Outcomes in Unicameral Bone Cyst Management Utilizing a Decision Tree Algorithm Approach

Objective To determine if utilization of a decision tree algorithm could improve the healing rate of unicameral bone cysts (UBCs). Methods Creation of the decision tree algorithm was based on previous literature and assessment of our clinical practice. Demographic, treatment, and radiographic data were collected retrospectively. Radiographic healing was determined using the Neer classification. Healing rate, as well as clinical/radiographic characteristics, were compared based on adherence to the treatment algorithm. Results Forty-seven subjects were included. Mean age at initial surgery was 10.0 ± 3.3 years in children following the algorithm and 9.1 ± 3.2 years in those deviating from the algorithm (p = 0.393). Follow-up was found to be similar among those following the algorithm (37.5 ± 15.8 months) and those deviating from the algorithm (45.2 ± 24.6 months), p = 0.38. Children who followed the algorithm healed at a rate of 75%, while children who deviated from the algorithm healed at a rate 67% (p = 0.552). Conclusions Although we reject our hypothesis that a decision tree algorithm for the management of UBCs in the pediatric population could improve the healing rate, we believe that we uncovered some utility in applying an algorithm to this pathology. Our algorithm was designed to minimize risk to the child and maximize healing with the least number of surgical events. Treating surgeons should consider this proposed pathway to determine the best treatment and to help families understand that these lesions rarely heal with a single-event surgery.