Individual Patient Data Meta-Analysis of Dynamic Cerebral Autoregulation and Functional Outcome After Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: The relationship between dynamic cerebral autoregulation (dCA) and functional outcome after acute ischemic stroke (AIS) is unclear. Previous studies are limited by small sample sizes and heterogeneity. METHODS: We performed a 1-stage individual patient data meta-analysis to investigate associations between dCA and functional outcome after AIS. Participating centers were identified through a systematic search of the literature and direct invitation. We included centers with dCA data within 1 year of AIS in adults aged over 18 years, excluding intracerebral or subarachnoid hemorrhage. Data were obtained on phase, gain, coherence, and autoregulation index derived from transfer function analysis at low-frequency and very low-frequency bands. Cerebral blood velocity, arterial pressure, end-tidal carbon dioxide, heart rate, stroke severity and sub-type, and comorbidities were collected where available. Data were grouped into 4 time points after AIS: <24 hours, 24 to 72 hours, 4 to 7 days, and >3 months. The modified Rankin Scale assessed functional outcome at 3 months. Modified Rankin Scale was analyzed as both dichotomized (0 to 2 versus 3 to 6) and ordinal (modified Rankin Scale scores, 0–6) outcomes. Univariable and multivariable analyses were conducted to identify significant relationships between dCA parameters, comorbidities, and outcomes, for each time point using generalized linear (dichotomized outcome), or cumulative link (ordinal outcome) mixed models. The participating center was modeled as a random intercept to generate odds ratios with 95% CIs. RESULTS: The sample included 384 individuals (35% women) from 7 centers, aged 66.3±13.7 years, with predominantly nonlacunar stroke (n=348, 69%). In the affected hemisphere, higher phase at very low-frequency predicted better outcome (dichotomized modified Rankin Scale) at <24 (crude odds ratios, 2.17 [95% CI, 1.47–3.19]; P <0.001) hours, 24–72 (crude odds ratios, 1.95 [95% CI, 1.21–3.13]; P =0.006) hours, and phase at low-frequency predicted outcome at 3 (crude odds ratios, 3.03 [95% CI, 1.10–8.33]; P =0.032) months. These results remained after covariate adjustment. CONCLUSIONS: Greater transfer function analysis-derived phase was associated with improved functional outcome at 3 months after AIS. dCA parameters in the early phase of AIS may help to predict functional outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".