The accuracy of blood pressure measured by arterial line and non-invasive cuff in critically ill children
Bibliographic record
Abstract
BACKGROUND: The accuracy of arterial lines (AL) using the flush test or stopcock test has not been described in children, nor has the difference between invasive arterial blood pressure (IABP) versus non-invasive cuff (NIBP) blood pressure. METHODS: After ethics approval and consent, we performed the flush test and stopcock test on AL (to determine over damping, under damping, and optimal damping), and determined the difference (NIBP-IABP) in systolic, diastolic, and mean blood pressure (ΔSBP, ΔDBP, and ΔMAP). The primary outcome was incidence (95 % CI) of optimally damped AL. Predictors of ΔBP (effect size (95 % CI)) were determined using multiple linear regression. RESULTS: There were 147 AL tests in 100 enrolled patients with mean age 44.7 (SD 56) months, weight 16.8 (SD 18.3) kg, male 59 %, postoperative-cardiovascular 52 %, peripheral-AL 78 %, inotropes 29 %, vasodilators 15 %, and ventilated 73 %. The flush test performed in 66 patients (45 %) showed optimal damping in 30 (46 %; 95 % CI 34, 57 %), over damping in 25 (38 %) and under damping in 11 patients (17 %). The stopcock test was over-damped in 128/146 patients (88 %), with the same damping as the flush test in 24/64 (38 %). In optimally damped (flush test) AL, ΔSBP, ΔDBP, and ΔMAP were 0.8 (SD 12.2), -5.2 (SD 8.7), and -4.9 (7.6) respectively. A second set of AL tests was done 2 h later on the same day in 62 patients; AL damping often changed (10/28 flush tests) and ΔBPs correlated poorly (r = 0.31-0.55). Predictors (effect size) of ΔDBP were vasodilator infusion (15.6 (2.9 to 28.3); p = 0.016) and optimal damping (-7.2 (-12.2 to 2.2); p = 0.005); and of ΔMAP were vasodilator infusion (10.0 (-0.3 to 20.4); p = 0.057) and optimal damping (-4.0 (-8 to 0.1); p = 0.058). There were no independent predictors of damping category (n = 66 flush tests). CONCLUSIONS: Optimally damped AL occur in half of critically ill children, and this is not predictable. There is much variability in ∆BP between NIBP and the gold standard IABP, and this varies even in the same patient on the same day, and is not easily predictable. In critically ill children, NIBP may not be accurate enough to guide management, and more attention to ensuring the AL is optimally damped is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".