Incidence of Tether Breakage in Anterior Vertebral Body Tethering.

BACKGROUND: Thoracic anterior vertebral body tethering (TAVBT) is an emerging treatment for adolescent idiopathic scoliosis. Tether breakage is a known complication of TAVBT with incompletely known incidence. We aim to define the incidence of tether breakage in patients with adolescent idiopathic scoliosis who undergo TAVBT. The incidence of tether breakage in TAVBT is hypothesized to be high and increase with time postoperatively. METHODS: All patients with right-sided, thoracic curves who underwent TAVBT with at least 2 and up to 3 years of radiographic follow-up were included. Tether breakage between 2 vertebrae was defined a priori as any increase in adjacent screw angle >5 degrees from the minimum over the follow-up period. The presence and timing of tether breakage were noted for each patient. A Kaplan-Meier survival analysis was performed to calculate expected tether breakage up to 36 months. χ 2 analysis was performed to examine the relationship between tether breakage and reoperations. Independent t test was used to compare the average final Cobb angle between cohorts. RESULTS: In total, 208 patients from 10 centers were included in our review. Radiographically identified tether breakage occurred in 75 patients (36%). The initial break occurred at or beyond 24 months in 66 patients (88%). Kaplan-Meier survival analysis estimated the cumulative rate of expected tether breakage to be 19% at 24 months, increasing to 50% at 36 months. Twenty-one patients (28%) with a radiographically identified tether breakage went on to require reoperation, with 9 patients (12%) requiring conversion to posterior spinal fusion. Patients with a radiographically identified tether breakage went on to require conversion to posterior spinal fusion more often than those patients without identified tether breakage (12% vs. 2%; P =0.004). The average major coronal curve angle at final follow-up was significantly larger for patients with radiographically identified tether breakage than for those without tether breakage (31 deg±12 deg vs. 26 deg±12 deg; P =0.002). CONCLUSIONS: The incidence of tether breakage in TAVBT is high, and it is expected to occur in 50% of patients by 36 months postoperatively. LEVEL OF EVIDENCE: Level IV.

Predicting early return to the operating room in early-onset scoliosis patients using machine learning techniques.

PURPOSE: Surgical treatment of early-onset scoliosis (EOS) is associated with high rates of complications, often requiring unplanned return to the operating room (UPROR). The aim of this study was to create and validate a machine learning model to predict which EOS patients will go on to require an UPROR during their treatment course. METHODS: A retrospective review was performed of all surgical EOS patients with at least 2 years follow-up. Patients were stratified based on whether they had experienced an UPROR. Ten machine learning algorithms were trained using tenfold cross-validation on an independent training set of patients. Model performance was evaluated on a separate testing set via their area under the receiver operating characteristic curve (AUC). Relative feature importance was calculated for the top-performing model. RESULTS: 257 patients were included in the study. 146 patients experienced at least one UPROR (57%). Five factors were identified as significant and included in model training: age at initial surgery, EOS etiology, initial construct type, and weight and height at initial surgery. The Gaussian naïve Bayes model demonstrated the best performance on the testing set (AUC: 0.79). Significant protective factors against experiencing an UPROR were weight at initial surgery, idiopathic etiology, initial definitive fusion construct, and height at initial surgery. CONCLUSIONS: The Gaussian naïve Bayes machine learning algorithm demonstrated the best performance for predicting UPROR in EOS patients. Heavier, taller, idiopathic patients with initial definitive fusion constructs experienced UPROR less frequently. This model can be used to better quantify risk, optimize patient factors, and choose surgical constructs. LEVEL OF EVIDENCE: Prognostic: III.