E-172 CT perfusion derived hypoperfusion intensity ratio predicts infarct volume and growth pre- and post-endovascular therapy in large vessel occlusion acute ischemic stroke
Bibliographic record
Abstract
Purpose Evaluation of large vessel occlusion (LVO) acute ischemic stroke (AIS) patients for endovascular therapy (EVT) is a multifaceted decision-making process. Previous work using CT angiography has demonstrated that collateral blood supply is a key determinant of infarct volume and functional outcomes in LVO AIS. The CT Perfusion (CTP) derived Hypoperfusion Intensity Ratio (HIR), Tmax >10 s over Tmax >6 s within the affected territory, is a surrogate marker of collateral status. The purpose of this study is to investigate whether an association exists between HIR and infarct volumes in LVO AIS. Materials and Methods IRB approval was obtained. The medical records and imaging of patients with suspected LVO AIS and undergoing CTP (between 0–24 hours since last known well) and treated with EVT between March to September 2019 were retrospectively reviewed. Patients with preexisting infarcts were excluded. CTP was processed using software (RAPID, Ischemaview) with HIR automatically calculated. ASPECTS scores were obtained and infarct volumes calculated using the ABC/2 method on the initial noncontrast CT and CT performed 24–72 hours post-EVT. Spearman correlation test were performed between HIR and initial CT ASPECTS, initial CT infarct volume. After excluding 5 patients without successful reperfusion, HIR Spearman correlation with post-EVT CT ASPECTS, final infarct size, and infarct growth was assessed. Results Thirty-two patients were included in the study (20 males and 12 females), with a mean age of 63.0 years (SD=17.3). Initial ASPECTS scores ranged from 2 to 10 (median 7). Initial infarct volume (IIV) ranged from 0 to 113 ml (median 19). Successful reperfusion (mTICI 2B, 2C, or 3) was obtained in 28/32 (88%). Final ASPECTS scores at discharge ranged from 0 to 10 (median 5.5), and final infarct volume (FIV) ranged from 0 to 367 ml (median 72 ml). There was a significant correlation between HIR and initial ASPECTS (-0.37, p=0.03), and a trend to significance with IIV (0.32, p=0.07) (figure 1A and 1B). With non-recanalizers excluded, a significant correlation was observed between HIR and final ASPECTS (-0.51, p=0.006), FIV (0.50, p=0.009), and infarct growth pre- and post-EVT (0.41, p=0.03) (figure 1B and 1C). Conclusions In the time-sensitive environment of LVO AIS, HIR allows rapid assessment of collateral status, showing similar correlations with infarct size as CTA collateral scoring. Further investigation is warranted to determine whether HIR is predictive of clinical outcomes. Disclosures M. Jutras: None. D. Lyndon: None. B. Niu: None. M. Lloret Villas: None. S. Yip: None. F. Settecase: None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".