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Jeffrey D. Oliver, Noah L. Lessing, Harry M. Mushlin, Joshua R. Olexa, Kenneth M. Crandall, and Charles A. Sansur


The sacroiliac joint (SIJ) is an important cause of low back pain and referred leg pain (RLP). Pain from SIJ dysfunction may occur in isolation or may result from a combination with lumbosacral area–mediated pain. SIJ fusion is one treatment modality for medically refractory symptoms and may also have a role in the treatment of RLP.


The authors present a challenging case of concomitant lumbosacral degenerative disease and SIJ dysfunction in a patient with radiculopathy. They provide clinical characteristics and imaging findings and discuss difficulties in dealing with the intersection of these two distinct diagnoses. In addition, the authors offer a review of the relevant literature, elucidating the role of SIJ dysfunction in causing radicular lower extremity pain, the relationship to concomitant lumbosacral degenerative disease, and outcome data for SIJ fusion as it relates to RLP.


With increasing numbers of patients undergoing spinal instrumentation in the setting of degenerative lumbosacral arthritis, as well as randomized controlled trial data demonstrating the efficacy of SIJ fusion for medically refractory SIJ dysfunction, it is important to recognize the challenges in understanding how both of these patient groups may present with radiculopathy. Failure to do so may result in incorrect patient selection, poor outcomes, and increased morbidity for at-risk patients.

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Andreas Leidinger, Scott L. Zuckerman, Yueqi Feng, Yitian He, Xinrui Chen, Beverly Cheserem, Linda M. Gerber, Noah L. Lessing, Hamisi K. Shabani, Roger Härtl, and Halinder S. Mangat


The burden of spinal trauma in low- and middle-income countries (LMICs) is immense, and its management is made complex in such resource-restricted settings. Algorithmic evidence-based management is cost-prohibitive, especially with respect to spinal implants, while perioperative care is work-intensive, making overall care dependent on multiple constraints. The objective of this study was to identify determinants of decision-making for surgical intervention, improvement in function, and in-hospital mortality among patients experiencing acute spinal trauma in resource-constrained settings.


This study was a retrospective analysis of prospectively collected data in a cohort of patients with spinal trauma admitted to a tertiary referral hospital center in Dar es Salam, Tanzania. Data on demographic, clinical, and treatment characteristics were collected as part of a quality improvement neurotrauma registry. Outcome measures were surgical intervention, American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade improvement, and in-hospital mortality, based on existing treatment protocols. Univariate analyses of demographic and clinical characteristics were performed for each outcome of interest. Using the variables associated with each outcome, a machine learning algorithm-based regression nonparametric decision tree model utilizing a bootstrapping method was created and the accuracy of the three models was estimated.


Two hundred eighty-four consecutively admitted patients with acute spinal trauma were included over a period of 33 months. The median age was 34 (IQR 26–43) years, 83.8% were male, and 50.7% had experienced injury in a motor vehicle accident. The median time to hospital admission after injury was 2 (IQR 1–6) days; surgery was performed after a further median delay of 22 (IQR 13–39) days. Cervical spine injury comprised 38.4% of the injuries. Admission AIS grades were A in 48.9%, B in 16.2%, C in 8.5%, D in 9.5%, and E in 16.6%. Nearly half (45.1%) of the patients underwent surgery, 12% had at least one functional improvement in AIS grade, and 11.6% died in the hospital. Determinants of surgical intervention were age ≤ 30 years, spinal injury level, admission AIS grade, delay in arrival to the referral hospital, undergoing MRI, and type of insurance; admission AIS grade, delay to arrival to the hospital, and injury level for functional improvement; and delay to arrival, injury level, delay to surgery, and admission AIS grade for in-hospital mortality. The best accuracies for the decision tree models were 0.62, 0.34, and 0.93 for surgery, AIS grade improvement, and in-hospital mortality, respectively.


Operative intervention and functional improvement after acute spinal trauma in this tertiary referral hospital in an LMIC environment were low and inconsistent, which suggests that nonclinical factors exist within complex resource-driven decision-making frameworks. These nonclinical factors are highlighted by the authors’ results showing clinical outcomes and in-hospital mortality were determined by natural history, as evidenced by the highest accuracy of the model predicting in-hospital mortality.