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PURPOSE Kidney failure is a rare but serious late effect following treatment for childhood cancer. We developed a model using demographic and treatment characteristics to predict individual risk of kidney failure among 5-year survivors of childhood cancer. METHODS Five-year survivors from the Childhood Cancer Survivor Study (CCSS) without history of kidney failure (n = 25,483) were assessed for subsequent kidney failure (ie, dialysis, kidney transplantation, or kidney-related death) by age 40 years. Outcomes were identified by self-report and linkage with the Organ Procurement and Transplantation Network and the National Death Index. A sibling cohort (n = 5,045) served as a comparator. Piecewise exponential models accounting for race/ethnicity, age at diagnosis, nephrectomy, chemotherapy, radiotherapy, congenital genitourinary anomalies, and early-onset hypertension estimated the relationships between potential predictors and kidney failure, using area under the curve (AUC) and concordance (C) statistic to evaluate predictive power. Regression coefficient estimates were converted to integer risk scores. The St Jude Lifetime Cohort Study and the National Wilms Tumor Study served as validation cohorts. RESULTS Among CCSS survivors, 204 developed late kidney failure. Prediction models achieved an AUC of 0.65-0.67 and a C-statistic of 0.68-0.69 for kidney failure by age 40 years. Validation cohort AUC and C-statistics were 0.88/0.88 for the St Jude Lifetime Cohort Study (n = 8) and 0.67/0.64 for the National Wilms Tumor Study (n = 91). Risk scores were collapsed to form statistically distinct low- (n = 17,762), moderate- (n = 3,784), and high-risk (n = 716) groups, corresponding to cumulative incidences in CCSS of kidney failure by age 40 years of 0.6% (95% CI, 0.4 to 0.7), 2.1% (95% CI, 1.5 to 2.9), and 7.5% (95% CI, 4.3 to 11.6), respectively, compared with 0.2% (95% CI, 0.1 to 0.5) among siblings. CONCLUSION Prediction models accurately identify childhood cancer survivors at low, moderate, and high risk for late kidney failure and may inform screening and interventional strategies.

BACKGROUNDThe use of traditional models to predict heart failure (HF) has limitations in preventing HF hospitalizations. Artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine only have limited data published regarding HF populations, with none assessing the favorability of decongestive therapy aquapheresis (AQ). AI and ML can be leveraged to design non-traditional models to identify those who are at high risk of HF readmissions.OBJECTIVESThis study aimed to develop a model for pretreatment identification of risk for 90-day HF events among HF patients who have undergone AQ.METHODSUsing data from the AVOID-HF (Aquapheresis versus Intravenous Diuretics and Hospitalization for Heart Failure) trial, we designed a ML-based predictive model that can be used before initiating AQ to anticipate who will respond well to AQ and who will be at high risk of future HF events.RESULTSUsing ML we identified the top ten predictors for 90-day HF events. Interestingly, the variable for ‘intimate relationships with loved ones’ strongly predicted response to therapy. This ML-model was more successful in predicting the outcome in HF patients who were treated with AQ. In the original AVOID-HF trial, the overall 90-day HF event rate in the AQ arm was 32%. Our proposed predictive model was accurate in anticipating 90-day HF events with better statistical accuracy (area under curve 0.88, sensitivity 80%, specificity 75%, negative predictive value 90%, and positive predictive value 57%).CONCLUSIONSML can help identify HF patients who will respond to AQ therapy. Our model can identify super-respondents to AQ therapy and predict 90-day HF events better than currently existing traditional models.CONDENSED ABSTRACTUtilizing data from the AVOID-HF trial, we designed a ML-predictive model that can be used before initiating AQ to anticipate who will respond well to AQ and who will be at high risk of future HF events. Using ML, we identified the top 10 predictors for 90-day HF events. Our model can identify super-respondents to ultrafiltration therapy and predict 90-day HF events better than currently existing traditional models.

