Developmental care pathway for hospitalised infants with CHD: on behalf of the Cardiac Newborn Neuroprotective Network, a Special Interest Group of the Cardiac Neurodevelopmental Outcome Collaborative.

Infants and children born with CHD are at significant risk for neurodevelopmental delays and abnormalities. Individualised developmental care is widely recognised as best practice to support early neurodevelopment for medically fragile infants born premature or requiring surgical intervention after birth. However, wide variability in clinical practice is consistently demonstrated in units caring for infants with CHD. The Cardiac Newborn Neuroprotective Network, a Special Interest Group of the Cardiac Neurodevelopmental Outcome Collaborative, formed a working group of experts to create an evidence-based developmental care pathway to guide clinical practice in hospital settings caring for infants with CHD. The clinical pathway, "Developmental Care Pathway for Hospitalized Infants with Congenital Heart Disease," includes recommendations for standardised developmental assessment, parent mental health screening, and the implementation of a daily developmental care bundle, which incorporates individualised assessments and interventions tailored to meet the needs of this unique infant population and their families. Hospitals caring for infants with CHD are encouraged to adopt this developmental care pathway and track metrics and outcomes using a quality improvement framework.

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.

Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.

Quality Over Quantity: Standardization of Pediatric HeartWare Ventricular Assist Device Dressing Changes.

Pediatric patients are unique both in their diagnosis and clinical presentation before implantation of a ventricular assist device (VAD) and in their driveline site characteristics post-implant. There is limited evidence in scholarly literature that describes complications of pediatric VAD driveline sites or approaches by which to manage them. The Cardiac Center at The Children's Hospital of Philadelphia (CHOP) follows a standard of care for HeartWare VAD (HVAD) dressing changes in the inpatient setting with the goal of transitioning patients to weekly dressing changes by the time they are discharged to home. As a patient with an HVAD nears discharge, members of an interprofessional team collaborate with insurance providers and home care agencies to procure the appropriate supplies needed at home. Individualized plans of care are necessary for patients who are unable to transition to weekly dressings; however, customized products (such as silicone foam border dressings and antimicrobial agents) may be challenging to supply as single items from home care agencies. Between March 2014 and June 2017, 15 patients underwent HVAD implantation, and eight (53%) were discharged home. Ten patients (67%) were able to transition to weekly dressing changes. Individualized plans of care for driveline site management were required for six (40%) patients with persistent drainage. Three patients (20%) experienced a driveline site infection. This article describes how a quality improvement (QI) initiative using rapid-cycle improvement methodology was executed to standardize HVAD dressing changes in our pediatric population.