Clinical Utility of Striational Antibodies in Paraneoplastic and Myasthenia Gravis Paraneoplastic Panels

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012050
Author(s):  
Shahar Shelly ◽  
John R Mills ◽  
Divyansu Dubey ◽  
Andrew McKeon ◽  
Anastasia Zekeridou ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Myasthenia Gravis ◽  
Clinical Utility ◽  
Roc Analysis ◽  
Thymic Cancer ◽  
Diagnostic Characteristics ◽  
Data Platform ◽  
Neurological Phenotype ◽  
Antibody Positivity

Objective:To critically assess the clinical utility of striational antibodies (StrAbs) within paraneoplastic and myasthenia gravis serological evaluations.Methods:All Mayo Clinic patients tested for StrAbs from January 1st 2012-December 31st 2018 utilizing Mayo’s Unified Data Platform (UDP) were reviewed for neurological diagnosis and cancer.Results:38,502 unique paraneoplastic and 1,899 MG patients were tested. In paraneoplastic evaluations, the StrAbs positivity rate was higher in cancer vs without cancer (5% [321/6775] vs 4% [1154/31727]; p<0.0001; OR 1.35; CI=1.19-1.53) but ROC analysis indicated no diagnostic accuracy in cancer (AUC=0.505). No neurological phenotype was significantly associated with StrAbs in the paraneoplastic group. Positivity was more common in all MG cancers compared to paraneoplastic cancers (p<0.0001). In MG evaluations, the StrAbs positivity rate was higher in those with cancer vs without (46% [217/474] vs 26% [372/1425]; p<0.0001; OR 2.39, CI 1.9-2.96) with ROC analysis indicating poor diagnostic accuracy for thymic cancer (AUC 0.634, recommended cutoff=1:60, sensitivity=56%, specificity=71%), with worse accuracy for extrathymic cancers (AUC 0.543). In paraneoplastic or MG evaluations, the value of antibody positivity did not improve cancer predictions. Paraneoplastic evaluated patients were more likely with positive StrAbs to obtain computed tomography (CT) (p=0.0001) with 3% (12/468) cancer found.Conclusion:Despite a statistically significant association with cancer, an expansive review of performance in clinical service demonstrates that StrAbs are neither specific nor sensitive in predicting malignancy or neurological phenotypes. CT imaging is over utilized with positive StrAbs results. Removal of StrAbs from paraneoplastic or MG evaluations will improve the diagnostic characteristics of the current MG test.Classification of Evidence:This study provides Class II evidence that the presence of StrAbs do not accurately identify patients with malignancy or neurological phenotypes.

scholarly journals POS1456-HPR THE SERUM IRON LEVEL MIGHT BE USEFUL IN DETERMINING THE SEVERITY OF MALNUTRITION IN PATIENTS WITH RHEUMATOID ARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1012.2-1012
Author(s):  
Y. Matsumoto ◽  
Y. Sugioka ◽  
M. Tada ◽  
T. Okano ◽  
K. Mamoto ◽  
...  
Keyword(s):  
Grip Strength ◽  
Serum Iron ◽  
Roc Analysis ◽  
Severe Malnutrition ◽  
Inflammatory Condition ◽  
Research Support ◽  
Chugai Pharmaceutical ◽  
The Relationship ◽  
Nutritional Indicator