Background Adult survivors of childhood cancer are at increased risk of cardiac late effects. Methods Using whole-genome sequencing data from 1870 survivors of European ancestry in the St. Jude Lifetime Cohort (SJLIFE) study, genetic variants were examined for association with ejection fraction (EF) and clinically assessed cancer therapy–induced cardiac dysfunction (CCD). Statistically significant findings were validated in 301 SJLIFE survivors of African ancestry and 4020 survivors of European ancestry from the Childhood Cancer Survivor Study. All statistical tests were 2-sided. Results A variant near KCNK17 showed genome-wide significant association with EF (rs2815063-A: EF reduction = 1.6%; P = 2.1 × 10-8) in SJLIFE survivors of European ancestry, which replicated in SJLIFE survivors of African ancestry (EF reduction = 1.5%; P = .004). The rs2815063-A also showed a 1.80-fold (P = .008) risk of severe or disabling or life-threatening CCD and replicated in 4020 Childhood Cancer Survivor Study survivors of European ancestry (odds ratio = 1.40; P = .04). Notably, rs2815063-A was specifically associated among survivors exposed to doxorubicin only, with a stronger effect on EF (3.3% EF reduction) and CCD (2.97-fold). Whole blood DNA methylation data in 1651 SJLIFE survivors of European ancestry showed statistically significant correlation of rs2815063-A with dysregulation of KCNK17 enhancers (false discovery rate Conclusions Leveraging the 2 largest cohorts of childhood cancer survivors in North America and survivor-specific polygenomic functional data, we identified a novel risk locus for CCD, which showed specificity with doxorubicin-induced cardiac dysfunction and highlighted dysregulation of KCNK17 as the likely molecular mechanism underlying this genetic association.

Adverse cardiovascular outcomes such as coronary artery disease (CAD) are the leading noncancer causes of morbidity and mortality among childhood cancer survivors.The aim of this study was to assess the role of a genome-wide polygenic score (GPS) for CAD, well validated in the general population, and its interplay with cancer-related risk factors among childhood cancer survivors.In a cohort study of 2,472 5-year childhood cancer survivors from the St. Jude Lifetime Cohort, the association between the GPS and the risk of CAD was performed using Cox regression models adjusted for age at cancer diagnosis, sex, cumulative dose of anthracyclines, and mean heart radiation dose.Among survivors of European ancestry, the GPS was significantly associated with the risk of CAD (HR per 1 SD of the GPS: 1.25; 95% CI: 1.04-1.49;Childhood cancer survivors are at risk for premature CAD. A GPS may help identify those who may benefit from targeted screening and personalized preventive interventions.

Supplementary Data

Cardiomyopathy occurs at significantly higher rates in survivors of childhood cancer than the general population, but few studies have evaluated racial or ethnic disparities, and none have assessed potential genetic factors contributing to this outcome. In this study, childhood cancer survivors of African ancestry exposed to cardiotoxic therapies (anthracyclines and/or heart radiotherapy; n = 246) were compared with cardiotoxic-exposed survivors of European ancestry (n = 1,645) in the St. Jude Lifetime Cohort. Genetic variants were examined using whole-genome sequencing data among survivors of African ancestry, first based on ejection fraction (EF) as a continuous outcome, followed by clinical history of cardiomyopathy. Survivors of African ancestry showed 1.53- and 2.47-fold risks of CTCAE grade 2–4 and grade 3–4 cardiomyopathy than survivors of European ancestry. A novel locus at 1p13.2 showed significant association with EF (rs6689879*C: EF reduction = 4.2%; P = 2.8 × 10−8) in 246 survivors of African ancestry, which was successfully replicated in 1,645 survivors of European ancestry but with attenuated magnitude (EF reduction = 0.4%; P = 0.042). In survivors of African ancestry, rs6689879*C showed a 5.43-fold risk of cardiomyopathy and 1.31-fold risk in those of European ancestry. Among survivors of African ancestry with rs6689879*C and CTCAE grade 2–4 cardiomyopathy, the PHTF1 promoter region was hypomethylated. Similar results were observed in survivors of European ancestry, albeit with reduced magnitudes of hypomethylation among those with rs6689879*C and CTCAE grade 2–4 cardiomyopathy. PHTF1 was upregulated in human-induced pluripotent stem cell-derived cardiomyocytes from patients with doxorubicin-induced cardiomyopathy. These findings have potential implications for long-term cardiac surveillance and up-front cancer care for patients of African ancestry.Significance:Childhood cancer survivors of African ancestry are at higher risk of cardiomyopathy than those of European ancestry, and a novel locus at 1p13.2 is associated with therapy-related cardiomyopathy specifically in African-American survivors.See related commentary by Brown and Richard, p. 2272

