Association between intraprocedural drops in blood pressure and infarct growth rate patterns after acute large-vessel occlusions
Bibliographic record
Abstract
BACKGROUND: Infarct growth rate (IGR) differs among patients with acute ischemic stroke due to large vessel occlusion (LVO-AIS), and this variability has critical clinical repercussions. We explored IGR patterns and their association with blood pressure during endovascular therapy (EVT). METHODS: 13 mL) and ExpIGR-B (<13 mL). Intraprocedural blood pressure (BP) drops were calculated as the difference between median arterial pressure (MAP) at admission and the lowest intraprocedural MAP reading before recanalization, and the area between admission MAP threshold and all lower measurements of intraprocedural MAP. Logistic and linear regression were used to investigate associations between variables of interest. RESULTS: Of 159 modified Thrombolysis in Cerebral Infarction (mTICI) 2 c-3 patients, we found that 36% demonstrated ExpIGR-A, 31% ExpIGR-B, and 32.7% NonExp patterns. The Exp-A and Exp-B groups differed significantly in National Institutes of Health Stroke Scale (NIHSS) score, Alberta Stroke Program Early CT Score (ASPECTS), glucose, and FIV. The Exp-A and NonExp groups differed in rCBF <30% vol, and time of stroke onset (SO) to admission CTP; and the Exp-B and NonExp groups in NIHSS, rCBF <30%, Tmax <6 s volume, collateral flow measured by hypoperfusion intensity ratio (HIR), and FIV. Hypotensive MAP area (HMA) was independently associated with an ExpIGR-A pattern. Infarct volume increased by 1 mL per 100 units of hypotensive area and 4.2 mL per 0.1 units of HIR, with a significant interaction between both variables. CONCLUSION: After an LVO-AIS, the IGR can be differentiated into two distinct exponential and non-exponential patterns. A subgroup of patients with the exponential pattern experienced clinically meaningful infarct growth rates between CTP acquisition and reperfusion and seem to be highly vulnerable to episodes of sustained intraprocedural BP drops during EVT.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".