Non-invasive detection of a femoral-to-radial arterial pressure gradient in intensive care patients with vasoactive agents
Bibliographic record
Abstract
Abstract Background In patient requiring vasopressors, the radial artery pressure may underestimate the true central aortic pressure leading to unnecessary interventions. When using a femoral and a radial arterial line, this femoral-to-radial arterial pressure gradient (FR-APG) can be detected. Our main objective was to assess the accuracy of non-invasive blood pressure (NIBP) measures; specifically, measuring the gradient between the NIBP obtained at the brachial artery and the radial artery pressure and calculating the non-invasive brachial-to-radial arterial pressure gradient (NIBR-APG) to detect an FR-APG. The secondary objective was to assess the prevalence of the FR-APG in a targeted sample of critically ill patients. Methods Adult patients in an intensive care unit requiring vasopressors and instrumented with a femoral and a radial artery line were selected. We recorded invasive radial and femoral arterial pressure, and brachial NIBP. Measurements were repeated each hour for 2 h. A significant FR-APG (our reference standard) was defined by either a mean arterial pressure (MAP) difference of more than 10 mmHg or a systolic arterial pressure (SAP) difference of more than 25 mmHg. The diagnostic accuracy of the NIBR-APG (our index test) to detect a significant FR-APG was estimated and the prevalence of an FR-APG was measured and correlated with the NIBR-APG. Results Eighty-one patients aged 68 [IQR 58–75] years and an SAPS2 score of 35 (SD 7) were included from which 228 measurements were obtained. A significant FR-APG occurred in 15 patients with a prevalence of 18.5% [95%CI 10.8–28.7%]. Diabetes was significantly associated with a significant FR-APG. The use of a 11 mmHg difference in MAP between the NIBP at the brachial artery and the MAP of the radial artery led to a specificity of 92% [67; 100], a sensitivity of 100% [95%CI 83; 100] and an AUC ROC of 0.93 [95%CI 0.81–0.99] to detect a significant FR-APG. SAP and MAP FR-APG correlated with SAP (r2 = 0.36; p < 0.001) and MAP (r2 = 0.34; p < 0.001) NIBR-APG. Conclusion NIBR-APG assessment can be used to detect a significant FR-APG which occur in one in every five critically ill patients requiring vasoactive agents.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".