Background The prevalence of diastolic dysfunction has not been systematically evaluated in a large population of survivors of childhood cancer using established guidelines and standards. Objectives This study sought to assess the prevalence and progression of diastolic dysfunction in adult survivors of childhood cancer exposed to cardiotoxic therapy. Methods Comprehensive, longitudinal echocardiographic examinations of adult survivors of childhood cancer≥18 years of age and≥10 years from diagnosis in SJLIFE (St. Jude Lifetime Cohort Study) were performed. Diastolic dysfunction was defined based on 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging guidelines. Results Among 3,342 survivors, the median (25th-75th percentiles [quartile (Q)1-Q3]) age at diagnosis was 8.1 years (Q1-Q3: 3.6-13.7), 30.1 years (Q1-Q3: 24.4-37.0) at the baseline echocardiography evaluation (Echo 1), and 36.6 years (Q1-Q3: 30.8-43.6) at the last follow-up echocardiography evaluation (1,435 survivors) (Echo 2). The proportion of diastolic dysfunction was 15.2% (95% CI: 14.0%-16.4%) at Echo 1 and 15.7% (95% CI: 13.9%-17.7%) at Echo 2, largely attributable to concurrent systolic dysfunction. Less than 5% of survivors with preserved ejection fraction had diastolic dysfunction (2.2% at Echo 1, 3.7% at Echo 2). Using global longitudinal strain assessment in adult survivors with preserved ejection fraction (defined with a cut point worse than−15.9%), the proportion of diastolic dysfunction increased to 9.2% at baseline and 9.0% at follow-up. Conclusions The prevalence of isolated diastolic dysfunction is low among adults who received cardiotoxic therapies for childhood cancer. The inclusion of left ventricular global longitudinal strain significantly increased the identification of diastolic dysfunction.

Background Clinical manifestations of classic Fabry disease (α-galactosidase A deficiency) usually occur in childhood, while complications involving major organs typically develop in adulthood. Outcomes of Fabry-specific treatment among young patients have not been extensively reported. Our aim was to analyze clinical outcomes among patients aged 5–30 years at initiation of treatment with agalsidase beta using data from the Fabry Registry (NCT00196742, sponsor: Sanofi). Methods Reported GLA variants were predicted to be associated with the classic phenotype or not classified in fabry-database.org. Linear mixed models were conducted to assess changes over ≥2-year follow-up in the estimated glomerular filtration rate (eGFR) stratified by low (LRI) and high (HRI) renal involvement (defined by proteinuria/albuminuria levels), and changes in interventricular septal thickness (IVST) and left ventricular posterior wall thickness (LVPWT) Z-scores stratified by median age at first treatment. Self-reports (‘yes’/‘no’) of abdominal pain, diarrhea, chronic peripheral pain (denoting neuropathic pain), and acute pain crises at baseline were compared with reports after ≥0.5-year and ≥2.5-year follow-up using McNemar's test. Results Male (n = 117) and female patients (n = 59) with LRI initiated treatment at a median age of 19.9 and 23.6 years, respectively, and were followed for a median of 6.3 and 5.0 years, respectively. The eGFR slopes were −1.18 (Pfrom 0 5.5 years and ≥4.5 years, respectively). Older females (treatment initiation at median age 27.5 years) had a slope of LVPWT Z-scores of 0.18/year (n = 12, Pfrom 0 = 0.028), whereas IVST Z-scores remained stable (n = 13, 0.10/year, Pfrom 0 = 0.304) during a median follow-up of ≥3.7 years. These slopes did not significantly differ from slopes of younger females. Reports of chronic peripheral pain and acute pain crises by males, and of diarrhea and acute pain crises by females, significantly reduced after a median follow-up of ≥4.0 years. After a median follow-up of ≥5.4 years, reports of all four symptoms significantly decreased among males, whereas among females only reports of abdominal pain significantly decreased. Conclusions During sustained treatment with agalsidase beta in young Fabry patients with a predicted classic phenotype or with unclassified GLA variants with similar characteristics, the decline in eGFR was modest among male and female patients with LRI. The greater decline in eGFR among older, proteinuric (i.e., HRI) males may suggest a benefit of earlier treatment. Overall, echocardiographic variables remained stable, particularly among males and younger females. Significant reductions in symptom reports occurred primarily among males after longer follow-up and were less noticeable among females. These observed trends are suggestive of an overall improvement after treatment in young patients, but warrant larger longitudinal studies. publishedVersion