Background:The Global Leadership Initiative on Malnutrition (GLIM) criteria, the first international criteria for diagnosis of malnutrition, was released in 2018 [1]. Patients with rheumatoid arthritis (RA) are thought to be prone to malnutrition due to decreased food intake and increased muscle catabolism caused by chronic inflammation or pain. However, there has been no report to assess the nutritional status of RA patients in accordance with the GLIM criteria. In addition, commonly used blood nutrient indicators such as albumin might not be appropriate as nutritional indicators for RA because these values are affected by inflammation.Objectives:This study aims to examine the rates of malnutrition in RA patients according to GLIM criteria, and the relationship between blood nutrient indicators and the severity of malnutrition.Methods:In this study, we conducted a cross-sectional survey of 135 female RA patients in 2020. According to the GLIM criteria, patients were considered to be malnourished if patients had one of the following phenotypic: (1) low body mass index, (2) non-volitional weight loss, (3) reduced muscle mass, and one of the following etiologic: (1) reduced food intake or assimilation, (2) disease burden/inflammatory condition. Reduced muscle mass was evaluated by measuring calf circumference, and inflammatory condition was evaluated by Disease Activity Score (DAS) 28. In accordance with the GLIM criteria, the severity of malnutrition was judged as three levels: no problem, moderate, and severe malnutrition. Albumin, transthyretin, transferrin, retinol binding protein, zinc, iron, ceruloplasmin, and total cholesterol were assessed as blood nutrition indicators. Also grip strength was assessed. We compared each nutritional indicator among the three groups according to the severity of malnutrition using age-adjusted analysis of covariance, and examined the relationship between each nutritional indicator and the severity of malnutrition using receiver operating characteristic (ROC) analysis.Results:In RA patients, 20% were classified as severe malnutrition, and 40% were moderate or more. Serum iron levels were significantly lower in the severe malnutrition group compared to the no problem group (p = 0.001). In ROC analysis, serum iron, zinc, albumin, and grip strength (area under curve; AUC; 0.680, 0.696, 0.636, 0.790, respectively) were significant parameters for classification of moderate and severe malnutrition. Serum iron and grip strength (AUC for respective parameters were 0.741, 0.747) were significant parameters for classification of severe malnutrition.Conclusion:Evaluation based on the GLIM criteria showed that about 40% of RA patients were under moderate or severe malnutrition. It was suggested that serum iron and grip strength might be useful to predict the severity of malnutrition.References:[1]Cederholm T, Jensen GL, Correia MITD, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community. Clinical Nutrition 2019; 38: 1-9.Acknowledgements:We thank to Tomoko Nakatsuka, and the Center for Drug & Food Clinical Evaluation, Osaka City University Hospital, for management and collection of the study data. We also thank to study participants.Disclosure of Interests:Yoshinari Matsumoto Grant/research support from: Yamada Research Grant, Yuko Sugioka: None declared, Masahiro Tada: None declared, Tadasi Okano Speakers bureau: AbbVie, Asahikasei, Astellas Pharma Inc, Ayumi Pharmaceutical, Bristol-Myers Squibb, Chugai Pharmaceutical, Daiich Sankyo, Eisai, Janssen, Lilly, Mitsubishi Tanabe Pharma Corporation, Novartis Pharma, Ono Pharmaceutical, Pfizer, Sanofi, Takeda Pharmaceutical, Teijin Pharma and UCB, Grant/research support from: AbbVie, Eisai, Mitsubishi Tanabe Pharma Corporation and Nipponkayaku, Kenji Mamoto: None declared, Kentaro Inui Speakers bureau: Daiichi Sankyo Co. Ltd., Mitsubishi Tanabe Pharma, Janssen Pharmaceutical K.K., Astellas Pharma Inc., Takeda Pharmaceutical Co. Ltd., Ono Pharmaceutical Co. Ltd., Abbvie GK, Pfizer Inc., Eisai Co., Ltd., Chugai Pharmaceutical Co., Ltd, Grant/research support from: anssen Pharmaceutical K.K., Astellas Pharma Inc., Sanofi K.K., Abbvie GK, Takeda Pharmaceutical Co. Ltd., QOL RD Co. Ltd., Mitsubishi Tanabe Pharma, Ono Pharmaceutical Co. Ltd., Eisai Co., Ltd., Daiki Habu: None declared, Tatsuya Koike Speakers bureau: AbbVie, Astellas Pharma Inc, Bristol-Myers Squibb, Chugai Pharmaceutical, Eisai, Janssen, Lilly, Mitsubishi Tanabe Pharma Corporation, MSD, Ono Pharmaceutical, Pfizer, Roche, Takeda Pharmaceutical, Teijin Pharma, and UCB, Grant/research support from: AbbVie, Astellas Pharma Inc, Bristol-Myers Squibb, Chugai Pharmaceutical, Eisai, Janssen, Lilly, Mitsubishi Tanabe Pharma Corporation, MSD, Ono Pharmaceutical, Pfizer, Roche, Takeda Pharmaceutical, Teijin Pharma, and UCB

Diagnostic accuracy of MRI textural analysis in the classification of breast tumors

2021 ◽  
Vol 77 ◽  
pp. 86-91
Author(s):  
Ann L. Brown ◽  
Joanna Jeong ◽  
Rifat A. Wahab ◽  
Bin Zhang ◽  
Mary C. Mahoney
Keyword(s):  
Diagnostic Accuracy ◽  
Breast Tumors ◽  
Textural Analysis

Reliability of equine visual lameness classification as a function of expertise, lameness severity and rater confidence

2018 ◽  
Vol 184 (2) ◽  
pp. 63-63 ◽  
Author(s):  
Sandra Dorothee Starke ◽  
Maarten Oosterlinck
Keyword(s):  
Diagnostic Accuracy ◽  
Three Dimensional ◽  
Chance Level ◽  
Correct Classification ◽  
Full Understanding ◽  
Case Load ◽  
Movement Asymmetry ◽  
Load Exposure ◽  
Hindlimb Lameness

