Assessment of the CLOT (children's likelihood of thrombosis) real-time risk prediction model of hospital-associated venous thromboembolism in children with congenital heart disease.

BACKGROUND: Children with congenital heart disease (CHD) are at high risk for hospital-associated venous thromboembolism (HA-VTE). The children's likelihood of thrombosis (CLOT) trial validated a real-time predictive model for HA-VTE using data extracted from the EHR for pediatric inpatients. We tested the hypothesis that addition of CHD specific data would improve model prediction in the CHD population. METHODS: Model performance in CHD patients from 2010 to 2022, was assessed using 3 iterations of the CLOT model: 1) the original CLOT model, 2) the original model refit using only data from the CHD cohort, and 3) the model updated with the addition of cardiopulmonary bypass time, STAT Mortality Category, height, and weight as covariates. The discrimination of the three models was quantified and compared using AUROC. RESULTS: Our CHD cohort included 1457 patient encounters (median 2.0 IQR [0.5-5.2] years-old). HA-VTE was present in 5% of our CHD cohort versus 1% in the general pediatric population. Several features from the original model were associated with thrombosis in the CHD cohort including younger age, thrombosis history, infectious disease consultation, and EHR coding of a central venous line. Lower height and weight were associated with thrombosis. HA-VTE rate was 12% (18/149) amongst those with STAT Category 4-5 operation versus 4% (49/1256) with STAT Category 1-3 operation (P < .001). Longer cardiopulmonary bypass time (124 [92-205] vs. 94 [65-136] minutes, P < .001) was associated with thrombosis. The AUROC for the original (0.80 95% CI [0.75-0.85]), refit (0.85 [0.81-0.89]), and updated (0.86 [0.81-0.90]) models demonstrated excellent discriminatory ability within the CHD cohort. CONCLUSION: The automated approach with EHR data extraction makes the applicability of such models appealing for ease of clinical use. The addition of cardiac specific features improved model discrimination; however, this benefit was marginal compared to refitting the original model to the CHD cohort. This suggests strong predictive generalized models, such as CLOT, can be optimized for cohort subsets without additional data extraction, thus reducing cost of model development and deployment.

Comprehensive cardiac magnetic resonance T1, T2, and extracellular volume mapping to define Duchenne cardiomyopathy.

BACKGROUND: Cardiomyopathy is the leading cause of death in Duchenne muscular dystrophy (DMD). Cardiac magnetic resonance (CMR) parametric mapping sequences offer insights into disease pathophysiology. We propose a novel approach by leveraging T2 mapping in conjunction with T1 and extracellular volume (ECV) mapping to perform a virtual myocardial biopsy. While previous work has attempted to describe myocardial changes in DMD, our inclusion of T2 mapping enables comprehensive categorization of myocardial tissue characteristics of fibrosis, edema, and fat to better understand the pathological composition of the myocardium with disease progression. METHODS: DMD patients (n = 49; median: 12 years-old) underwent CMR, including T1, T2, and ECV. Categories were defined as normal, isolated high T1 (normal ECV, high T1, normal T2), fibrosis (high ECV, normal or high T1, normal T2), edema (normal or high ECV, normal or high T1, high T2), fat (normal ECV, low T1, high T2) or fibrofatty (high ECV, low T1, high T2). RESULTS: Median left ventricular ejection fraction (LVEF) was 59% with 27% having LVEF < 55%. Those with normal LVEF and no late gadolinium enhancement (37%) were younger in age (10.5 ± 2.6 vs. 15.0 ± 4.3 years-old, p < 0.001). Native T1 was elevated in at least one slice in 82% of patients. Those with high T2 at any slice (27%) were older (p = 0.005) and had lower LVEF (p = 0.005) compared with subjects with normal T2 (73%). The most common myocardial characterization was fibrosis (43%) followed by isolated high T1 (24%). Of the 13 with high T2, ten were categorized as edema, two as fibrofatty, and one as fat. CONCLUSION: CMR parametric mapping sequences offer insights into Duchenne cardiomyopathy pathophysiology, which should drive development of therapeutic interventions aimed at these targets. Myocardial fibrosis is common in DMD. Patients with elevated T2 were older and had lower LVEF. Though fat infiltration was present, the majority of subjects with elevated T2 met criteria for myocardial edema.

Association of cardiovascular magnetic resonance diastolic indices with arrhythmia in repaired Tetralogy of Fallot.