PURPOSE Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial intelligence (AI) methods using the Children's Oncology Group guideline–recommended baseline ECG to predict cardiomyopathy. MATERIAL AND METHODS Seven AI and signal processing methods were applied to 10-second 12-lead ECGs obtained on 1,217 adult survivors of childhood cancer prospectively followed in the St Jude Lifetime Cohort (SJLIFE) study. Clinical and echocardiographic assessment of cardiac function was performed at initial and follow-up SJLIFE visits. Cardiomyopathy was defined as an ejection fraction < 50% or an absolute drop from baseline ≥ 10%. Genetic algorithm was used for feature selection, and extreme gradient boosting was applied to predict cardiomyopathy during the follow-up period. Model performance was evaluated by five-fold stratified cross-validation. RESULTS The median age at baseline SJLIFE evaluation was 31.7 years (range 18.4-66.4), and the time between baseline and follow-up evaluations was 5.2 years (0.5-9.5). Two thirds (67.1%) of patients were exposed to chest radiation, and 76.6% to anthracycline chemotherapy. One hundred seventeen (9.6%) patients developed cardiomyopathy during follow-up. In the model based solely on ECG features, the cross-validation area under the curve (AUC) was 0.87 (95% CI, 0.83 to 0.90), whereas the model based on clinical features had an AUC of 0.69 (95% CI, 0.64 to 0.74). In the model based on ECG and clinical features, the cross-validation AUC was 0.89 (95% CI, 0.86 to 0.91), with a sensitivity of 78% and a specificity of 81%. CONCLUSION AI using ECG data may assist in the identification of childhood cancer survivors at increased risk for developing future cardiomyopathy.

Barth Syndrome is an X-linked disorder of mitochondrial cardiolipin metabolism caused by pathogenic variants in TAFAZZIN with pleiotropic effects including cardiomyopathy, neutropenia, growth delay, and skeletal myopathy. Management requires a multidisciplinary approach to the organ-specific manifestations including specialists from cardiology, hematology, nutrition, physical therapy, genetics, and metabolism. Currently, treatment is centered on management of specific clinical features, and is not targeted towards remediating the underlying biochemical defect. However, two clinical trials have been recently undertaken which target the mitochondrial pathology of this disease: a study to examine the effects of elamipretide, a cardiolipin targeted agent, and a study to examine the effects of bezafibrate, a peroxisome proliferator-activated receptor (PPAR) agonist. Treatments to directly target the defective TAFAZZIN pathway are under development, including enzyme and gene therapies. This article is protected by copyright. All rights reserved.

Little is known about Electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before incidence of the disease. This retrospective case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Only data available at least 6 months before PD diagnosis was used as the model’s input. Data from LUC spanned back to May 2014 while that from MLH spanned to January 2015. PD was denoted by at least two primary ICD diagnostic codes, namely ICD9 332.0, ICD10 G20. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. Prediction of prodromal PD (6-months to 5-years preceding PD diagnosis) was the primary outcome of this research. Three time windows were set: 6 months-1year, 6months-3 years and 6months – 5 years. A novel deep neural network using standard 10-second 12-lead ECG was used to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for gender, race and age were also performed. A one-dimensional convolutional neural network (1D-CNN) was used to predict PD risk (or identify prodromal PD) from standard 10 second 12-lead ECGs collected between 6 months to 5 years before a clinical diagnosis. The prediction model was built using MLH data and externally validated on LUC data. 131 cases/1058 controls at MLH and 29 cases/165 controls at LUC were identified. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation of AUC = 0.67 when predicting prodromal PD at any time between 6 months and 5 years. The accuracy increased when using ECGs to predict prodromal PD within 6 months to 3 years, with an external validation AUC of 0.69 and achieving highest AUC when predicting PD within 1 year before onset (AUC of 0.74). A predictive model that can correctly classify individuals with prodromal PD was developed using only raw ECGs as inputs. The model was effective in predicting prodromal PD within an independent cohort, particularly closer to disease diagnosis. The ECG-based model outperformed multiple models built using ECG feature engineering. Subgroup analyses showed that some subgroups, including females and those of over 60 years of age, might benefit from closer monitoring, especially when symptoms start becoming more evident but not enough to make a diagnosis. This research highlights that standard ECGs may help identify individuals with prodromal PD for cost-effective early detection and inclusion in disease-modifying therapeutic trials.

