TY - JOUR
T1 - Distinguishing Chronic Inflammatory Demyelinating Polyneuropathy From Mimic Disorders
T2 - The Role of Statistical Modeling
AU - Swart, Grace
AU - Skolka, Michael P.
AU - Shelly, Shahar
AU - Lewis, Richard A.
AU - Allen, Jeffrey A.
AU - Dubey, Divyanshu
AU - Niu, Zhiyv
AU - Spies, Judith
AU - Laughlin, Ruple S.
AU - Thakolwiboon, Smathorn
AU - Santilli, Ashley R.
AU - Rashed, Hebatallah
AU - Mirman, Igal
AU - Swart, Alexander
AU - Berini, Sarah E.
AU - Shouman, Kamal
AU - Pinto, Marcus V.
AU - Mauermann, Michelle L.
AU - Mills, John R.
AU - Dyck, P. James B.
AU - Harmsen, William S.
AU - Mandrekar, Jay
AU - Klein, Christopher J.
N1 - Publisher Copyright:
© 2025 Peripheral Nerve Society.
PY - 2025/3
Y1 - 2025/3
N2 - Background and Aims: Chronic 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. Methods: Using 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. Results: We 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. Interpretation: A 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.
AB - Background and Aims: Chronic 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. Methods: Using 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. Results: We 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. Interpretation: A 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.
KW - chronic inflammatory demyelinating polyradiculoneuropathy
KW - CIDP
KW - diagnostic criteria
KW - EAN/PNS
KW - European academy of neurology/peripheral nerve society
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UR - http://www.scopus.com/inward/citedby.url?scp=85215269313&partnerID=8YFLogxK
U2 - 10.1111/jns.12682
DO - 10.1111/jns.12682
M3 - Article
C2 - 39801067
AN - SCOPUS:85215269313
SN - 1085-9489
VL - 30
JO - Journal of the Peripheral Nervous System
JF - Journal of the Peripheral Nervous System
IS - 1
M1 - e12682
ER -
