Long-term impact of perfusion CT data after subarachnoid hemorrhage

Christian Mathys1, Daniel Martens, Dorothea C Reichelt

  • 1Department of Diagnostic and Interventional Radiology, University Düsseldorf, Medical Faculty, Moorenstr. 5, 40225, Düsseldorf, Germany.

Neuroradiology
|September 13, 2013
PubMed

Insights

Perfusion computed tomography (PCT) can predict long-term outcomes in subarachnoid hemorrhage (SAH) patients. Elevated maximum mean transit time (MTTPEAK) on PCT indicates a higher risk of unfavorable outcomes.

Area of Science:

  • Neuroradiology
  • Neurology
  • Medical Imaging

Background:

  • Dynamic perfusion computed tomography (PCT) is a key diagnostic tool for detecting vasospasm post-subarachnoid hemorrhage (SAH).
  • Assessing the prognostic value of PCT parameters in SAH patients is crucial for long-term outcome prediction.

Purpose of the Study:

  • To evaluate the prognostic impact of perfusion computed tomography (PCT) parameters on the long-term outcomes of patients following subarachnoid hemorrhage (SAH).

Main Methods:

  • Retrospective analysis of 312 patients with spontaneous subarachnoid hemorrhage (SAH).
  • Assessment of long-term outcomes using the modified Rankin scale (mRS) via questionnaire at 23.06 ± 14.33 months post-ictus.
  • Utilized perfusion computed tomography (PCT) data from the initial days after subarachnoid hemorrhage (SAH).

Main Results:

  • Maximum mean transit time over several examinations per hemisphere (MTTPEAK) significantly correlated with long-term modified Rankin scale (mRS) scores (p ≤ 0.001, r = 0.422).
  • MTTPEAK emerged as the second most significant predictor of long-term mRS, after initial hemorrhage severity.
  • An MTTPEAK threshold of 3.98 s predicted unfavorable long-term outcomes (mRS ≥ 2) with 69.6% diagnostic accuracy.

Conclusions:

  • Perfusion computed tomography (PCT) data are relevant for predicting the long-term clinical outcomes of subarachnoid hemorrhage (SAH) patients.
  • Identifying high-risk patients aids in therapeutic decision-making and risk-benefit analysis for escalated treatment.
Abstract