Contemporary Care and Outcomes of Critically-ill Children With Clinically Diagnosed Myocarditis.

PURPOSE: To describe contemporary management and outcomes in children with myocarditis who are admitted to a cardiac intensive care unit (CICU) and to identify the characteristics associated with mortality. METHODS: All patients in the Pediatric Cardiac Critical Care Consortium (PC4) registry between August 2014 and June 2021 who were diagnosed with myocarditis were included. Univariable analyses and multivariable logistic regression evaluated the factors associated with in-hospital mortality. RESULTS: There were 847 CICU admissions for myocarditis in 51 centers. The median age was 12 years (IQR 2.7-16). In-hospital mortality occurred in 53 patients (6.3%), and 60 (7.1%) had cardiac arrest during admission. Mechanical ventilation was required in 339 patients (40%), and mechanical circulatory support (MCS) in 177 (21%); extracorporeal membrane oxygenation (ECMO)-only in 142 (16.7%), ECMO-to-ventricular assist device (VAD) in 20 (2.4%), extracorporeal cardiac resuscitation in 43 (5%), and VAD-only in 15 (1.8%) patients. MCS was associated with in-hospital mortality; 20.3% receiving MCS died compared to 2.5% without MCS (P < 0.001). Mortality rates were similar in ECMO-only, ECMO-to-VAD and VAD-only groups. The median time from CICU admission to ECMO was 2.0 hours (IQR 0-9.4) and to VAD, it was 9.9 days (IQR 6.3-16.8). Time to MCS was not associated with mortality. In multivariable modeling of patients' characteristics, smaller body surface area (BSA) and low eGFR were independently associated with mortality, and after including critical therapies, mechanical ventilation and ECMO were independent predictors of mortality. CONCLUSION: This contemporary cohort of children admitted to CICUs with myocarditis commonly received high-resource therapies; however, most patients survived to hospital discharge and rarely received VAD. Smaller patient size, acute kidney injury and receipt of mechanical ventilation or ECMO were independently associated with mortality.

Assessing enrollment of eligible infants in the national pediatric cardiology quality improvement collaborative (NPC-QIC) through linkage to the pediatric cardiac critical care consortium (PC4) registry.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Fetal growth and gestational age improve outcome predictions in neonatal heart surgery.

OBJECTIVE: Current risk adjustment models for congenital heart surgery do not fully incorporate multiple factors unique to neonates such as granular gestational age (GA) and birth weight (BW) z score data. This study sought to develop a Neonatal Risk Adjustment Model for congenital heart surgery to address these deficiencies. METHODS: Cohort study of neonates undergoing cardiothoracic surgery during the neonatal period captured in the Pediatric Cardiac Critical Care Consortium database between 2014 and 2020. Candidate predictors were included in the model if they were associated with mortality in the univariate analyses. GA and BW z score were both added as multicategory variables. Mortality probabilities were predicted for different GA and BW z scores while keeping all other variables at their mean value. RESULTS: The C statistic for the mortality model was 0.8097 (95% confidence interval, 0.7942-0.8255) with excellent calibration. Mortality prediction for a neonate at 40 weeks GA and a BW z score 0 to 1 was 3.5% versus 9.8% for the same neonate at 37 weeks GA and a BW z score -2 to -1. For preterm infants the mortality prediction at 34 to 36 weeks with a BW z score 0 to 1 was 10.6%, whereas it was 36.1% for the same infant at <32 weeks with a BW z score of -2 to -1. CONCLUSIONS: This Neonatal Risk Adjustment Model incorporates more granular data on GA and adds the novel risk factor BW z score. These 2 factors refine mortality predictions compared with traditional risk models. It may be used to compare outcomes across centers for the neonatal population.