Citations 2
A Statistical Model to Facilitate Differentiation of Chronic Inflammatory Demyelinating Polyneuropathy Versus Mimics (S16.010)
Swart, Grace , Lewis, Richard A. , Spies, Judith , Dubey, Divyanshu , Laughlin, Ruple S. , Santilli, Ashley , Rashed, Hebat‐Allah G, Mirman, Igal, Swart, Alexander , Berini, Sarah E. , Shouman, Kamal , Dyck, P. James B. , Mills, John, Harmsen, William S. , Mandrekar, Jayawant, Skolka, Michael P , Shelly, Shahar , Allen, Jeffrey A. , Dubey, Divyanshu , Thakolwiboon, Smathorn , Rashed, Hebatallah, Niu, Zhiyv , Pinto, Marcus Vinícius , Mauermann, Michelle L. , Klein, Christopher J.
Neurology | April 7, 2025 | Volume: 104 | Issue: 7_Supplement_1 | Pages: No page available
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
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.