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Background: Patients with long-term follow-up after cervical decompression and fusion have often been noted to have development of adjacent segment degeneration with a smaller subset of these patients progressing to adjacent segment disease (ASD), which results in the development of new symptomatic radiculopathy or myelopathy referable to a site either directly above or below a prior fused segment. The cause of ASD is multifactorial often involving natural age-related progression of spondylosis, accelerated progression following cervical decompression and fusion, operative technique, and patient-related factors. The effect of age at the time of index cervical decompression and fusion on the need for reoperation for ASD is not fully understood. This study aims to establish underlying risk factors for the development of symptomatic cervical ASD following cervical decompression and fusion requiring reoperation in patients of various age groups. Methods: A retrospective database review of patients aged 20 or greater with insurance claims of primary cervical decompression and fusion over the course of 11 years and 10 months (January 01, 2010-October 31, 2022) was conducted using an insurance claims database. The primary outcome was to evaluate the incidence of cervical ASD requiring reoperation amongst patients stratified by age at the time of their primary procedure. Secondary outcomes included an evaluation of various risk factors for ASD following cervical decompression and fusion including surgeon-controlled factors such as the number of levels fused and approach taken, patient cervical pathology including cervical disc disorder and cervical spondylosis, and underlying patient medical comorbidities including osteoporosis and vitamin D deficiency, and substance use. Results: A total of 60,292 patient records were analyzed, where the overall reoperation incidence for symptomatic ASD was 6.57%, peaking at 8.12% among those aged 30 to 39 and decreasing with age. Regression analysis revealed ages lower than 50 years as more predictive for the development of symptomatic ASD requiring reoperation. Multivariate regression analysis identified predictive factors for reoperation, including age, Elixhauser Comorbidity Index (ECI), multiple-level surgery, cervical spondylosis, cervical disc disorder, osteoporosis, and vitamin D deficiency. Notably, these factors had a variable impact across various age groups, as revealed by subgroup analysis. Conclusions: The incidence of reoperation secondary to symptomatic ASD is 6.57%, highest in those aged 30 to 39. The surgical approach had no significant impact on the need for reoperation, but multiple-level fusions posed a consistent risk in the development of symptomatic ASD requiring reoperation. Patient factors like degenerative disc disease, spondylosis, osteoporosis, and vitamin D deficiency were associated, urging further age-specific risk assessment and nonoperative intervention exploration.
RESUMO
¼ There is increased integration of machine learning (ML) to aid clinical decision-making in orthopaedic surgery.¼ ML has the ability to predict both clinical outcomes such as range of motion and complications in total shoulder arthroplasty patients.¼ An increased area of focus is the ability for ML to identify implants to aid in revision surgery planning.¼ In this article, we review the current applications of ML in shoulder arthroplasty and discuss future areas where it may enhance orthopaedic practice.
