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<p>Acute stroke management is time-sensitive, making time data crucial for both research and quality management. However, these time data are often not reliably captured in routine clinical practice. In this proof-of-concept study we analysed image-based time data automatically captured in the DICOM format. We enrolled data from two separate stroke centers (<i>n</i> = 3136 and <i>n</i> = 2089). Data from the first center was additionally separated into groups with large-vessel-occlusion (LVO, <i>n</i> = 1.092), medium-vessel-occlusions (MVO, <i>n</i> = 416), and no occlusion (NVO, <i>n</i> = 1630). The DICOM-tag <i>StudyTime</i> was used to analyze the distribution of scan times throughout the day. Additionally, manually documented onset- and admission were extracted from the patients’ records in a subset of cases (<i>n</i> = 347). Timestamps were compared across centers and occlusion groups, and a probabilistic model was developed to illustrate and compare stroke occurrence patterns throughout the day. The temporal distribution of the scan times at both centers was exceptionally consistent with a peak around noon and a nighttime low. The groups with vessel occlusions showed an earlier peak compared to those without (<i>p</i> &lt; 0.04). The median interval between admission and scan time was 23&#xa0;min, while the median onset-to-imaging time was 1&#xa0;h:54&#xa0;min. This proof-of-concept study indicates that DICOM-timestamps can reveal insights into the temporal patterns of stroke imaging and may be a promising tool for quality control and stroke research in general since they are always automatically captured by imaging devices as opposed to manual data collection in routine clinical practice.</p>

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Analyzing temporal imaging patterns in acute ischemic stroke via DICOM-timestamps

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月7日

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  • Alexander Rau,
  • Marco Reisert,
  • Benedikt Frank,
  • Cornelius Deuschl,
  • Maximilian F Russe,
  • Samer Elsheikh,
  • Martin Köhrmann,
  • Horst Urbach,
  • Elias Kellner

摘要

Acute stroke management is time-sensitive, making time data crucial for both research and quality management. However, these time data are often not reliably captured in routine clinical practice. In this proof-of-concept study we analysed image-based time data automatically captured in the DICOM format. We enrolled data from two separate stroke centers (n = 3136 and n = 2089). Data from the first center was additionally separated into groups with large-vessel-occlusion (LVO, n = 1.092), medium-vessel-occlusions (MVO, n = 416), and no occlusion (NVO, n = 1630). The DICOM-tag StudyTime was used to analyze the distribution of scan times throughout the day. Additionally, manually documented onset- and admission were extracted from the patients’ records in a subset of cases (n = 347). Timestamps were compared across centers and occlusion groups, and a probabilistic model was developed to illustrate and compare stroke occurrence patterns throughout the day. The temporal distribution of the scan times at both centers was exceptionally consistent with a peak around noon and a nighttime low. The groups with vessel occlusions showed an earlier peak compared to those without (p < 0.04). The median interval between admission and scan time was 23 min, while the median onset-to-imaging time was 1 h:54 min. This proof-of-concept study indicates that DICOM-timestamps can reveal insights into the temporal patterns of stroke imaging and may be a promising tool for quality control and stroke research in general since they are always automatically captured by imaging devices as opposed to manual data collection in routine clinical practice.