Logo image
Evaluation and construction of diagnostic criteria for inclusion body myositis
Journal article   Open access   Peer reviewed

Evaluation and construction of diagnostic criteria for inclusion body myositis

T.E. Lloyd, A.L. Mammen, A.A. Amato, M.D. Weiss, M. Needham and S.A. Greenberg
Neurology, Vol.83(5), pp.426-433
2014
pdf
NEUROLOGY2013559674.pdfDownloadView
Open Access
url
Link to Published Version *Subscription may be requiredView

Abstract

OBJECTIVE: To use patient data to evaluate and construct diagnostic criteria for inclusion body myositis (IBM), a progressive disease of skeletal muscle. METHODS: The literature was reviewed to identify all previously proposed IBM diagnostic criteria. These criteria were applied through medical records review to 200 patients diagnosed as having IBM and 171 patients diagnosed as having a muscle disease other than IBM by neuromuscular specialists at 2 institutions, and to a validating set of 66 additional patients with IBM from 2 other institutions. Machine learning techniques were used for unbiased construction of diagnostic criteria. RESULTS: Twenty-four previously proposed IBM diagnostic categories were identified. Twelve categories all performed with high (≥97%) specificity but varied substantially in their sensitivities (11%-84%). The best performing category was European Neuromuscular Centre 2013 probable (sensitivity of 84%). Specialized pathologic features and newly introduced strength criteria (comparative knee extension/hip flexion strength) performed poorly. Unbiased data-directed analysis of 20 features in 371 patients resulted in construction of higher-performing data-derived diagnostic criteria (90% sensitivity and 96% specificity). CONCLUSIONS: Published expert consensus-derived IBM diagnostic categories have uniformly high specificity but wide-ranging sensitivities. High-performing IBM diagnostic category criteria can be developed directly from principled unbiased analysis of patient data. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that published expert consensus-derived IBM diagnostic categories accurately distinguish IBM from other muscle disease with high specificity but wide-ranging sensitivities.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: InCites

Metrics

203 File views/ downloads
168 Record Views

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Citation topics
1 Clinical & Life Sciences
1.106 Rheumatology
1.106.1684 Dermatomyositis
Web Of Science research areas
Clinical Neurology
ESI research areas
Neuroscience & Behavior
Logo image