Accuracy and Prognostic Role of NCCT-ASPECTS Depend on Time from Acute Stroke Symptom-onset for both Human and Machine-learning Based Evaluation

Abstract

Purpose We hypothesize that the detectability of early ischemic changes on non-contrast computed tomography (NCCT) is limited in hyperacute stroke for both human and machine-learning based evaluation. In short onset-time-to-imaging (OTI), the CT angiography collateral status may identify fast stroke progressors better than early ischemic changes quantified by ASPECTS. Methods In this retrospective, monocenter study, CT angiography collaterals (Tan score) and ASPECTS on acute and follow-up NCCT were evaluated by two raters. Additionally, a machine-learning algorithm evaluated the ASPECTS scale on the NCCT (e-ASPECTS). In this study 136 patients from 03/2015 to 12/2019 with occlusion of the main segment of the middle cerebral artery, with a defined symptom-onset-time and successful mechanical thrombectomy (MT) (modified treatment in cerebral infarction score mTICI = 2c or 3) were evaluated. Results Agreement between acute and follow-up ASPECTS were found to depend on OTI for both human (Intraclass correlation coefficient, ICC = 0.43 for OTI < 100 min, ICC = 0.57 for OTI 100–200 min, ICC = 0.81 for OTI ≥ 200 min) and machine-learning based ASPECTS evaluation (ICC = 0.24 for OTI < 100 min, ICC = 0.61 for OTI 100–200 min, ICC = 0.63 for OTI ≥ 200 min). The same applied to the interrater reliability. Collaterals were predictors of a favorable clinical outcome especially in hyperacute stroke with OTI < 100 min (collaterals: OR = 5.67 CI = 2.38–17.8, p < 0.001; ASPECTS: OR = 1.44, CI = 0.91–2.65, p = 0.15) while ASPECTS was in prolonged OTI ≥ 200 min (collaterals OR = 4.21,CI = 1.36–21.9, p = 0.03; ASPECTS: OR = 2.85, CI = 1.46–7.46, p = 0.01). Conclusion The accuracy and reliability of NCCT-ASPECTS are time dependent for both human and machine-learning based evaluation, indicating reduced detectability of fast stroke progressors by NCCT. In hyperacute stroke, collateral status from CT-angiography may help for a better prognosis on clinical outcome and explain the occurrence of futile recanalization.

Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke

“…Moreover, previous studies have shown that for patients with AIS, large infarct volume, poor collateral circulation, and high NIHSS score are significant predictors of functional outcomes and indicators of the severity of the neurological injury ( 6 – 8 ). However, unlike in other studies, our results show that the symptom onset time and interval from puncture to recanalization did not play a particularly strong role in predicting futile recanalization ( 12 , 40 ).…”

Venous Outflow Profiles Are Linked to Clinical Outcomes in Ischemic Stroke Patients with Extensive Baseline Infarct

“…Arterial collaterals have been extensively studied in stroke research which, if present, may sustain salvageable brain tissue and reduce ischemic tissue damage. [10][11][12][13][14] In low ASPECTS patients, successful EVT was linked to improved clinical outcomes in dependence of good arterial collaterals. 15 However, cerebral perfusion is not exclusively determined by its arterial inflow, but also by its venous outflow (VO).…”

Benefits of Endovascular Treatment in Late Window for Acute Ischemic Stroke Selected without CT Perfusion: A Real-World Study

“… 30 , 31 On the other hand, the status of collaterals is considered to be a critical determinant for distinguishing between “rapid progressing” and “slow progressing”, 32 and robust collaterals can effectively prevent the extension of the core infarct by rescuing the penumbra area. 33 Therefore, in the extended time window, patients with a small core, moderate to good collateral, are by definition “slow progressors” who can potentially benefit most from recanalization penumbra, 14 , 34 , 35 and these are also important factors for achieving good outcomes in our study.…”