Resident Physician Connecticut Children's Medical Center Hartford, Connecticut, United States
Background: Umbilical vessel cannulation is a common procedure performed after delivery in neonates with critical congenital heart disease (CHD) to obtain central access for frequent monitoring, medication administration, and early surgical and catheter-based interventions. Failure rates of umbilical vessel cannulation are high and negatively impact perioperative monitoring and treatment in neonates with CHD. More advanced techniques have improved upon success rates, including fluoroscopy-guided and ultrasound-guided techniques. There is a paucity of research in the published literature evaluating risk factors that contribute to unsuccessful bedside placement of umbilical lines in CHD patients. Risk factor analysis would better identify patients that would benefit most from the use of advanced techniques to improve success rates in umbilical vessel cannulation. Objective: In this study, we aim to identify risk factors for unsuccessful bedside placement of umbilical lines in neonates with and without CHD. The results of this review will help inform the creation of a clinical pathway to guide the techniques used for umbilical vessel cannulation in neonates with CHD to maximize safe, reliable central access in this patient population. Design/Methods: This study is a single-center retrospective chart review approved by CT Children’s Scientific Review Committee and IRB. We collected demographic data and patient characteristics to delineate factors that may predict procedural success. Descriptive statistics will be used to describe the demographics of the CHD and non-CHD patient populations. Differences for specific explanatory variables between the two groups will be assessed using independent t-tests or Mann-Whitney U test for continuous variables and chi-square test or Fisher’s exact test for categorical variables. Those variables found to be significant in the bivariate tests will be used to develop an overall predictive logistic regression model(s) for procedural failure. Data collection is complete, and we anticipate data analysis and interpretation by early 2024.