BACKGROUND: Patients with repaired Tetralogy of Fallot (rTOF) experience a high burden of long-term morbidity, particularly arrhythmias. Cardiovascular magnetic resonance (CMR) is routinely used to assess ventricular characteristics but the relationship between CMR diastolic function and arrhythmia has not been evaluated. We hypothesized in rTOF, left ventricular (LV) diastolic dysfunction on CMR would correlate with arrhythmias and mortality. METHODS: Adolescents and adults with rTOF who underwent CMR were compared to healthy controls (n = 58). Standard ventricular parameters were assessed and manual planimetry was performed to generate filling curves and indices of diastolic function. Chart review was performed to collect outcomes. Univariate and multivariable logistic regression was performed to identify outcome associations. RESULTS: One-hundred sixty-seven subjects with rTOF (mean age 32 years) and 58 healthy control subjects underwent CMR. Patients with rTOF had decreased LV volumes and increased right ventricular (RV) volumes, lower RV ejection fraction (RVEF), lower peak ejection rate (PER), peak filling rate (PFR) and PFR indexed to end-diastolic volume (PFR/EDV) compared to healthy controls. Eighty-three subjects with rTOF had arrhythmia (63 atrial, 47 ventricular) and 11 died. Left atrial (LA) volumes, time to peak filling rate (tPFR), and PFR/EDV were associated with arrhythmia on univariate analysis. PER/EDV was associated with ventricular (Odds ratio, OR 0.43 [0.24-0.80], p = 0.007) and total arrhythmia (OR 0.56 [0.37-0.92], p = 0.021) burden. A multivariable predictive model including diastolic covariates showed improved prediction for arrhythmia compared to clinical and conventional CMR measures (area under curve (AUC) 0.749 v. 0.685 for overall arrhythmia). PFR/EDV was decreased and tPFR was increased in rTOF subjects with mortality as compared to those without mortality. CONCLUSIONS: Subjects with rTOF have abnormal LV diastolic function compared to healthy controls. Indices of LV diastolic function were associated with arrhythmia and mortality. CMR diastolic indices may be helpful in risk stratification for arrhythmia.

Machine Learning to Predict Interstage Mortality Following Single Ventricle Palliation: A NPC-QIC Database Analysis.

There is high risk of mortality between stage I and stage II palliation of single ventricle heart disease. This study aimed to leverage advanced machine learning algorithms to optimize risk-prediction models and identify features most predictive of interstage mortality. This study utilized retrospective data from the National Pediatric Cardiology Quality Improvement Collaborative and included all patients who underwent stage I palliation and survived to hospital discharge (2008-2019). Multiple machine learning models were evaluated, including logistic regression, random forest, gradient boosting trees, extreme gradient boost trees, and light gradient boosting machines. A total of 3267 patients were included with 208 (6.4%) interstage deaths. Machine learning models were trained on 180 clinical features. Digoxin use at discharge was the most influential factor resulting in a lower risk of interstage mortality (p < 0.0001). Stage I surgery with Blalock-Taussig-Thomas shunt portended higher risk than Sano conduit (7.8% vs 4.4%, p = 0.0002). Non-modifiable risk factors identified with increased risk of interstage mortality included female sex, lower gestational age, and lower birth weight. Post-operative risk factors included the requirement of unplanned catheterization and more severe atrioventricular valve insufficiency at discharge. Light gradient boosting machines demonstrated the best performance with an area under the receiver operative characteristic curve of 0.642. Advanced machine learning algorithms highlight a number of modifiable and non-modifiable risk factors for interstage mortality following stage I palliation. However, model performance remains modest, suggesting the presence of unmeasured confounders that contribute to interstage risk.

Influence of CYP2D6 genetic variation on adverse events with propafenone in the pediatric and young adult population.

Propafenone is an antiarrhythmic drug metabolized primarily by cytochrome P450 2D6 (CYP2D6). In adults, propafenone adverse events (AEs) are associated with CYP2D6 poor metabolizer status; however, pediatric data are lacking. Subjects were tested for 10 CYP2D6 allelic variants and copy number status, and activity scores assigned to each genotype. Seventy-six individuals (median 0.3 [range 0-26] years old) were included. Propafenone AEs occurred in 29 (38%); 14 (18%) required drug discontinuation due to AE. The most common AEs were QRS (n = 10) and QTc (n = 6) prolongation. Those with AEs were older at the time of propafenone initiation (1.58 [0.13-9.92] vs. 0.20 [0.08-2.01] years old; p = 0.042). CYP2D6 activity scores were not associated with presence of an AE (odds ratio [OR] 0.48 [0.22-1.03]; p = 0.055) but with the total number of AE (ß1  = -0.31 [-0.60, -0.03]; p = 0.029), systemic AEs (OR 0.33 [0.13-0.88]; p = 0.022), and drug discontinuation for systemic AEs (OR 0.28 [0.09-0.83]; p = 0.017). Awareness of CYP2D6 activity score and patient age may aid in determining an individual's risk for an AE with propafenone administration.