Abstract 17405: Phosphodiesterase-based Vasodilation Improves Oxygen Delivery and Clinical Outcomes Following Stage 1 Palliation
Bibliographic record
Abstract
Introduction: The use of systemic vasodilators is known to decrease the incidence of postoperative cardiac arrest in infants with hypoplastic left heart syndrome (HLHS) following stage 1 palliation (S1P) with Blalock-Taussig shunt. Their impact on measured oxygen delivery (DO2) are uncharacterized. Methods: In 2013, we implemented a treatment algorithm at our institution to standardize care and lower systemic vascular resistance (SVR) in the postoperative period (A). We measured standard hemodynamic variables in consecutive neonates prior to (n=32) and following (n=24) its implementation. 92% of these infants received a Sano modification. We continuously measured oxygen consumption (E-COVX module™, GE Healthcare) and venous oxyhemoglobin saturation (SvO2, PediaSat, Edwards) for Fick-based calculation of DO2 and SVR in a subset (n=21) of patients. Variables were compared pre and post-implementation using linear mixed effects models. Results: Demographic, anatomic and echocardiographic risk factors were similar between groups, although aortic cross-clamp (ACC) time was significantly shorter following protocol implementation (120.9 vs 91.9 mins, P<0.001). When corrected for ACC time, serum lactate (p<0.001, B) and incidence of postoperative cardiac arrest (P=0.04, C) were lower (B), and SvO2 (P<0.001) was higher following protocol implementation. Systemic cardiac output (β[SE], 0.52 [0.32] L/min/m2; P=0.10) and systemic DO2 (β[SE], 86.7 [55.4] mL/min/m2; P=0.12, D) were higher post-implementation. DO2 was most closely correlated with SVR (r2=0.87), and commonly utilized surrogate variables such as arterial saturation (r2=0.01), SvO2 (r2=0.1), and arterial-venous saturation difference (r2=0.35) correlated poorly. Conclusions: The implementation of a phosphodiesterase-based treatment algorithm decreased SVR, lactate accumulation and the incidence of cardiac arrest in infants with HLHS following S1P with Sano modification.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".