Objective Recent studies suggest that sensory phenotyping may provide critical information for the diagnosis and management of patients with chronic neuropathic pain (NP). However, many formal quantitative sensory testing (QST) paradigms require expensive equipment, a dedicated location, and substantial time commitments on the part of patient and examiner, highlighting the need for a convenient and portable “bedside” QST battery. We developed and tested a bedside QST battery in a sample of patients with chronic NP. Methods Participants (N = 51) attended two in-person visits over approximately two weeks, during which they underwent QST using both laboratory-based equipment and simple, easily accessible bedside tools. Participants also completed questionnaires about their daily pain and NP symptoms. Results Test–retest reliability estimates were highly statistically significant and did not differ between bedside (mean r = 0.60) and laboratory-based (mean r = 0.72) QST. Bedside QST variables and corresponding laboratory-based QST variables were highly correlated, suggesting adequate criterion validity for the bedside tests. Conclusions Results from the present study may have important implications for the sensory phenotyping and subsequent management of patients with chronic NP. Implementation of a protocol that uses inexpensive, portable, and convenient tools may allow for the application of QST in variety of clinical settings and advance NP research.

Background:Multiple system atrophy (MSA) is a progressive neurodegenerative disorder caused by the abnormal accumulation of alpha-synuclein in the nervous system. Clinical features include autonomic and motor dysfunction, which overlap with those of Parkinson disease (PD), particularly at early disease stages. There is an unmet need for accurate diagnostic and prognostic biomarkers for MSA, and, specifically, a critical need to distinguish MSA from the other synucleinopathies, particularly PD. The purpose of the study was to develop a unique cutaneous pathological signature of phosphorylated alpha-synuclein that could distinguish patients with MSA from patients with PD and healthy controls.Methods:We studied 31 patients with MSA and 54 patients with PD diagnosed according to current clinical consensus criteria. We also included 24 matched controls. All participants underwent neurologic examinations, autonomic testing and skin biopsies at three locations. The density of intra-epidermal, sudomotor and pilomotor nerve fibers was measured. The deposition of phosphorylated alpha-synuclein was quantified. Results were compared to clinical rating assessments and autonomic function test results.Results:Patients with PD had reduced nerve fiber densities compared to patients with MSA (P<0.05, all fiber types). All patients with MSA and 51/54 with PD had evidence of phosphorylated alpha-synuclein in at least one skin biopsy. No phosphorylated alpha-synuclein was detected in controls. Patients with MSA had greater phosphorylated alpha-synuclein deposition (P<0.0001) and more widespread peripheral distribution (P<0.0001) than patients with PD. These results provided >90% sensitivity and specificity in distinguishing between the two disorders.Discussion:Alpha-synuclein is present in peripheral autonomic nerves of MSA patients, and when combined with synuclein distribution, accurately distinguishes MSA from PD.Classification of Evidence:This study provides Class II evidence that measurement of phosphorylated alpha-synuclein in skin biopsies can differentiate patients with MSA from those with PD.

Introduction/Aims Small fiber neuropathies (SFN) have been associated with two autoantibodies, trisulfated heparin disaccharide (TS‐HDS) and fibroblast growth factor receptor 3 (FGFR‐3), and intravenous immune globulin (IVIG) has been suggested as a potential therapy. The study objective is to determine the efficacy of IVIG on nerve density, pain and neurologic examinations in patients with SFN associated with TS‐HDS and FGFR‐3 autoantibodies. Methods This was a double‐blind placebo‐controlled pilot study. Subjects with SFN confirmed by history, examination, and skin biopsy with elevated autoantibodies to TS‐HDS and/or FGFR‐3 received IVIG (or blinded placebo) 2 grams/kg followed by 1 gram/kg every 3 wk for a total of 6 treatments. All subjects had Utah Early Neuropathy Scores (UENS), questionnaires and skin biopsies with quantitation of intra‐epidermal nerve fiber density (IENFD) taken from adjacent sites at the distal leg at baseline and 6 mo later. The primary outcome was the change in IENFD over 6 mo. Results Twenty subjects were enrolled; 17 completed treatment (8 IVIG, 9 placebo). Three did not have final data due to coronavirus disease 2019 (COVID‐19). Skin biopsy IENFD improved by 0.5 ± 0.8 fibers/mm in the placebo group and improved by 0.6 ± 0.6 fibers/mm in the IVIG‐treated group (p = NS).Over 24 wk the change in pain scores (11 point pain scale) was −1.9 ± 2.6 in the placebo group, and − 1.7 ± 0.9 in the IVIG group (p = NS), the UENS improved by 3.0 ± 5.8 in the placebo group and improved by 1.8 ± 3.9 in the IVIG group (p = NS). Discussion This pilot study did not detect a benefit of treatment with IVIG in patients with SFN and autoantibodies to TS‐HDS and FGFR‐3.

Although many papers report significant differences in ESC values between disease and control subjects, the compiled data assessed in this review raises questions about the technique. Many of the published results violate biologic plausibility. A single funding source with a vested interest in the study outcomes has supported most of the studies. Normative values are inconsistent across publications, and large combined data sets do not support a high sensitivity and specificity. Finally, there is insufficient evidence supporting the claim that Sudoscan tests sudomotor or sensory nerve fiber function.

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Objective To determine the sensitivity and specificity of cutaneous amyloid deposition in relation to patient‐reported measures in the earliest disease stage of hereditary ATTR amyloidosis (ATTRv). Methods In a cross‐sectional study, we analyzed 88 individuals with TTR mutations, 47 of whom were in the earliest disease stage and without clinically evident neuropathy, 12 healthy controls, and 13 disease controls with diabetes. All participants' neuropathy symptoms and signs were assessed using validated patient and clinician‐reported measures and 3‐mm skin punch biopsies were immunostained using protein gene product 9.5 and Congo Red. Results Amyloid can be detected in the earliest disease stages in up to 86% of patients with ATTRv amyloidosis. Amyloid was not detected in healthy individuals or individuals with diabetic peripheral neuropathy supporting a sensitivity of 86% and a specificity of 100%. The cutaneous deposition of amyloid correlates with neuropathy sensory symptoms, measured with the Neuropathy Total Symptom Score‐6 ( R = 0.46, p < 0.01); pain measured with the Brief Pain Symptom Inventory ( R = 0.44, p < 0.05); autonomic symptoms, measured with the Boston Autonomic Symptom Questionnaire ( R = 0.38, p < 0.05); and quality of life measured with the Norfolk Diabetic Neuropathy Quality of Life Questionnaire ( R = 0.44, p < 0.05). Individuals with amyloid deposition were more likely to have sensory symptoms, pain, autonomic impairment, and reduced quality of life than ATTRv patients without amyloid deposition. Interpretation These findings have implications for understanding the earliest manifestations of the clinical phenotype of ATTRv‐associated neuropathy, for the pathophysiological construct of disease staging, and for timing the introduction of disease‐modifying therapy.

OBJECTIVES: Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).DESIGN: Retrospective cohort study. SETTING: Single-center tertiary academic medical center. PATIENTS:Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:We developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity. CONCLUSIONS:The E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.

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