A Statistical Model to Facilitate Differentiation of Chronic Inflammatory Demyelinating Polyneuropathy Versus Mimics
Grace Swart1, Michael Skolka2, Shahar Shelly3, Richard Lewis4, Jeffrey Allen5, Judith Spies6, Divyanshu Dubey2, Ruple Laughlin7, Smathorn Thakolwiboon8, Ashley Santilli2, Hebatallah Rashed2, Igal Mirman9, Alexander Swart2, Zhiyv Niu2, Sarah Berini2, Kamal Shouman2, Marcus Vinicius Pinto2, Michelle Mauermann2, P. James B. Dyck2, John Mills2, William Harmsen2, Jayawant Mandrekar2, Christopher Klein2
1University of Sydney, 2Mayo Clinic, 3Rambam Medical Center, 4Cedars-Sinai Medical Center, 5UMN, 6Royal Prince Alfred Hospital, 7Mayo Clinic Rochester, 8Mayo Clinic Health System, 9The University of Tennessee Medical Center
Objective:

We sought to create a simple clinical-electrophysiological model facilitating CIDP versus mimic neuropathy prediction, accessible as a web-based probability calculator.

Background:

CIDP misdiagnosis leads to inappropriate treatment with economic and patient burden. Consensus guidelines assist diagnosis but are underutilized based on complexity.

Design/Methods:

2021-EAN/PNS guidelines were used for CIDP diagnosis with 26 clinical and 144 nerve-conduction variables chosen based on these criteria and publications addressing mimics. 110 CIDP and 309 mimics (IgG4-nodopathies, paraneoplastic, POEMS, anti-MAG, diabetic radiculoplexus neuropathies (DRPN), MMN, inherited, eight others) underwent data extraction. Univariate and multivariate regression analysis identified the most informative variables, validated in a CIDP subset cohort.

Results:

We analysed 9,282 clinical and 51,408 electrophysiological data points. Univariate analysis identified 11/26 clinical variables with significant odds-ratios. A multivariate regression model revealed six clinical-electrophysiologic variables—progression over 8 weeks (OR 40.66, p=0.0004), absent autonomic involvement (OR 17.82, p=0.0018), absent muscle atrophy (OR 16.65, p=0.0007), proximal weakness (OR 3.63, p=0.0024), ulnar motor conduction velocity slowing <35.7 m/s (OR 5.21, p=0.0003), ulnar motor conduction block (OR 13.37, p=0.0026) —achieved 93%-area-under-curve (95%CI 91-95). EAN/PNS electrodiagnostic criteria did not enhance the model (92% area-under-curve, 95% CI 88.6-94.8). The calculator at 92% probability cut-off showed 100% sensitivity but 67% specificity. Specificity improved to 93% after considering clinical “red-flags”, 2021-EAN/PNS electrophysiologic demyelination, and laboratory testing where indicated for NF155/Contactin1-IgG4, paraneoplastic antibodies, VEGF-gammopathy, MAG-gammopathy and inherited neuropathy gene panel.

Conclusions:
A CIDP calculator with limited clinical-nerve-conduction variables facilitates differentiation of CIDP versus mimics, with highest utility in ruling out CIDP mimics at scores under 92%. Specificity is enhanced by revisiting the clinical phenotype and considering individualized serum laboratory testing. Further validation studies of the model in external cohorts are warranted.
10.1212/WNL.0000000000212345
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