Outcome after extracorporeal membrane oxygenation therapy in Norwood patients before the bidirectional Glenn operation.

OBJECTIVES: Patients after the Norwood procedure are prone to postoperative instability. Extracorporeal membrane oxygenation (ECMO) can help to overcome short-term organ failure. This retrospective single-centre study examines ECMO weaning, hospital discharge and long-term survival after ECMO therapy between Norwood and bidirectional Glenn palliation as well as risk factors for mortality. METHODS: In our institution, over 450 Norwood procedures have been performed. Since the introduction of ECMO therapy, 306 Norwood operations took place between 2007 and 2022, involving ECMO in 59 cases before bidirectional Glenn. In 48.3% of cases, ECMO was initiated intraoperatively post-Norwood. Patient outcomes were tracked and mortality risk factors were analysed using uni- and multivariable testing. RESULTS: ECMO therapy after Norwood (median duration: 5 days; range 0-17 days) saw 31.0% installed under CPR. Weaning was achieved in 46 children (78.0%), with 55.9% discharged home after a median of 45 (36-66) days. Late death occurred in 3 patients after 27, 234 and 1541 days. Currently, 30 children are in a median 4.8 year (3.4-7.7) follow-up. At the time of inquiry, 1 patient awaits bidirectional Glenn, 6 are at stage II palliation, Fontan was completed in 22 and 1 was lost to follow-up post-Norwood. Risk factor analysis revealed dialysis (P < 0.001), cerebral lesions (P = 0.026), longer ECMO duration (P = 0.002), cardiac indication and lower body weight (P = 0.038) as mortality-increasing factors. The 10-year mortality probability after ECMO therapy was 48.5% (95% CI 36.5-62.9%). CONCLUSIONS: ECMO therapy in critically ill patients after the Norwood operation may significantly improve survival of a patient cohort otherwise forfeited and give the opportunity for successful future-stage operations.

Physiological Modeling of Hemodynamic Responses to Sodium Nitroprusside.

BACKGROUND: Computational modeling of physiology has become a routine element in the development, evaluation, and safety testing of many types of medical devices. Members of the Food and Drug Administration have recently published a manuscript detailing the development, validation, and sensitivity testing of a computational model for blood volume, cardiac stroke volume, and blood pressure, noting that such a model might be useful in the development of closed-loop fluid administration systems. In the present study, we have expanded on this model to include the pharmacologic effect of sodium nitroprusside and calibrated the model against our previous experimental animal model data. METHODS: Beginning with the model elements in the original publication, we added six new parameters to control the effect of sodium nitroprusside: two for the onset time and clearance rates, two for the stroke volume effect (which includes venodilation as a "hidden" element), and two for the direct effect on arterial blood pressure. Using this new model, we then calibrated the predictive performance against previously collected animal study data using nitroprusside infusions to simulate shock with the primary emphasis on MAP. Root-mean-squared error (RMSE) was calculated, and the performance was compared to the performance of the model in the original study. RESULTS: RMSE of model-predicted MAP to actual MAP was lower than that reported in the original model, but higher for SV and CO. The individually fit models showed lower RMSE than using the population average values for parameters, suggesting the fitting process was effective in identifying improved parameters. Use of partially fit models after removal of the lowest variance population parameters showed a very minor decrement in improvement over the fully fit models. CONCLUSION: The new model added the clinical effects of SNP and was successfully calibrated against experimental data with an RMSE of <10% for mean arterial pressure. Model-predicted MAP showed an error similar to that seen in the original base model when using fluid shifts, heart rate, and drug dose as model inputs.