Combined effect of cortical superficial siderosis and cerebral microbleed on short-term and long-term outcomes after intracerebral haemorrhage

Background and purpose Cortical superficial siderosis(cSS)and cerebral microbleed(CMB)have distinct effects on intracerebral haemorrhage(ICH).We aim to investigate the combined effect of cSS and CMB on outcomes after ICH.Methods Based on a single-centre stroke registry database,patients with spontaneous ICH who had CT scan within 48 hours after ictus and MRI subsequently were identified.Eligible patients were divided into four groups(cSS-CMB−,cSS-CMB+,cSS+CMB−,cSS+CMB+)according to cSS and CMB on susceptibility-weighted image of MRI.Primary outcomes were haematoma volume on admission and unfavourable outcome defined as modified Rankin Scale scores≥3 at 3 months.Secondary outcomes were all-cause death,recurrence of stroke and ICH during follow-up(median follow-up 2.0 years,IQR 1.0-3.0 years).Results A total of 673 patients were identified from 1044 patients with spontaneous ICH.131(19.5%)had cSS and 468(69.5%)had CMB.Patients with cSS+CMB+had the highest rate of poor outcome at 3 months,as well as all-cause death,recurrent stroke and ICH during follow-up.In cSS−patients,CMB was associated with smaller haematoma(β−0.13;95%CI−0.22 to−0.03;p=0.009),but it still increased risks of recurrent ICH(OR 4.6;95%CI 1.3 to 15.6;p=0.015)and stroke(OR 2.0;95%CI 1.0 to 4.0;p=0.049).These effects of CMB became unremarkable in the context of cSS+.Conclusions Patients with different combinations of cSS and CMB have distinct patterns of short-term and long-term outcomes.Although CMB is related to restrained haematoma,it does not improve long-term outcomes.

Treatment with intravenous alteplase in ischaemic stroke patients with onset time between 4.5 and 24 hours(HOPE):protocol for a randomised,controlled,multicentre study

Background While intravenous thrombolysis is recommended for patients who had an acute ischaemic stroke(AIS)within 4.5hours of symptom onset,there are few randomised trials investigating the benefits of thrombolysis beyond this therapeutic window.Aim To determine whether patients who had an AIS selected with the presence of potentially salvageable tissue on CT perfusion at 4.5–24hours after stroke onset(for stroke with unknown onset time,the midpoint of the time last known to be well and symptom recognition time;for wake-up stroke,the midpoint of the time last known to be well or sleep onset and wake up time)will benefit from intravenous thrombolysis.Design HOPE is a prospective,multicentre,randomised,open-label blinded endpoint trial with the stage of phase III.The treatment allocation employs 1:1 randomisation.The treatment arm under investigation is alteplase with standard therapy,the control arm is standard therapy.Eligibility imaging criteria include ischaemic core volume≤70mL,penumbra≥10mL and mismatch≥20%.Study outcomes The primary outcome is non-disabled functional outcome(assessed as modified Rankin Scale score of 0–1 at 90 days).Discussion HOPE is the first trial to investigate whether intravenous thrombolysis with alteplase offers benefits in patients who had an AIS presenting within 4.5–24hours,which has the potential to extend time window and expand eligible population for thrombolysis therapy.

Deep learning for automatically predicting early haematoma expansion in Chinese patients

Background and purpose Early haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage(ICH)patients.The aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning model and validate its prediction accuracy.Methods Data of this study were obtained from a prospectively enrolled cohort of patients with primary supratentorial ICH from our centre.We developed a deep learning model to predict haematoma expansion and compared its performance with conventional non-contrast CT(NCCT)markers.To evaluate the predictability of this model,it was also compared with a logistic regression model based on haematoma volume or the BAT score.Results A total of 266 patients were finally included for analysis,and 74(27.8%)of them experienced early haematoma expansion.The deep learning model exhibited highest C statistic as 0.80,compared with 0.64,0.65,0.51,0.58 and 0.55 for hypodensities,black hole sign,blend sign,fluid level and irregular shape,respectively.While the C statistics for swirl sign(0.70;p=0.211)and heterogenous density(0.70;p=0.141)were not significantly higher than that of the deep learning model.Moreover,the predictive value for the deep learning model was significantly superior to that of the logistic model of haematoma volume(0.62;p=0.042)and the BAT score(0.65;p=0.042).Conclusions Compared with the conventional NCCT markers and BAT predictive model,the deep learning algorithm showed superiority for predicting early haematoma expansion in ICH patients.