Distinguishing Chronic Inflammatory Demyelinating Polyneuropathy From Mimic Disorders: The Role of Statistical Modeling.

in Journal of the peripheral nervous system : JPNS by Grace Swart, Michael P Skolka, Shahar Shelly, Richard A Lewis, Jeffrey A Allen, Divyanshu Dubey, Zhiyv Niu, Judith Spies, Ruple S Laughlin, Smathorn Thakolwiboon, Ashley R Santilli, Hebatallah Rashed, Igal Mirman, Alexander Swart, Sarah E Berini, Kamal Shouman, Marcus V Pinto, Michelle L Mauermann, John R Mills, P James B Dyck, William S Harmsen, Jay Mandrekar, Christopher J Klein

TLDR

  • A new tool uses patient information and test results to calculate the probability of CIDP.
  • The tool is accurate in most cases, but can be improved by considering more patient information and test results.
  • The study aims to reduce misdiagnosis and IVIG overutilization by developing a reliable diagnostic tool for CIDP.

Abstract

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. 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. 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. 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.

Overview

  • The study aimed to develop a clinical-electrophysiological model to distinguish Chronic Inflammatory Demyelinating Polyradiculoneuropathy (CIDP) from mimicking disorders.
  • The study used 26 clinical and 144 nerve conduction variables to develop the model, based on the European Academy of Neurology/Peripheral Nerve Society (EAN/PNS) 2021 CIDP guidelines.
  • The goal of the study was to develop a probability calculator to assist in the diagnosis of CIDP and reduce misdiagnosis and IVIG overutilization.

Comparative Analysis & Findings

  • Univariate analysis identified 11 of 26 clinical variables with significant odds ratios.
  • A multivariate regression model using four clinical and two electrophysiological variables achieved 93% area-under-curve (95% CI 91-95).
  • The web-based probability calculator achieved 100% sensitivity and 68% specificity at a 92% probability threshold.

Implications and Future Directions

  • The study provides a valuable tool for clinicians to assist in the diagnosis of CIDP and reduce misdiagnosis and IVIG overutilization.
  • Future studies can be designed to validate the calculator in different patient populations and settings.
  • The study highlights the importance of considering clinical, electrophysiological, and laboratory criteria in the diagnosis of CIDP.
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