RT Journal Article
SR Electronic
T1 Prediction of In-Hospital Mortality Following Vertebral Fracture Fixation in Patients With Ankylosing Spondylitis or Diffuse Idiopathic Skeletal Hyperostosis: Machine Learning Analysis
JF International Journal of Spine Surgery
JO Int J Spine Surg
FD International Society for the Advancement of Spine Surgery
SP 62
OP 68
DO 10.14444/8567
VO 18
IS 1
A1 Andrew Cabrera
A1 Alexander Bouterse
A1 Michael Nelson
A1 Coleman Dietrich
A1 Jacob Razzouk
A1 Udochukwu Oyoyo
A1 Christopher M. Bono
A1 Olumide Danisa
YR 2024
UL http://ijssurgery.com//content/18/1/62.abstract
AB Background Ankylosing spondylitis (AS) and diffuse idiopathic skeletal hyperostosis (DISH) are distinct pathological entities that similarly increase the risk of vertebral fractures. Such fractures can be clinically devastating and frequently portend significant neurological injury, thus making their prevention a critical focus. Of particular significance, spinal fractures in patients with AS or DISH carry a considerable risk of mortality, with reports on 1-year injury-related deaths ranging from 24% to 33%. As such, the purpose of this study was to conduct machine learning (ML) analysis to predict postoperative mortality in patients with AS or DISH using the Nationwide Inpatient Sample Healthcare Cost and Utilization Project (HCUP-NIS) database.Methods HCUP-NIS was queried to identify adult patients carrying a diagnosis of AS or DISH who were admitted for spinal fractures and underwent subsequent fusion or corpectomy between 2016 and 2018. Predictions of in-hospital mortality in this cohort were then generated by three independent ML algorithms.Results An in-hospital mortality rate of 5.40% was observed in our selected population, including a rate of 6.35% in patients with AS, 2.81% in patients with DISH, and 8.33% in patients with both diagnoses. Increasing age, hypertension with end-organ complications, spinal cord injury, and cervical spinal fractures each carried considerable predictive importance across the algorithms utilized in our analysis. Predictions were generated with an average area under the curve of 0.758.Conclusions This study’s application of ML algorithms to predict in-hospital mortality among patients with AS or DISH identified a number of clinical risk factors relevant to this outcome.Clinical Relevance These findings may serve to provide physicians with an awareness of risk factors for in-hospital mortality and, subsequently, guide management and shared decision-making among patients with AS or DISH.Level of Evidence 4.