The National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC) lacks a rigorous enrollment audit process, unlike other collaborative networks. Most centers require individual families to consent to participate. It is unknown whether there is variation across centers or biases in enrollment.

We used the Pediatric Cardiac Critical Care Consortium (PC4) registry to assess enrollment rates in NPC-QIC for those centers participating in both registries using indirect identifiers (date of birth, date of admission, gender, and center) to match patient records. All infants born 1/1/2018–12/31/2020 and admitted 30 days of life were eligible. In PC4, all infants with a fundamental diagnosis of hypoplastic left heart or variant or who underwent a surgical or hybrid Norwood or variant were eligible. Standard descriptive statistics were used to describe the cohort and center match rates were plotted on a funnel chart.

Of 898 eligible NPC-QIC patients, 841 were linked to 1,114 eligible PC4 patients (match rate 75.5%) in 32 centers. Match rates were lower in patients of Hispanic/Latino ethnicity (66.1%, p = 0.005), and those with any specified chromosomal abnormality (57.4%, p = 0.002), noncardiac abnormality (67.8%, p = 0.005), or any specified syndrome (66.5%, p = 0.001). Match rates were lower for patients who transferred to another hospital or died prior to discharge. Match rates varied from 0 to 100% across centers.

It is feasible to match patients between the NPC-QIC and PC4 registries. Variation in match rates suggests opportunities for improvement in NPC-QIC patient enrollment.

Family-centred rounds benefit families and clinicians and improve outcomes in general paediatrics, but are understudied in subspecialty settings. We sought to improve family presence and participation in rounds in a paediatric acute care cardiology unit.

We created operational definitions for family presence, our process measure, and participation, our outcome measure, and gathered baseline data over 4 months of 2021. Our SMART aim was to increase mean family presence from 43 to 75% and mean family participation from 81 to 90% by 30 May, 2022. We tested interventions with iterative plan-do-study-act cycles between 6 January, 2022 and 20 May, 2022, including provider education, calling families not at bedside, and adjustment to rounding presentations. We visualised change over time relative to interventions with statistical control charts. We conducted a high census days subanalysis. Length of stay and time of transfer from the ICU served as balancing measures.

Mean presence increased from 43 to 83%, demonstrating special cause variation twice. Mean participation increased from 81 to 96%, demonstrating special cause variation once. Mean presence and participation were lower during high census (61 and 93% at project end) but improved with special cause variation. Length of stay and time of transfer remained stable.

Through our interventions, family presence and participation in rounds improved without apparent unintended consequences. Family presence and participation may improve family and staff experience and outcomes; future research is warranted to evaluate this. Development of high level of reliability interventions may further improve family presence and participation, particularly on high census days.

Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease.

This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve.

Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved.

Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.

The aim of the present study was to explore and compare the association between a new vasoactive score – the Total Inotrope Exposure Score – and outcome and the established Vasoactive Inotrope Score in children undergoing cardiac surgery with cardiopulmonary bypass

The present study was a single-centre, retrospective study.

The study was carried out at a 21-bed cardiovascular ICU in a Tertiary Children’s Hospital between September, 2010 and May, 2011

The Total Inotrope Exposure Score is a new vasoactive score that brings together cumulative vasoactive drug exposure and incorporates dose adjustments over time. The performance of these scores – average, maximum Vasoactive Inotrope Score at 24 and 48 hours, and Total Inotrope Exposure Score – to predict primary clinical outcomes – either death, cardiopulmonary resuscitation, or extra-corporeal membrane oxygenation before hospital discharge – and secondary outcomes – length of invasive mechanical ventilation, length of ICU stay, and hospital stay – was calculated.

The study cohort included 167 children under 18 years of age, with 37 (22.2%) neonates and 65 (41.3%) infants aged between 1 month and 1 year. The Total Inotrope Exposure Score best predicted the primary outcome (six of 167 cases) with an unadjusted odds ratio for a poor outcome of 42 (4.8, 369.6). Although the area under curve was higher than other scores, this difference did not reach statistical significance. The Total Inotrope Exposure Score best predicted prolonged invasive mechanical ventilation, length of ICU stay, and hospital stay as compared with the other scores.

The Total Inotrope Exposure Score appears to have a good association with poor postoperative outcomes and warrants prospective validation across larger numbers of patients across institutions.

With improvements in early survival following congenital heart surgery, it has become increasingly important to understand longer-term outcomes; however, routine collection of these data is challenging and remains very limited. We describe the development and initial results of a collaborative programme incorporating standardised longitudinal follow-up into usual care at the Children’s Hospital of Philadelphia (CHOP) and University of Michigan (UM).

We included children undergoing benchmark operations of the Society of Thoracic Surgeons. Considerations regarding personnel, patient/parent engagement, funding, regulatory issues, and annual data collection are described, and initial follow-up rates are reported.

The present analysis included 1737 eligible patients undergoing surgery at CHOP from January 2007 to December 2014 and 887 UM patients from January 2010 to December 2014. Overall, follow-up data, of any type, were obtained from 90.8% of patients at CHOP (median follow-up 4.3 years, 92.2% survival) and 98.3% at UM (median follow-up 2.8 years, 92.7% survival), with similar rates across operations and institutions. Most patients lost to follow-up at CHOP had undergone surgery before 2010. Standardised questionnaires assessing burden of disease/quality of life were completed by 80.2% (CHOP) and 78.4% (UM) via phone follow-up. In subsequent pilot testing of an automated e-mail system, 53.4% of eligible patients completed the follow-up questionnaire through this system.

Standardised follow-up data can be obtained on the majority of children undergoing benchmark operations. Ongoing efforts to support automated electronic systems and integration with registry data may reduce resource needs, facilitate expansion across centres, and support multi-centre efforts to understand and improve long-term outcomes in this population.