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Simplified Assessment of Lesion Water Uptake for Identification of Patients Within 4.5 Hours of Stroke Onset: An Analysis of the MissPerfeCT Study

Abstract

Background And Purpose: Many patients with stroke cannot receive intravenous thrombolysis because the time of symptom onset is unknown. We tested whether a simple method of computed tomography (CT)-based quantification of water uptake in the ischemic tissue can identify patients with stroke onset within 4.5 hours.

Methods: This retrospective analysis of the MissPerfeCT study (August 2009 to November 2017) includes consecutive patients with known onset of symptoms from seven tertiary stroke centers. We developed a simplified algorithm based on region of interest (ROI) measurements to quantify water uptake of the ischemic lesion and thereby quantify time of symptom onset within and beyond 4.5 hours. Perfusion CT was used to identify ischemic brain tissue, and its density was measured in non-contrast CT and related to the density of the corresponding area of the contralateral hemisphere to quantify lesion water uptake.

Results: Of 263 patients, 204 (77.6%) had CT within 4.5 hours. Water uptake was significantly lower in patients with stroke onset within (6.7%; 95% confidence interval [CI], 6.0% to 7.4%) compared to beyond 4.5 hours (12.7%; 95% CI, 10.7% to 14.7%). The area under the curve for distinguishing these patient groups according to percentage water uptake was 0.744 with an optimal cut-off value of 9.5%. According to this cut-off the positive predictive value was 88.8%, sensitivity was 73.5%, specificity 67.8%, negative predictive value was 42.6%.

Conclusions: Ischemic stroke patients with unknown time of symptom onset can be identified as being within a timeframe of 4.5 hours using a ROI-based method to assess water uptake on admission non-contrast head CT.

Citing Articles

Haemorrhage after thrombectomy with adjuvant thrombolysis in unknown onset stroke depends on high early lesion water uptake.

Broocks G, Meyer L, Hanning U, Faizy T, Bechstein M, Kniep H Stroke Vasc Neurol. 2023; 9(4):390-398.

PMID: 37699728 PMC: 11420915. DOI: 10.1136/svn-2022-002264.


Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage.

Rusche T, Wasserthal J, Breit H, Fischer U, Guzman R, Fiehler J J Clin Med. 2023; 12(7).

PMID: 37048712 PMC: 10094957. DOI: 10.3390/jcm12072631.

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