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A Causal Classification System for Intracerebral Hemorrhage Subtypes.

Nicolas Raposo1,2, Maria Clara Zanon Zotin3,4, David J Seiffge5

  • 1Department of Neurology, Toulouse University Hospital, Toulouse, France.

Annals of Neurology
|October 5, 2022
PubMed
Summary

A new classification system, CLAS-ICH, aids in identifying intracerebral hemorrhage (ICH) subtypes and their causes using neuroimaging. This reliable system improves ICH subtyping for better clinical guidance and multicenter studies.

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Area of Science:

  • Neurology
  • Radiology
  • Medical Diagnostics

Background:

  • Accurate intracerebral hemorrhage (ICH) subtyping is crucial as risk factors, prognosis, and management vary significantly by cause.
  • Current classification methods may not fully capture the complexity and overlap of ICH etiologies.
  • Advances in neuroimaging necessitate updated classification systems for improved diagnostic accuracy.

Purpose of the Study:

  • To introduce and validate a novel classification system, CLAS-ICH, for categorizing intracerebral hemorrhage (ICH) subtypes based on neuroimaging.
  • To define distinct ICH subtypes including arteriolosclerosis, cerebral amyloid angiopathy, small vessel disease (SVD) variants, and secondary causes.
  • To develop a scoring mechanism reflecting the level of diagnostic evidence for each potential ICH cause.

Main Methods:

  • Development of the CLAS-ICH classification system incorporating 5 ICH subtypes and a scoring system for diagnostic evidence.
  • Evaluation of CLAS-ICH in a derivation cohort (n=113) from Massachusetts General Hospital and a validation cohort (n=203) from Inselspital, Bern.
  • Assessment of interobserver agreement for the classification categories.

Main Results:

  • A well-defined ICH subtype was identified in 65.5% of the derivation cohort.
  • Specific subtypes identified included arteriolosclerosis (21.2%), cerebral amyloid angiopathy (20.4%), mixed SVD (15.9%), and secondary causes (8.0%).
  • Excellent interobserver agreement (kappa 0.86–1.00) was achieved, and similar results were observed in the validation cohort despite differing imaging modalities.

Conclusions:

  • CLAS-ICH offers a simple, reliable method for ICH subtyping, effectively capturing overlapping causes and diagnostic certainty.
  • The system has the potential to guide clinicians in identifying ICH causes more accurately.
  • CLAS-ICH may enhance the consistency and utility of ICH classification in multicenter research settings.