The incidence of cervical spine surgeries has been steadily increasing over the last 20 years. Most of the recipients are older than 65 years old, a population projected to reach 83.4 million in the United States by the year 2050. Previous studies show that hospital readmissions from posterior cervical spine surgeries in particular occur at the highest frequency in this age group and have multidimensional contributing factors. The most common factors in 30-day and 90-day hospital readmissions are post-operative infections and wound dehiscence, which are largely preventable. Peripheral but significant contributing factors include payer status, facility type, comorbidity status, etc. Analysis of these factors on a systems level is critical to constructing a singular statistical model to reduce readmissions for the purposes of optimizing patient outcomes and hospital resource management. We propose structural equation modeling (SEM) as a novel approach to comprehensively evaluate hospital readmissions. Data was collected from the National Readmissions Database (NRD) in the year 2013, which is an all-payer database of inpatient care and represents almost half of the total US hospitalizations. Adults 18 years and older were included and outliers, missed data, redundancies, and inconsistencies were addressed. Predictive factors included in the model were patient demographics, diagnostic and procedural information, as well as hospital characteristics. Outcome variables include hospital mortality, 30-day readmissions, and 90-day readmissions. A more complex analysis of direct and peripheral contributing factors for readmissions from posterior cervical spine surgeries is imperative to create a comprehensive predictive model. This new model can be used to implement administrative changes and changes in procedural protocol, including post- and pre-operative prophylaxis. SEM has been applied previously for assessment of atrial fibrillation readmissions and it stands to reason that our model could be extrapolated to readmissions in other surgical specialties or specific types of inpatient complications. If our model is successful in optimizing resource management for hospitals, it would be possible to clearly understand the allocation of funding within the hospital which could lead to targeted decreases in costs of healthcare for the hospital, insurance company, and patient. In anticipation of the possible changes in health policy climate which may restrict funding for state healthcare programs in states which have the largest populations, efficient utilization of hospital resources and patient outcomes is of utmost priority.