Visual equine lameness assessment is often unreliable, yet the full understanding of this issue is missing. Here, we investigate visual lameness assessment using near-realistic, three-dimensional horse animations presenting with 0–60 per cent movement asymmetry. Animations were scored at an equine veterinary seminar by attendees with various expertise levels. Results showed that years of experience and exposure to a low, medium or high case load had no significant effect on correct assessment of lame (P>0.149) or sound horses (P≥0.412), with the exception of a significant effect of case load exposure on forelimb lameness assessment at 60 per cent asymmetry (P=0.014). The correct classification of sound horses as sound was significantly (P<0.001) higher for forelimb (average 72 per cent correct) than for hindlimb lameness assessment (average 28 per cent correct): participants often saw hindlimb lameness where there was none. For subtle lameness, errors often resulted from not noticing forelimb lameness and from classifying the incorrect limb as lame for hindlimb lameness. Diagnostic accuracy was at or below chance level for some metrics. Rater confidence was not associated with performance. Visual gait assessment may overall be unlikely to reliably differentiate between sound and mildly lame horses irrespective of an assessor’s background.

The potential value of a pictorial atlas for aid in the visual diagnosis of 123I FP-CIT SPECT scans

2009 ◽  
Vol 48 (04) ◽  
pp. 173-178 ◽  
Author(s):  
H. Ham ◽  
A. Dobbeleir ◽  
P. Santens ◽  
Y. D'Asseler ◽  
I. Goethals
Keyword(s):  
Diagnostic Accuracy ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Interobserver Variability ◽  
Area Under The Curve ◽  
Visual Assessment ◽  
Added Value ◽  
Related Disorder ◽  
Visual Diagnosis ◽  
Spect Images

SummaryThe aim of our study was to evaluate the value of a pictorial atlas of 123I FP-CIT SPECT images for aid in the visual diagnosis. Patients, materials, methods: Sixty patients, of whom 20 were clinically diagnosed as ‘non-parkinsonian’ and 40 as having Parkinson's disease or any related disorder, were included in the study. An atlas consisting of 12 123I FP-CIT SPECT images was constructed first. Validity of the atlas was investigated by performing a receiver operating characteristic (ROC) analysis with the clinical diagnosis as the gold standard. The remaining 48 SPECT images were visually assessed twice by 5 observers, first with and secondly without consulting the atlas, or vice versa. The added value of the atlas was investigated by comparing the diagnostic accuracy and the interobserver variability for both methods. Results: ROC analysis performed on the atlas yielded an area under the curve of 1 for a threshold discriminating between clinically non-parkinsonian and parkinsonian patients that was situated between image 4 and 5 of the atlas. For the diagnostic accuracy, we found that the area under the ROC curve was systematically higher if observers had access to the atlas compared to when they had not (Wilcoxon's test, p<0.05). Also, the interobserver variability was significantly lower when observers used the atlas when compared to when they did not (p = 0.05). Conclusion: Diagnostic accuracy was significantly higher and interobserver variability significantly lower if observers had access to the atlas compared to when they had not. Hence, having a pictorial atlas available may facilitate the visual assessment of 123I FP-CIT SPECT scans.

The clinical utility of the glycemic index and its application to mixed meals

1991 ◽  
Vol 69 (1) ◽  
pp. 100-107 ◽  
Author(s):  
Clarie B. Hollenbeck ◽  
Ann M. Coulston
Keyword(s):  
Glycemic Index ◽  
Clinical Utility ◽  
Postprandial Glucose ◽  
Dependent Diabetes Mellitus ◽  
Glycemic Response ◽  
Glucose Response ◽  
Number Of Factors ◽  
Carbohydrate Intolerance ◽  
Low Glycemic Index

A classification of carbohydrate-containing foods based on their glycemic response to 50-g carbohydrate portions has recently been developed. The relative glycemic potency of many of these carbohydrate-containing foods have been compared, and these data have been published in the form of a glycemic index. It has been suggested that meals containing low glycemic index foods will result in a lower postprandial glucose response than meals with a higher glycemic index. However, whether or not these data will lead to a clinically useful reduction in postprandial hyperglycemia in individuals with carbohydrate intolerance remains controversial. In this review, we will try to delineate why we believe that the glycemic index, as currently developed, may be a specious issue. In addition, we will briefly discuss a number of factors that may explain the apparent discrepancy in viewpoints on this issue.Key words: glycemic index, noninsulin-dependent diabetes mellitus, glycemic response, dietary carbohydrate.

