Predictors of Increased Hospital Stay in Adolescent Idiopathic Scoliosis Patients Undergoing Posterior Spinal Fusion: Analysis of National Database.

Abstract

Analysis of population-based national hospital discharge data collected for the Nationwide Inpatient Sample (NIS). To examine the predictors of increased hospital stay in adolescent idiopathic scoliosis (AIS) patients undergoing posterior spinal fusion. As policy makers and hospitals are increasingly looking to cut costs, length of stay (LOS) after surgery has come into focus as an area for improvement. Despite this, there is limited research about the factors contributing to increased LOS for AIS patients undergoing posterior spinal fusion. The Nationwide Inpatient Sample was used to identify pediatric patients with idiopathic scoliosis who underwent posterior spinal fusion from 2004 to 2009, using the International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) codes. Patient demographics, surgical variables, hospital characteristics, and in-hospital outcomes were retrieved. LOS was divided into two groups (longer- and shorter-stay groups) by its average. Longer stay was defined as ≥6 days. Multivariable logistic regression analysis was performed to identify the predictors of increased LOS in AIS patients undergoing posterior spinal fusion. Significant predictors of increased LOS in posterior spinal fusion for AIS patients included increased Elixhauser Comorbidity Score, number of fused levels ≥9 vertebrae, teaching hospital status, in-hospital complications, and nonroutine disposition. Wound-related complications were the strongest predictor and patients with wound-related complications were 3.14-fold more likely to have an increased LOS compared to those without wound-related complications. This study identified significant predictors of increased hospital stay in posterior spinal fusion for pediatric patients with idiopathic scoliosis and patients at higher risk of longer hospitalization can be recognized. Eventually these data are expected to help optimize LOS and cost containment.