Haemorrhage after thrombectomy with adjuvant thrombolysis in unknown onset stroke depends on high early lesion water uptake
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In wake-up stroke, CT-based quantitative net water uptake (NWU) might serve as an alternative tool to MRI to guide intravenous thrombolysis with alteplase (IVT). An important complication after IVT is symptomatic intracerebral haemorrhage (sICH). As NWU directly implies ischaemic lesion progression, reflecting blood-brain barrier injury, we hypothesised that NWU predicts sICH in patients who had a ischaemic stroke undergoing thrombectomy with unknown onset. METHODS: Consecutive analysis of all patients who had unknown onset anterior circulation ischaemic stroke who underwent CT at baseline and endovascular treatment between December 2016 and October 2020. Quantitative NWU was assessed on baseline CT. The primary endpoint was sICH. The association of NWU and other baseline parameters to sICH was investigated using inverse-probability weighting (IPW) analysis. RESULTS: A total of 88 patients were included, of which 46 patients (52.3%) received IVT. The median NWU was 10.7% (IQR: 5.1-17.7). The proportion of patients with any haemorrhage and sICH were 35.2% and 13.6%. NWU at baseline was significantly higher in patients with sICH (19.1% vs 9.6%, p<0.0001) and the median Alberta Stroke Program Early CT Score (ASPECTS) was lower (5 vs 8, p<0.0001). Following IPW, there was no association between IVT and sICH in unadjusted analysis. However, after adjusting for ASPECTS and NWU, there was a significant association between IVT administration and sICH (14.6%, 95% CI: 3.3% to 25.6%, p<0.01). CONCLUSION: In patients with ischaemic stroke with unknown onset, the combination of high NWU with IVT is directly linked to higher rates of sICH. Besides ASPECTS for evaluating the extent of the early infarct lesion, quantitative NWU could be used as an imaging biomarker to assess the degree of blood-brain barrier damage in order to predict the risk of sICH in patients with wake up stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".