PT  - JOURNAL ARTICLE
AU  - Andrew Cabrera
AU  - Alexander Bouterse
AU  - Michael Nelson
AU  - Coleman Dietrich
AU  - Jacob Razzouk
AU  - Udochukwu Oyoyo
AU  - Christopher M. Bono
AU  - Olumide Danisa
TI  - Prediction of In-Hospital Mortality Following Vertebral Fracture Fixation in Patients With Ankylosing Spondylitis or Diffuse Idiopathic Skeletal Hyperostosis: Machine Learning Analysis
AID  - 10.14444/8567
DP  - 2024 Feb 01
TA  - International Journal of Spine Surgery
PG  - 62--68
VI  - 18
IP  - 1
4099  - http://ijssurgery.com//content/18/1/62.short
4100  - http://ijssurgery.com//content/18/1/62.full
SO  - Int J Spine Surg2024 Feb 01; 18
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.