Non-Invasive Measurement of Right Atrial Pressure by Near-Infrared Spectroscopy: Preliminary Experience. A Report from the SICA-HF Study
Bibliographic record
Abstract
AIMS: To assess the clinical value of measuring right atrial pressure (RAP) using near-infrared spectroscopy (NIRS) in patients with chronic heart failure (CHF). METHODS AND RESULTS: RAP was measured non-invasively using NIRS over the external jugular vein (Venus 1000, Mespere LifeSciences, Canada) in ambulatory patients with CHF enrolled in the Studies Investigating Co-morbidities Aggravating Heart Failure (SICA-HF) programme. Comparing 243 patients with CHF (mean age 71 years; mean left ventricular ejection fraction (LVEF) 45%, median NT-proBNP 788 ng/L) to 49 controls (NT-proBNP ≤125 ng/L), RAP was 7 [interquartile range (IQR) 4-11] mmHg vs. 4 (IQR 3-8) mmHg (P < 0.001). Those with RAP ≥10 mmHg (n = 75) were older, had more severe clinical congestion and renal dysfunction, higher plasma NT-proBNP, larger left atrial volume, higher systolic pulmonary pressure and were more often in atrial fibrillation but their LVEF was similar to patients with lower RAP. During a median follow-up of 595 (IQR: 492-714) days, 49 patients (20%) died or were hospitalized for worsening CHF. Compared with patients with RAP ≤5 mmHg, those with RAP ≥10 mmHg had a greater risk of an event (hazard ratio 2.38, 95% confidence interval 1.19-4.75, P = 0.014). RAP measured by NIRS predicted outcome, competing with NT-proBNP in multivariable models. CONCLUSIONS: Measuring RAP using NIRS identifies ambulatory patients with CHF who have more severe congestion and a worse outcome. The device might be a useful objective method of monitoring RAP, especially for those inexperienced in eliciting physical signs or when measurement of natriuretic peptides is not immediately available.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".