The Utility of Valsalva Maneuver in the Diagnosis of Orthostatic Disorders (P5.121)
Bibliographic record
Abstract
OBJECTIVE: To assess patterns of systolic blood pressure (SBP) fluctuations during Valsalva maneuver (VM) and head-up tilt (HUT) testing in orthostatic intolerance (OI). BACKGROUND: VM is a reliable clinical tool in the autonomic assessment, which allows for assessment of important cardiovascular markers including baroreflex sensitivity (BRS). The VM offers immediate quantitative analyses of hemodynamic responses, which have been shown to demonstrate reproducible patterns. VM patterning may improve the diagnosis of various autonomic disorders characterized by orthostasis, where a single estimation of BRS is often compromised due to severe adrenergic failure. Given that OI can be a substantial cause of morbidity, improved clinical assessment of orthostasis would be beneficial. Therefore, our goal was to clarify the utility of the VM in the diagnosis of OI. METHODS: Patients with neurogenic orthostatic hypotension (NOH, n=26), postural tachycardia syndrome (POTS, n=26) and symptomatic orthostatic intolerance (SOI, n=14) were compared to a healthy population (Control, n=107) and inappropriate sinus tachycardia (IST, n=7). Quantitative VM analysis included adrenergic and vagal BRS measurements (BRSa/BRSa1 and BRSv). Repeated VM trials during the same visit were evaluated for reproducibility with rANOVA. RESULTS: In NOH, cardiovagal SBP decrements in VM and HUT were correlated (r=0.660, p<0.001); a “V” pattern of VM indicated alpha BRSa failure. Yet, BRSa1 did not reveal changes vs. Controls (p>0.05) and was not applicable in 60[percnt] of NOH. In SOI, compared to Controls there were larger cardiovagal SBP decrements (p<0.05) and BRSa1 that contradicted greater adrenergic dysfunction (defined by the Composite Autonomic Severity Score). Overshoot in phase IV dipped below baseline or dropped ≥10mmHg over 8s in POTS (“N” pattern), but by only 3s in IST (“M” pattern”). CONCLUSIONS: Pathological hemodynamic responses to the VM may compromise BRSa evaluation; however, SBP patterning is a valuable tool for detecting and differentiating OI.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".