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Swart, Alexander

https://orcid.org/0000-0003-4611-4268

Mayo Clinic

Mayo Clinic

Citations 3

Distinguishing Chronic Inflammatory Demyelinating Polyneuropathy From Mimic Disorders: The Role of Statistical Modeling
Swart, Grace , Lewis, Richard A. , Dubey, Divyanshu , Spies, Judith , Laughlin, Ruple S. , Santilli, Ashley R. , Rashed, Hebat‐Allah G, Mirman, Igal, Swart, Alexander , Berini, Sarah E. , Shouman, Kamal , Mauermann, Michelle L. , Mills, John R. , Dyck, P. James B. , Harmsen, William S. , Mandrekar, Jay , Dubey, Divyanshu , Rashed, Hebatallah R. , Skolka, Michael P , Allen, Jeffrey A , Thakolwiboon, Smathorn , Rashed, Hebatallah, Klein, Christopher J , Dubey, Divyanshu , Mauermann, Michelle L , Shelly, Shahar , Allen, Jeffrey A. , Niu, Zhiyv , Pinto, Marcus Vinícius , Klein, Christopher J.

Journal of the Peripheral Nervous System | January 13, 2025 | Volume: 30 | Issue: 1 | Pages: No page available

10.1111/jns.12682 Get It

ABSTRACTBackground and AimsChronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is difficult to distinguish from mimicking disorders, with misdiagnosis resulting in IVIG overutilization. We evaluate a clinical‐electrophysiological model to facilitate CIDP versus mimic neuropathy prediction.MethodsUsing the European Academy of Neurology/Peripheral Nerve Society (EAN/PNS) 2021 CIDP guidelines we derived 26 clinical and 144 nerve conduction variables. The model was generated and validated utilizing total CIDP (n = 129) and mimics (n = 309); including (1) IgG4‐nodopathies; (2) POEMS (polyneuropathy–organomegaly–endocrinopathy–monoclonal protein‐skin changes); (3) anti‐myelin‐associated‐glycoprotein; (4) paraneoplastic; (5) Waldenström B‐cell lymphoma; (6) diabetic neuropathies; (7) amyloidosis; (8) Charcot–Marie–Tooth; (9) motor neuropathies/neuronopathies; and (10) idiopathic‐inflammatory‐myopathies.ResultsWe analyzed 9282 clinical and 51 408 electrophysiological data points. Univariate analysis identified 11 of 26 clinical variables with significant odds ratios. A multivariate regression model using four clinical and two electrophysiologic variables achieved 93% area‐under‐curve (95% CI 91–95): progression over 8 weeks (OR 40.66, 95% CI 5.31–311.36), absent autonomic involvement (OR 17.82, 95% CI 2.93–108.24), absent muscle atrophy (OR 16.65, 95% CI 3.27–84.73), proximal weakness (OR 3.63, 95% CI 1.58–8.33), ulnar motor conduction velocity slowing < 35.7 m/s (OR 5.21, 95% CI 2.13–12.76), and ulnar motor conduction block (OR 13.37, 95% CI 2.47–72.40). A web‐based probability calculator (https://news.mayocliniclabs.com/cidp‐calculator/) was developed, with 100% sensitivity and 68% specificity at a 92% probability threshold. Specificity improved to 93% when considering “red flags,” electrophysiologic criteria, and laboratory testing.InterpretationA probability calculator using clinical electrophysiological variables assists CIDP differentiation from mimics, with scores below 92% unlikely to have CIDP. The highest specificity is achieved by considering clinical “red flags,” electrophysiologic demyelination, and laboratory testing.
Significant Reduction in Bone Density as Measured by Hounsfield Units in Patients with Ankylosing Spondylitis or Diffuse Idiopathic Skeletal Hyperostosis
Swart, Alexander , Hamouda, Abdelrahman M. , Pennington, Zach , Lakomkin, Nikita , Mikula, Anthony L. , Subramaniam, Thirusivapragasam , Sebastian, Arjun S. , Freedman, Brett A. , Fogelson, Jeremy L. , Elder, Benjamin D. , Hamouda, Abdelrahman , Shafi, Mahnoor , Pennington, Zach , Martini, Michael L. , Nassr, Ahmad

Journal of Clinical Medicine | March 1, 2024 | Volume: 13 | Issue: 5 | Pages: 1430

10.3390/jcm13051430 Get It

Background: Multisegmental pathologic autofusion occurs in patients with ankylosing spondylitis (AS) and diffuse idiopathic skeletal hyperostosis (DISH). It may lead to reduced vertebral bone density due to stress shielding. Methods: This study aimed to determine the effects of autofusion on bone density by measuring Hounsfield units (HU) in the mobile and immobile spinal segments of patients with AS and DISH treated at a tertiary care center. The mean HU was calculated for five distinct regions—cranial adjacent mobile segment, cranial fused segment, mid-construct fused segment, caudal fused segment, and caudal adjacent mobile segment. Means for each region were compared using paired-sample t-tests. Multivariable regression was used to determine independent predictors of mid-fused segment HUs. Results: One hundred patients were included (mean age 76 ± 11 years, 74% male). The mean HU for the mid-construct fused segment (100, 95% CI [86, 113]) was significantly lower than both cranial and caudal fused segments (174 and 108, respectively; both p < 0.001), and cranial and caudal adjacent mobile segments (195 and 115, respectively; both p < 0.001). Multivariable regression showed the mid-construct HUs were predicted by history of smoking (−30 HU, p = 0.009). Conclusions: HUs were significantly reduced in the middle of long-segment autofusion, which was consistent with stress shielding. Such shielding may contribute to the diminution of vertebral bone integrity in AS/DISH patients and potentially increased fracture risk.