Predictors of in-ICU length of stay among congenital heart defect patients using artificial intelligence model: A pilot study.

Objective: This study aims to develop a predictive model using artificial intelligence to estimate the ICU length of stay (LOS) for Congenital Heart Defects (CHD) patients after surgery, improving care planning and resource management.

Design: We analyze clinical data from 2240 CHD surgery patients to create and validate the predictive model. Twenty AI models are developed and evaluated for accuracy and reliability.

Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study.

Background: Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pre-surgical mortality is scarce.

Functional Performance of Different Venous Limb Options in Simulated Neonatal/Pediatric Cardiopulmonary Bypass Circuits.

Objective: Hemodilution is a concern in cardiopulmonary bypass (CPB). Using a smaller dual tubing rather than a single larger inner diameter (ID) tubing in the venous limb to decrease prime volume has been a standard practice. The purpose of this study is to evaluate these tubing options.