection and care.

Purpose

The present study aimed to develop a risk index for DJK development in the first year after surgery.

Study Design/Setting

This is a retrospective review of a prospective multicenter cervical deformity database.

Patient Sample

Patients over the age of 18 meeting one of the following deformities were included in the study: cervical kyphosis (C2–7 Cobb angle>10°), cervical scoliosis (coronal Cobb angle>10°), positive cervical sagittal imbalance (C2–C7 sagittal vertical axis (SVA)>4 cm or T1-C6>10°), or horizontal gaze impairment (chin-brow vertical angle>25°).

Outcome Measures

Development of DJK at any time before 1 year.

Methods

Distal junctional kyphosis was defined by both clinical diagnosis (by enrolling surgeon) and post hoc identification of development of an angle<−10° from the end of fusion construct to the second distal vertebra, as well as a change in this angle by <−10° from baseline. Conditional Inference Decision Trees were used to identify factors predictive of DJK incidence and the cut-off points at which they have an effect. A conditional Variable-Importance table was constructed based on a non-replacement sampling set of 2,000 Conditional Inference Trees. Twelve influencing factors were found; binary logistic regression for each variable at significant cutoffs indicated their effect size.

Results

Statistical analysis included 101 surgical patients (average age: 60.1 years, 58.3% female, body mass index: 30.2) undergoing long cervical deformity correction (mean levels fused: 7.1, osteotomy used: 49.5%, approach: 46.5% posterior, 17.8% anterior, 35.7% combined). In 2 years after surgery, 6% of patients were diagnosed with clinical DJK; however, 23.8% of patients met radiographic definition for DJK. Patients with neurologic symptoms were at risk of DJK (odds ratio [OR]: 3.71, confidence interval [CI]: 0.11–0.63). However, no significant relationship was found between osteoporosis, age, and ambulatory status with DJK incidence. Baseline radiographic malalignments were the most numerous and strong predictors for DJK: (1) C2-T1 tilt>5.33 (OR: 6.94, CI: 2.99–16.14); (2) kyphosis<−50.6° (OR: 5.89, CI: 0.07–0.43); (3) C2–C7 lordosis<−12° (OR: 5.7, CI: 0.08–0.41); (4) T1 slope minus cervical lordosis>36.4 (OR: 5.6, CI: 2.28–13.57); (5) C2-C7 SVA>56.3° (OR: 5.4, CI: 2.20–13.23); and (6) C4_Tilt>56.7 (OR: 5.0, CI: 1.90–13.1). Clinically, combined approaches (OR: 2.67, CI: 1.21–5.89) and usage of Smith-Petersen osteotomy (OR: 2.55, CI: 1.02–6.34) were the most important predictors of DJK.

Conclusions

In a surgical cohort of patients with cervical deformity, we found a 23.8% incidence of DJK. Different procedures and patient malalignment predicted incidence of DJK up to 1 year. Preoperative T1 slope-cervical lordosis, cervical kyphosis, SVA, and cervical lordosis all strongly predicted DJK at specific cut-off points. Knowledge of these factors will potentially help direct future study and strategy aimed at minimizing this potentially dramatic occurrence.

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