ABSTRACT
This study assessed the effects of repeated courses of antenatal corticosteroids on biometric characteristics, salivary cortisol, and heart function in children 6 to 10 years of age using a retrospective cohort study. Twenty-nine children whose mothers had received two or more courses of antenatal corticosteroids were identified from hospital charts. Eighty-seven children whose mothers did not receive antenatal corticosteroids were frequency matched with the exposed group by child's age, sex, and ethnicity. The body development, heart function, and salivary corticosteroid level were evaluated at 6 to 10 years of age. The percentiles of body measurements were calculated based on the 2000 Centers for Disease Control and Prevention growth charts. The general linear models were applied to assess the observed association. Decreased head circumference ( P=0.017) and body mass index (BMI) ( P=0.047) in children 6 to 10 years of age were associated with repeated courses of antenatal corticosteroids. Morning salivary cortisol level was lower in the exposed group than the unexposed group ( P=0.048). No difference was found in height, weight, blood pressure, heart rate, and echocardiogram measurements between the two groups. Repeated courses of antenatal corticosteroid therapy are associated with decreased head circumference, BMI, and salivary cortisol level in children 6 to 10 years of age.
Subject(s)
Adrenal Cortex Hormones/adverse effects , Prenatal Exposure Delayed Effects , Adrenal Cortex Hormones/administration & dosage , Adult , Body Mass Index , Case-Control Studies , Cephalometry , Child , Cohort Studies , Female , Humans , Hydrocortisone/analysis , Pregnancy , Retrospective Studies , Saliva/chemistryABSTRACT
Artificial Neural Networks (ANNs) have been used in identifying the risk factors for many medical outcomes. In this paper, the risk factors for low Apgar score are introduced. This is the first time, to our knowledge, that the ANNs are used for Apgar score prediction. The medical domain of interest used is the perinatal database provided by the Perinatal Partnership Program of Eastern and Southeastern Ontario (PPPESO). The ability of the feed forward back propagation ANNs to generate strong predictive model with the most influential variables is tested. Finally, minimal sets of variables (risk factors) that are important in predicting Apgar score outcome without degrading the ANN performance are identified.
Subject(s)
Neural Networks, Computer , Algorithms , Apgar Score , Artificial Intelligence , Automation , Databases, Factual , Humans , Infant, Newborn , Models, Statistical , Ontario , Outcome Assessment, Health Care , Pattern Recognition, Automated , Pediatrics/methods , Risk Factors , SoftwareABSTRACT
This research is built on the belief that artificial intelligence estimations need to be integrated into clinical social context to create value for health-care decisions. In sophisticated neonatal intensive care units (NICUs), decisions to continue or discontinue aggressive treatment are an integral part of clinical practice. High-quality evidence supports clinical decision-making, and a decision-aid tool based on specific outcome information for individual NICU patients will provide significant support for parents and caregivers in making difficult "ethical" treatment decisions. In our approach, information on a newborn patient's likely outcomes is integrated with the physician's interpretation and parents' perspectives into codified knowledge. Context-sensitive content adaptation delivers personalized and customized information to a variety of users, from physicians to parents. The system provides structuralized knowledge translation and exchange between all participants in the decision, facilitating collaborative decision-making that involves parents at every stage on whether to initiate, continue, limit, or terminate intensive care for their infant.
