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  3. A Causal Classification System for Intracerebral Hemorrhage subtypes (CLAS-ICH).
 

A Causal Classification System for Intracerebral Hemorrhage subtypes (CLAS-ICH).

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BORIS DOI
10.48350/173518
Publisher DOI
10.1002/ana.26519
PubMed ID
36197294
Description
OBJECTIVE

Determining the underlying causes of intracerebral hemorrhage (ICH) is of major importance because risk factors, prognosis and management differ by ICH subtype. We developed a new causal classification system for ICH Subtypes - termed CLAS-ICH - based on recent advances in neuroimaging.

METHODS

CLAS-ICH defines 5 ICH subtypes: Arteriolosclerosis, Cerebral Amyloid Angiopathy (CAA), Mixed small vessel disease (SVD), Other rare forms of SVD (genetic SVD and others), Secondary Causes (macrovascular causes, tumour and other rare causes). Every patient is scored in each category according to the level of diagnostic evidence: (1) well-defined ICH subtype; (2) possible underlying disease; (0) no evidence of the disease. We evaluated CLAS-ICH in a derivation cohort of 113 patients with ICH from Massachusetts General Hospital, Boston (USA) and in a derivation cohort of 203 patients from Inselspital, Bern (Switzerland).

RESULTS

In the derivation cohort, a well-defined ICH subtype could be identified in 74 (65.5%) patients, including 24 (21.2%) with Arteriolosclerosis, 23 (20.4%) with CAA, 18 (15.9%) with mixed SVD, and 9 (8.0%) with a secondary cause. One or more possible causes were identified in 42 (37.2%) patients. Inter-observer agreement was excellent for each category (kappa value ranging from 0.86 to 1.00). Despite substantial differences in imaging modalities, we obtained similar results in the validation cohort.

INTERPRETATION

CLAS-ICH is a simple and reliable classification system for ICH subtyping, that captures overlap between causes and the level of diagnostic evidence. CLAS-ICH may guide clinicians to identify ICH causes, and improve ICH classification in multicenter studies. This article is protected by copyright. All rights reserved.
Date of Publication
2023-01
Publication Type
article
Subject(s)
600 - Technology::610 - Medicine & health
Language(s)
en
Contributor(s)
Raposo, Nicolas
Zanon Zotin, Maria Clara
Seiffge, David Julian
Universitätsklinik für Neurologie
Li, Qi
Göldlin, Martina Béatriceorcid-logo
Universitätsklinik für Neurologie
Charidimou, Andreas
Shoamanesh, Ashkan
Jäger, Hans Rolf
Cordonnier, Charlotte
Klijn, Catharina Jm
Smith, Eric E
Greenberg, Steven M
Werring, David J
Viswanathan, Anand
Additional Credits
Universitätsklinik für Neurologie
Series
Annals of neurology
Publisher
Wiley-Blackwell
ISSN
1531-8249
Access(Rights)
open.access
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