Impact of Comorbid Disease on Length of Hospitalization in Spine Fusion Patients: An HCUP-US-NIS Study

Introduction: The following is a study of the impact of comorbid conditions on hospital length of stay following spinal fusion. Methods: Surgeries were identified from the 2016 Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-US-NIS) by Medicare Severity Diagnosis Related Group (MS-DRG) codes and subdivided for analysis by fusion location and procedure approach. Length of stay was evaluated in relation to comorbid disease status, fusion location, and surgical technique. Comorbidities of interest included hypothyroidism, diabetes mellitus, hypertension, hyperlipidemia, anxiety, obesity, chronic obstructive pulmonary disease, osteoarthritis, rheumatoid arthritis, major depression, coronary atherosclerosis, arrhythmia, congestive heart failure, osteoporosis, stroke, and transient ischemic attack. Patients hospitalized longer than two months were excluded from this analysis. Results: 185,216 patients undergoing an inpatient spinal fusion were identified (Cervical 32,753, Cervicothoracic 2,633, Thoracic 2,817, Thoracolumbar 4,761, Lumbar 32,316, Lumbosacral 17,326). Each comorbid disease was found to significantly increase the length of hospital stay for at least one procedure location (p<.05), with transient ischemic attack (8.5 days in cervicothoracic cases), arrhythmia (5.4 days in thoracic cases), and chronic heart failure (4.8 days in cervicothoracic cases) associated with substantially increased duration of hospitalization. Chronic heart failure (β 2.85, SE 0.11, p <.001), stroke (β 3.05, SE 0.08, p <.001), and osteoarthritis (β 2.12, SE 0.41, p <.001) demonstrated strong positive association with increases in length of peroperative hospitalization. J Spine Res Surg 2019; 1 (2): 048-059 DOI: 10.26502/fjsrs008 Journal of Spine Research and Surgery 49 Conclusion: Preoperative comorbidities contribute variably to the length of post-spinal fusion hospital stay. With increasing trends towards predictive modeling in healthcare outcomes these conditions represent important factors for consideration in surgical planning.