Cerebral Blood Flow Dynamics in Neurogenic Orthostatic Hypotension: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Neurogenic orthostatic hypotension (nOH) causes pathological falls in standing blood pressure that may or may not be symptomatic. nOH also raises the risk of poor neurological outcomes irrespective of symptom presence, possibly reflecting subclinical cerebral hypoperfusion. Dynamic changes in cerebral blood flow velocity (CBFv) help infer how blood pressure fluctuations influence CBFv and cerebral autoregulation. Whether CBFv is impacted in nOH relative to related conditions without nOH and healthy controls (HC) remains unresolved. Whether nOH symptoms reflect greater CBFv falls is also unclear. This review aimed to compare CBFv between nOH and HC, nOH and disease-matched controls (eg, Parkinson disease±nOH), and between symptomatic and asymptomatic nOH. METHODS: Embase and MEDLINE were searched up to April 2024. Means, SDs, and sample sizes for supine and upright CBFv were extracted to generate standardized effect sizes (Hedge g). Random-effects modeling compared postintervention between-group effect sizes. RESULTS: Seventeen studies were included for review. Thirteen studies were suitable for meta-analysis comparing nOH to HC, 2 comparing disease-matched controls to nOH and to HC, and 3 for symptomatic comparisons. Compared with HC, nOH had larger drops in CBFv (Hedge g, −0.64 [95% CI, −0.85 to −0.44]; P <0.001). CBFv falls between nOH and disease-matched controls were similar ( P =0.17). Symptomatic nOH had larger CBFv drops (Hedge g, 0.84 [95% CI, 0.212–1.461]; P =0.009) than asymptomatic nOH. CONCLUSIONS: nOH causes significant orthostatic reductions in CBFv compared with HC, and symptomatic patients experience greater falls in CBFv than asymptomatic patients. Recognizing the clinical implications of CBFv in nOH is crucial for mitigating adverse 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".