Evidence map›Paper›PMID 31359310›Full record

ArticleNeurocritical care2020

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

Pascal Salazar, Mario Di Napoli, Mostafa Jafari, Alibay Jafarli, Wendy Ziai, Alexander Petersen, Stephan A Mayer, Eric M Bershad, Rahul Damani, Afshin A Divani

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In one paragraph

Article in Neurocritical care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Matrix Metalloproteinases in Acute Intracerebral Hemorrhage.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2020
    Review
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Pascal SalazarVital Images, Minnetonka, MN, USA.
Mario Di NapoliDepartment of Neurology, San Camillo de' Lellis District General Hospital, Rieti, Italy.
Mostafa JafariDepartment of Neurology, University of Minnesota, MMC 295, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA.
Alibay JafarliDepartment of Neurology, University of Minnesota, MMC 295, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA.
Wendy ZiaiDepartment of Neurology, Neurosurgery, and Anesthesia/Critical Care Medicine, Johns Hopkins, Baltimore, USA.
Alexander PetersenDepartment of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA.
Stephan A MayerDepartment of Neurology, Henry Ford Health System, Detroit, MI, USA.
Eric M BershadDepartment of Neurology, Baylor College of Medicine, Houston, TX, USA.
Rahul DamaniDepartment of Neurology, Baylor College of Medicine, Houston, TX, USA.
Afshin A DivaniDepartment of Neurology, University of Minnesota, MMC 295, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA. adivani@gmail.com.ORCID http://orcid.org/0000-0001-8448-0758

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice.

methodsWe conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012-2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4-6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome.

resultsWe included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76-0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86-0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89-0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76-0.93] for volume-only model to AUC: 0.88 [0.80-0.95] for imaging data models and AUC: 0.92 [0.86-0.98] for imaging plus clinical predictors.

conclusionsMultiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.

Indexed as

Hospital MortalityAgedAged, 80 and overAge FactorsArea Under CurveCerebral HemorrhageClinical Decision-MakingClinical Decision RulesFemaleFunctional StatusGlasgow Coma ScaleHematomaHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMaleComputed tomographyCT densityHematoma shapeHematoma volumeIntracerebral hemorrhageOutcomesRadiologic predictors

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.