Evidence map›Paper›PMID 38594772›Full record

ArticleCritical care (London, England)2024

Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images.

Hsinhan Tsai, Chien-Yu Chi, Liang-Wei Wang, Yu-Jen Su, Ya-Fang Chen, Min-Shan Tsai, Chih-Hung Wang, Cheyu Hsu, Chien-Hua Huang, Weichung Wang

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Article in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 4 pooled it
–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

8 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Guideline
  2. Pooled it
  3. Guideline
  4. Pooled it
  5. Article
  6. Review
  7. Article
  8. 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.

Hsinhan TsaiDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, 106216, Taiwan R.O.C.
Chien-Yu ChiDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C.
Liang-Wei WangDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C.
Yu-Jen SuDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C.
Ya-Fang ChenDepartment of Medical Imaging, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C.
Min-Shan TsaiDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C.
Chih-Hung WangDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C.
Cheyu HsuDepartment of Oncology, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C.
Chien-Hua HuangDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C.. chhuang5940@ntu.edu.tw.
Weichung WangInstitute of Applied Mathematical Sciences, National Taiwan University, Taipei, 106216, Taiwan R.O.C.. wwang@ntu.edu.tw.

Funding

Ministry of Science and Technology, Taiwan MOST 111-2634-F-002-015
6 · The paper itself

Abstract

backgroundThis study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes.

methodsPatients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations.

resultsOverall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR.

conclusionsAutomated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

Indexed as

Out-of-Hospital Cardiac ArrestWhite MatterGray MatterHumansPrognosisRetrospective StudiesTomography, X-Ray ComputedClinical decision makingCT scanGray-white matter ratioHypoxic-ischemic encephalopathyOut-of-hospital cardiac arrestPrognosisReturn of spontaneous circulation

Identifiers

PMID38594772
PMCPMC11005205

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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.