Blood Pressure Variability in Acute Stroke: A Narrative Review
Bibliographic record
Abstract
The management of blood pressure variability (BPV) in acute stroke presents a complex challenge with profound implications for patient outcomes. This narrative review examines the role of BPV across various stages of acute stroke care, highlighting its impact on treatment strategies and prognostic considerations. In the prehospital setting, while guidelines lack specific recommendations for BP management, emerging evidence suggests a potential link between BPV and outcomes. Among ischaemic stroke patients who are ineligible for reperfusion therapies, BPV independently influences functional outcomes, emphasising the need for individualised approaches to BP control. During intravenous thrombolysis and endovascular therapy, the intricate interplay between BP levels, recanalisation status, and BPV is evident. Striking a balance between aggressive BP lowering and avoiding hypoperfusion-related complications is essential. Intracerebral haemorrhage management is further complicated by BPV, which emerges as a predictor of mortality and disability, necessitating nuanced BP management strategies. Finally, among patients with acute subarachnoid haemorrhage, increased BPV may be correlated with a rebleeding risk and worse outcomes, emphasizing the need for BPV monitoring in this population. Integration of BPV assessment into clinical practice and research protocols is crucial for refining treatment strategies that are tailored to individual patient needs. Future studies should explore novel interventions targeting BPV modulation to optimise stroke care outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".