Electrophysiological features of acute inflammatory demyelinating polyneuropathy associated with SARS-CoV-2 infection.

OBJECTIVE: To assess whether patients with acute inflammatory demyelinating polyneuropathy (AIDP) associated with SARS-CoV-2 show characteristic electrophysiological features. METHODS: Clinical and electrophysiological findings of 24 patients with SARS-CoV-2 infection and AIDP (S-AIDP) and of 48 control AIDP (C-AIDP) without SARS-CoV-2 infection were compared. RESULTS: S-AIDP patients more frequently developed respiratory failure (83.3% vs. 25%, P=0.000) and required intensive care unit (ICU) hospitalization (58.3% vs. 31.3%, P=0.000). In C-AIDP, distal motor latencies (DMLs) were more frequently prolonged (70.9% vs. 26.2%, P=0.000) whereas in S-AIDP distal compound muscle action potential (dCMAP) durations were more frequently increased (49.5% vs. 32.4%, P=0.002) and F waves were more often absent (45.6% vs. 31.8%, P=0.011). Presence of nerves with increased dCMAP duration and normal or slightly prolonged DML was elevenfold higher in S-AIDP (31.1% vs. 2.8%, P=0.000);11 S-AIDP patients showed this pattern in 2 nerves. CONCLUSION: Increased dCMAP duration, thought to be a marker of acquired demyelination, can also be oserved in critical illness myopathy. In S-AIDP patients, an increased dCMAP duration dissociated from prolonged DML, suggests additional muscle fiber conduction slowing, possibly due to a COVID-19-related hyperinflammatory state. Absent F waves, at least in some S-AIDP patients, may reflect α-motor neuron hypoexcitability because of immobilization during the ICU stay. These features should be considered in the electrodiagnosis of SARS-CoV-2 patients with weakness, to avoid misdiagnosis.

Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners.

OBJECTIVE: The interpretation of electrophysiological findings may lead to misdiagnosis in polyneuropathies. We investigated the electrodiagnostic accuracy of three supervised learning algorithms (SLAs): shrinkage discriminant analysis, multinomial logistic regression, and support vector machine (SVM), and three expert and three trainee neurophysiologists. METHODS: We enrolled 434 subjects with the following diagnoses: chronic inflammatory demyelinating polyneuropathy (99), Charcot-Marie-Tooth disease type 1A (124), hereditary neuropathy with liability to pressure palsy (46), diabetic polyneuropathy (67), and controls (98). In each diagnostic class, 90% of subjects were used as training set for SLAs to establish the best performing SLA by tenfold cross validation procedure and 10% of subjects were employed as test set. Performance indicators were accuracy, precision, sensitivity, and specificity. RESULTS: SVM showed the highest overall diagnostic accuracy both in training and test sets (90.5 and 93.2%) and ranked first in a multidimensional comparison analysis. Overall accuracy of neurophysiologists ranged from 54.5 to 81.8%. CONCLUSIONS: This proof of principle study shows that SVM provides a high electrodiagnostic accuracy in polyneuropathies. We suggest that the use of SLAs in electrodiagnosis should be exploited to possibly provide a diagnostic support system especially helpful for the less experienced practitioners.

Predictive functional ANOVA models for longitudinal analysis of mandibular shape changes.

In this paper, we introduce a Bayesian statistical model for the analysis of functional data observed at several time points. Examples of such data include the Michigan growth study where we wish to characterize the shape changes of human mandible profiles. The form of the mandible is often used by clinicians as an aid in predicting the mandibular growth. However, whereas many studies have demonstrated the changes in size that may occur during the period of pubertal growth spurt, shape changes have been less well investigated. Considering a group of subjects presenting normal occlusion, in this paper we thus describe a Bayesian functional ANOVA model that provides information about where and when the shape changes of the mandible occur during different stages of development. The model is developed by defining the notion of predictive process models for Gaussian process (GP) distributions used as priors over the random functional effects. We show that the predictive approach is computationally appealing and that it is useful to analyze multivariate functional data with unequally spaced observations that differ among subjects and times. Graphical posterior summaries show that our model is able to provide a biological interpretation of the morphometric findings and that they comprehensively describe the shape changes of the human mandible profiles. Compared with classical cephalometric analysis, this paper represents a significant methodological advance for the study of mandibular shape changes in two dimensions.

Optimizing the electrodiagnostic accuracy in Guillain-Barré syndrome subtypes: Criteria sets and sparse linear discriminant analysis.

OBJECTIVE: To optimize the electrodiagnosis of Guillain-Barré syndrome (GBS) subtypes at first study. METHODS: The reference electrodiagnosis was obtained in 53 demyelinating and 45 axonal GBS patients on the basis of two serial studies and results of anti-ganglioside antibodies assay. We retrospectively employed sparse linear discriminant analysis (LDA), two existing electrodiagnostic criteria sets (Hadden et al., 1998; Rajabally et al., 2015) and one we propose that additionally evaluates duration of motor responses, sural sparing pattern and defines reversible conduction failure (RCF) in motor and sensory nerves at second study. RESULTS: At first study the misclassification error rates, compared to reference diagnoses, were: 15.3% for sparse LDA, 30% for our criteria, 45% for Rajabally's and 48% for Hadden's. Sparse LDA identified seven most powerful electrophysiological variables differentiating demyelinating and axonal subtypes and assigned to each patient the diagnostic probability of belonging to either subtype. At second study 46.6% of axonal GBS patients showed RCF in two motor and 8.8% in two sensory nerves. CONCLUSIONS: Based on a single study, sparse LDA showed the highest diagnostic accuracy. RCF is present in a considerable percentage of axonal patients. SIGNIFICANCE: Sparse LDA, a supervised statistical method of classification, should be introduced in the electrodiagnostic practice.