Development of a Pediatric Vascular Catheterization Complication Score (Ped-VCCScore) for predicting post-cardiac catheterization complications

Abstract

Cardiac catheterization, which is essential for clinical diagnosis and treatment, carries certain risks in pediatric patients, including complications such as loss of pulse, internal bleeding, vessel rupture, and subcutaneous hematoma. We investigated vascular complications in pediatric cardiac patients undergoing catheterization and developed a scoring system to predict these risks. We investigated pediatric patients aged <15 years who underwent cardiac catheterization at a tertiary hospital between January 2017 and December 2019 and developed a statistical model identifying the key factors influencing the risk of complications based on complication frequency, patient demographics, and treatment types. The identified key factors were body weight, procedure type, and maximum sheath size in the arterial-side-to-body-weight ratio. Using the scores determined by the model, participants were categorized into low-, intermediate-, and high-risk groups. The effectiveness of the model was assessed based on accuracy, alignment with real outcomes, and the ability to distinguish between cases with and without complications. Of the 390 patients, 6.2% experienced complications after cardiac catheterization. Transient pulse loss was the predominant complication (72%), followed by subcutaneous hematoma (12%) and bleeding (16%). In the development dataset, the vascular complication rates were 1.8%, 6.8%, and 26.1% in the low-, intermediate-, and high-risk groups, respectively. The likelihood ratios for vascular complications in the low-, intermediate-, and high-risk groups were 0.27 (95% confidence interval [CI]: 0.07, 0.81; P=0.014), 1.12 (95% CI: 0.42, 2.67; P=0.828), and 5.38 (95% CI: 2.23, 12.33; P<0.001), respectively. Our model based on body weight, procedure type, and sheath size-to-body weight ratio accurately predicted vascular complications in pediatric cardiac catheterization. As one of the first studies to identify these risk factors, this study highlights the model’s potential applicability to support risk stratification-based clinical decision-making. Further validation in diverse clinical settings is needed to confirm its generalizability and predictive performance.