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  • Predictive Factors and Subgroup Analysis of Patient Satisfaction Rates With Long-term Outcomes Following Sacroiliac Joint Fusion

    Final Number:

    Alex Y Chen PhD; Sonia Veronica Eden MD; Bruce Dall MD

    Study Design:

    Subject Category:

    Meeting: Congress of Neurological Surgeons 2016 Annual Meeting

    Introduction: The demographics and preoperative care profile of patients who diagnosed with SIJ pain generator are complex and heterogeneous. To characterize this complexity and to identify any indicative factors for future surgical satisfaction, we performed multivariate analyses and a subgroup comparison study.

    Methods: Patients with lower back pain were evaluated and diagnosed using a published algorithm for SIJ pain generator. Candidates for SIJ surgery were consequently admitted over five-year. A standardized survey was given to all patients pre- and post-operation. Data is analyzed using unpaired t-test, Fisher's exact test, logistic regression analysis and discriminant analysis.

    Results: With less narcotics used postoperatively (P = 0.030), significant pain relief and dyspareunia (P = 0.024) were found in patients with SIJ fusion satisfaction. The predictive factors of SIJ fusion satisfaction, determined by correlation analysis, are no dyspareunia after surgery, no postoperative narcotics use, but not postoperative complications (P =0.286) and not the different kinds of post-operative pain (with sitting pain (P = 0.5527), with walking pain (P = 0.2455), with leg pain (P = 0.0648), and with back pain (P = 0.7729)). Interestingly, postoperative complications of SIJ fusion are most correlated with postoperative leg pain (P = 0.047) and not with other kinds of pain (back, walking, and sitting pain).

    Conclusions: Our findings suggest that despite the complexity of patient presentations, several features of SIJ fusion procedure can be predictive of the future satisfactory outcomes.

    Patient Care: Identify opportunities to improve patient satisfaction

    Learning Objectives: - Describe the complexity of patient presentation. - Describe the algorithm for diagnosing SIJ pain generator - Explain how factors can be predictive of future satisfactory outcomes.

    References: 1. Bruce E. Dall SVE, Hunter G. Brumblay: Sacroiliac Joint Dysfunction: An Algorithm for Diagnosis and Treatment, in White Paper: Borgess Brain & Spine Institute, 2010, Vol 2. Bruce E. Dall SVE, Michael D. Rahl: Surgery for the Painful, Dysfunctional Sacroiliac Joint, in, ed 1: Springer International Publishing, 2015, pp 15-35 3. Cohen SP: Sacroiliac joint pain: a comprehensive review of anatomy, diagnosis, and treatment. Anesth Analg 101:1440-1453, 2005 4. Laslett M: Evidence-based diagnosis and treatment of the painful sacroiliac joint. J Man Manip Ther 16:142-152, 2008 5. Sembrano JN, Polly DW, Jr.: How often is low back pain not coming from the back? Spine (Phila Pa 1976) 34:E27-32, 2009

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