Pulse Wave Analysis Using the Pressure Recording Analytical Method to Measure Cardiac Output in Pediatric Cardiac Surgery Patients: A Method Comparison Study Using Transesophageal Doppler Echocardiography as Reference Method
Bibliographic record
Abstract
BACKGROUND: Cardiac output (CO) is a key determinant of oxygen delivery, but choosing the optimal method to obtain CO in pediatric patients remains challenging. The pressure recording analytical method (PRAM), implemented in the MostCareUp system (Vygon), is an invasive uncalibrated pulse wave analysis (PWA) method to measure CO. The objective of this study is to compare CO measured by PRAM (PRAM-CO; test method) with CO simultaneously measured by transesophageal Doppler echocardiography (TEE-CO; reference method) in pediatric patients. METHODS: In this prospective observational method comparison study, PRAM-CO and TEE-CO were assessed in pediatric elective cardiac surgery patients at 2 time points: after anesthesia induction and after surgery. The study was performed in a German university medical center from March 2019 to March 2020. We included pediatric patients scheduled for elective cardiac surgery with arterial catheter and TEE monitoring. PRAM-CO and TEE-CO were compared using Bland-Altman analysis accounting for repeated measurements per subject, and the percentage error (PE). RESULTS: We included 52 PRAM-CO and TEE-CO measurement pairs of 30 patients in the final analysis. Mean ± SD TEE-CO was 2.15 ± 1.31 L/min (range 0.55-6.07 L/min), and mean PRAM-CO was 2.21 ± 1.38 L/min (range 0.55-5.90 L/min). The mean of the differences between TEE-CO and PRAM-CO was -0.06 ±0.38 L/min with 95% limits of agreement (LOA) of 0.69 (95% confidence interval [CI], 0.53-0.82 L/min) to -0.80 L/min (95% CI, -1.00 to -0.57 L/min). The resulting PE was 34% (95% CI, 27%-41%). CONCLUSIONS: With a PE of <45%, PRAM-CO shows clinically acceptable agreement with TEE-CO in hemodynamically stable pediatric patients before and after cardiac surgery.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".