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.

The reduction in hospital stay and costs in the care of patients with congenital heart diseases undergoing fast-track cardiac surgery.

Objective: To assess the care provided to patients with congenital heart diseases and ischemic heart diseases undergoing cardiac surgery according to the fast-track recovery protocol compared with those undergoing the conventional procedure.

Methods: The transfer of patients from one hospital unit to another was assessed for 175 patients, 107 (61%) men and 68 (39%) women, with ages ranging from 0.3 to 81 years.