Translation: Beyond Diagnostic Accuracy: The Clinical Utility of Diagnostic Tests

2013 ◽  
Vol 3 (4) ◽  
pp. 269
Author(s):  
Patrick M.M. Bossuyt ◽  
Johannes B. Reitsma ◽  
Kristian Linnet ◽  
Karel G.M. Moons
Keyword(s):  
Diagnostic Accuracy ◽  
Diagnostic Tests ◽  
Clinical Utility

scholarly journals Insights into the Classification of Myasthenia Gravis

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106757 ◽  
Author(s):  
Tetsuya Akaishi ◽  
Takuhiro Yamaguchi ◽  
Yasushi Suzuki ◽  
Yuriko Nagane ◽  
Shigeaki Suzuki ◽  
...  
Keyword(s):  
Myasthenia Gravis

scholarly journals Diagnostic accuracy and clinical utility of a simplified low cost method of counting CD4 cells with flow cytometry in Malawi: diagnostic accuracy study

BMJ ◽  
2007 ◽  
Vol 335 (7612) ◽  
pp. 190-190 ◽  
Author(s):  
Calman A MacLennan ◽  
Michael K P Liu ◽  
Sarah A White ◽  
Joep J G van Oosterhout ◽  
Felanji Simukonda ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Diagnostic Accuracy ◽  
Clinical Utility ◽  
Low Cost ◽  
Diagnostic Accuracy Study ◽  
Cd4 Cells ◽  
Accuracy Study

scholarly journals T1 and ECV mapping texture analysis distinguishing hypertrophic cardiomyopathy from athletes heart better than median values

2021 ◽  
Vol 22 (Supplement_2) ◽  
Author(s):  
T Dresselaers ◽  
P Rafouli-Stergiou ◽  
R De Bosscher ◽  
S Tilborghs ◽  
C Dausin ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Texture Analysis ◽  
Roc Analysis ◽  
Left Ventricular ◽  
Athlete’S Heart ◽  
Support Vector ◽  
Mapping Data ◽  
Intensive Training ◽  
Native T1 ◽  
Athlete's Heart

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ph.D fellowship of the Research Foundation Flanders (FWO). The Master@Heart trial is funded by the FWO. Introduction Differentiating intensive training induced hypertrophy from hyperthropic cardiomyopathy (HCM) is important to identify those young athletes at risk of sudden cardiac death. Swoboda and colleagues demonstrated that T1 and ECV mapping can aid such a differentiation between athletic and pathological hypertrophy, particularly in subjects with indeterminate wall thickness (1). Recently texture analysis (TA) methods of CMR data have demonstrated improved diagnostic accuracy over conventional qualitative analysis in various heart diseases. Only few studies have applied TA to T1 and ECV mapping data (2-4). Here we aimed to demonstrate that a TA approach provides superior capacity to distinguish HCM from athlete’s heart over average native T1 and ECV values. Purpose It was our hypothesis that a texture analysis of T1 and ECV mapping images would identify features that could discriminate between a HCM and athlete’s heart with a higher classification accuracy (CA) than average T1 and ECV values. Methods This study included data from 97 subjects diagnosed with HCM (acc. to guidelines; 5) and 28 athletes that took part in the Master@Heart trial (an ongoing study assessing the beneficial effects of long-term endurance exercise for the prevention of coronary artery disease, 6).  Long and short axis T1 mapping data was acquired on a 1.5T Philips Ingenia system using MOLLI (seconds scheme). After offline motion correction and T1 and ECV map calculation (7), the left ventricular myocardium was manually delineated (3D Slicer; 8). Texture analysis of the masked images resulted in 194 features (Pyradiomics, standard settings; 9). The dataset was then split (75/25%) for training and testing purposes keeping images from the same subject within the same set. A fast correlation based filter rank was applied to the training data to derive relevant features. A further reduction to only two features was based on the CA of a support vector machine (SVM) learning method (linear kernel; cost 0.9 regression loss epsilon 0.1; leave-one-out). Finally, ROC analysis on the test data was used to determine the diagnostic accuracy for the following predictors: (1) median T1 and ECV (2) two most relevant features (training) (3) combination of (1) and (2) (ROC AUC statistics (10)). Results The two most relevant features were the histogram feature ECV energy and the gray level size zone matrix (GLSZM) feature native T1 zone entropy, a measure of heterogeneity in the texture pattern. A model to distinguish HCM from athletes based on these features outperformed the model using only median T1 and ECV values with both higher sensitivity and specificity (table 1) and a significantly  higher AUC in the ROC analysis (p &lt; 0.05, figure 1). Combining these two features with median values did not improve the CA further.  Conclusion Texture analysis of motion-corrected T1 and ECV mapping images out-performs classical analysis based on average values in distinguishing HCM from athlete"s heart.

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