Literature DB >> 26892283

Magnitude of Hematoma Volume Measurement Error in Intracerebral Hemorrhage.

David Rodriguez-Luna1, Matthew Boyko1, Suresh Subramaniam1, Evgenia Klourfeld1, Patricia Jo1, Brendan J Diederichs1, Jayme C Kosior1, Dar Dowlatshahi1, Richard I Aviv1, Carlos A Molina1, Michael D Hill1, Andrew M Demchuk2.   

Abstract

BACKGROUND AND
PURPOSE: Limiting intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) expansion is a common target for acute ICH studies and, therefore, accurate measurement of hematoma volumes is required. We investigated the amount of hematoma volume difference between computed tomography scans that can be considered as measurement error.
METHODS: Five raters performed baseline (<6 hours) and 24-hour total hematoma (ICH+IVH) computer-assisted volumetric analysis from 40 selected ICH patients from the Predicting Hematoma Growth and Outcome in Intracerebral Hemorrhage Using Contrast Bolus CT (PREDICT) study cohort twice. Estimates of intrarater and interrater reliability are expressed as intraclass correlation coefficients and minimum detectable difference (MDD).
RESULTS: Total hematoma volumetric analyses had excellent intra- and interrater agreements (intraclass correlation coefficients 0.994 and 0.992, respectively). MDD for intra- and interrater volumes was 6.68 and 7.72 mL, respectively, and were higher the larger total hematoma volume was and in patients with subarachnoid hemorrhage or IVH. MDD for total hematoma volume measurement of 10.4 mL was found in patients with largest hematoma volumes. In patients with subarachnoid hemorrhage or IVH, MDD for total hematoma volume was 10.3 and 10.4 mL, respectively. In patients without IVH, MDD for intra- and interrater pure ICH volumes were 3.82 and 5.83 mL, respectively.
CONCLUSIONS: A threshold higher than 10.4 mL seems to be reliable to avoid error of total hematoma volume measurement in a broad range of patients. An absolute ICH volume increase of >6 mL, commonly used as outcome in ICH studies, seems well above MDD and, therefore, could be used to reliably detect ICH expansion.
© 2016 American Heart Association, Inc.

Entities:  

Keywords:  computed tomography; intracerebral hemorrhage; measurement; planimetry; subarachnoid hemorrhage

Mesh:

Year:  2016        PMID: 26892283     DOI: 10.1161/STROKEAHA.115.012170

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  10 in total

1.  17p12 Influences Hematoma Volume and Outcome in Spontaneous Intracerebral Hemorrhage.

Authors:  Sandro Marini; William J Devan; Farid Radmanesh; Laura Miyares; Timothy Poterba; Björn M Hansen; Bo Norrving; Jordi Jimenez-Conde; Eva Giralt-Steinhauer; Roberto Elosua; Elisa Cuadrado-Godia; Carolina Soriano; Jaume Roquer; Christina E Kourkoulis; Alison M Ayres; Kristin Schwab; David L Tirschwell; Magdy Selim; Devin L Brown; Scott L Silliman; Bradford B Worrall; James F Meschia; Chelsea S Kidwell; Joan Montaner; Israel Fernandez-Cadenas; Pilar Delgado; Steven M Greenberg; Arne Lindgren; Charles Matouk; Kevin N Sheth; Daniel Woo; Christopher D Anderson; Jonathan Rosand; Guido J Falcone
Journal:  Stroke       Date:  2018-06-18       Impact factor: 7.914

2.  Intracerebral haemorrhage volume, haematoma expansion and 3-month outcomes in patients on antiplatelets. A systematic review and meta-analysis.

Authors:  Martina B Goeldlin; Bernhard M Siepen; Madlaine Mueller; Bastian Volbers; Werner Z'Graggen; David Bervini; Andreas Raabe; Nikola Sprigg; Urs Fischer; David J Seiffge
Journal:  Eur Stroke J       Date:  2021-11-16

3.  Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans.

Authors:  Eli Gibson; Bogdan Georgescu; Pascal Ceccaldi; Pierre-Hugo Trigan; Youngjin Yoo; Jyotipriya Das; Thomas J Re; Vishwanath Rs; Abishek Balachandran; Eva Eibenberger; Andrei Chekkoury; Barbara Brehm; Uttam K Bodanapally; Savvas Nicolaou; Pina C Sanelli; Thomas J Schroeppel; Thomas Flohr; Dorin Comaniciu; Yvonne W Lui
Journal:  Radiol Artif Intell       Date:  2022-04-20

4.  Software output from semi-automated planimetry can underestimate intracerebral haemorrhage and peri-haematomal oedema volumes by up to 41.

Authors:  Teddy Y Wu; Oluwaseun Sobowale; Robert Hurford; Gagan Sharma; Søren Christensen; Nawaf Yassi; Turgut Tatlisumak; Patricia M Desmond; Bruce C V Campbell; Stephen M Davis; Adrian R Parry-Jones; Atte Meretoja
Journal:  Neuroradiology       Date:  2016-07-05       Impact factor: 2.804

5.  Location of intracerebral haemorrhage predicts haematoma expansion.

Authors:  Vignan Yogendrakumar; Andrew M Demchuk; Richard I Aviv; David Rodriguez-Luna; Carlos A Molina; Yolanda S Blas; Imanuel Dzialowski; Adam Kobayashi; Jean-Martin Boulanger; Cheemun Lum; Gord Gubitz; Vasantha Padma; Jayanta Roy; Carlos S Kase; Rohit Bhatia; Michael D Hill; Dar Dowlatshahi
Journal:  Eur Stroke J       Date:  2017-06-15

6.  Automation of CT-based haemorrhagic stroke assessment for improved clinical outcomes: study protocol and design.

Authors:  Betty Chinda; George Medvedev; William Siu; Martin Ester; Ali Arab; Tao Gu; Sylvain Moreno; Ryan C N D'Arcy; Xiaowei Song
Journal:  BMJ Open       Date:  2018-04-19       Impact factor: 2.692

7.  Different Effects of Hematoma Expansion on Short-Term Functional Outcome in Basal Ganglia and Thalamic Hemorrhages.

Authors:  Lijing Deng; Kai Chen; Liu Yang; Zhaoxu Deng; Haijun Zheng
Journal:  Biomed Res Int       Date:  2021-10-25       Impact factor: 3.411

8.  Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage.

Authors:  Pascal Salazar; Mario Di Napoli; Mostafa Jafari; Alibay Jafarli; Wendy Ziai; Alexander Petersen; Stephan A Mayer; Eric M Bershad; Rahul Damani; Afshin A Divani
Journal:  Neurocrit Care       Date:  2020-04       Impact factor: 3.210

9.  Meta-analysis of haematoma volume, haematoma expansion and mortality in intracerebral haemorrhage associated with oral anticoagulant use.

Authors:  David J Seiffge; Martina B Goeldlin; Turgut Tatlisumak; Philippe Lyrer; Urs Fischer; Stefan T Engelter; David J Werring
Journal:  J Neurol       Date:  2019-09-20       Impact factor: 4.849

10.  Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement.

Authors:  Tao Wang; Na Song; Lingling Liu; Zichao Zhu; Bing Chen; Wenjun Yang; Zhiqiang Chen
Journal:  BMC Med Imaging       Date:  2021-08-13       Impact factor: 1.930

  10 in total

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