Studies have shown poor correlation between intra-cardiac pressures and blood volume (BV) measurements including HF. The impact of sex and left ventricular ejection fraction (LVEF) on this relationship has not been studied. We obtained pressure (pulmonary artery diastolic pressure (PADP)) and volume (total blood volume (TBV) and estimated stress blood volume (eSBV)) measurements from HF patients at the time of CardioMEMS implantation. A total of 20 patients were included. There was no significant difference between PADP, TBV, and eSBV between sexes. There was only a moderate correlation between PADP and eSBV in men but not in women or with TBV in both sexes. HFrEF had higher PADP and eSBV than HFpEF. There was a consistent lack of correlation between PADP and both TBV and eSBV. Further studies evaluating mid- to long-term implications of pressure-volume profiles as well as changes following decongestion therapy are warranted to better understand the pressure-volume interplay and determine appropriate decongestion strategy for each pressure-volume phenotype.

Fabry disease (FD) is a rare, progressive, X-linked inherited disorder of glycosphingolipid metabolism. It is a monogenic disease due to α-galactosidase A (α-GAL) enzyme deficiency, leading to the accumulation of globotriaosylceramide (GL3) within lysosomes beginning in utero. Multiple systems are involved, most notably the vascular, renal, cardiac, and nervous systems. Early clinical manifestations include neuropathic pain, angiokeratomas, anhidrosis, cornea verticillata, and gastrointestinal symptoms. In the later stages, FD manifests with transient ischemic attacks, strokes, hearing loss, and life-threatening complications involving the kidneys and heart. Cardiac involvement in Fabry disease is typically characterized by increased left ventricular wall thickness/mass, functional abnormalities, valvular heart disease, arrhythmias, and heart failure. The life expectancy of the patient with untreated Fabry disease falls significantly once cardiac or renal manifestations develop. This review will focus on the cardiac manifestations of FD and the role of multimodality imaging in diagnosis and follow-up.

Autonomic nervous system pathology manifests early in Parkinson’s disease (PD) course. Although heart rate variability measured by a 5-minute electrocardiogram (ECG) is reported to be reduced in PD, little is known about ECG markers during prodromal stage, and brief 10-second ECGs have been rarely studied. The aim of the study is to build externally validated ECG based fully automatic artificial intelligence (AI) model to predict PD risk during prodromal stage. We obtained data for PD and date/age/sex/race-matched control subjects from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). We used AI to predict PD risk from standard 10-second 12-lead ECGs performed between 6-months-5 years before diagnosis. The prediction model was built with MLH data and externally validated with LUC data. We identified 131 cases/1058 controls at MLH and 29 cases/165 controls at LUC. We initially trained models on 90% of the MLH data and internally validated them in the remaining 10%. The best performing model with an internal validation was a convolutional neural network model. External validation with LUC data yielded an AUC of 0.67. When we considered only ECGs obtained 6-months to 1-year preceding PD diagnosis, the external validation AUC was 0.74. Using only ECG, we built a predictive model that correctly classified individuals with prodromal PD with moderate accuracy. The model was effective in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help to identify individuals with prodromal PD for inclusion in disease-modifying therapeutic trials.

Objectives: Applying machine learning to predict the 10-year cardiomyopathy risk among adult survivors of childhood cancer. Methods: The St. Jude Lifetime Cohort Study (SJLIFE) is an ongoing study of adult survivors of childhood cancer with in-person clinical evaluations. ECG and ECHO data were obtained on participants who did not have cardiomyopathy (defined as left ventricular ejection fraction < 50% or absolute drop from baseline ≥ 10%) at initial SJLIFE evaluation and who had at least two evaluations with ECHO screening. Predictors including ECG-AI based risk (cardiomyopathy risk from our previously develop AI model using ECG-alone), ECHO parameters, age, race, sex, BMI, primary cancer diagnosis, heart rate, respiratory rate, systolic and diastolic blood pressures, smoking, diabetes, hypertriglyceridemia, hypertension, or hypercholesterolemia. Light gradient boosting machines (LGBM) were applied to predict risk for cardiomyopathy using training (60%) and validation (20%) data. The hold-out (20%) data was used to evaluate predictive performance as sensitivity, specificity, precision, negative predictive value (NPV) and AUC. Shapley Additive exPlanations method (SHAP) was used to uncover predictors’ importance. Findings: The study population included 1,046 survivors with mean age±std of 32.2±8.0. The median time between baseline and follow-up evaluations was 5.2 years (0.5-9.5). In the hold out set, a model using ECHO parameters-only yielded an AUC of 0.68 (CI: 0.55-0.81), ECG-AI based risk alone yielded a hold-out test AUC of 0.87 (CI: 0.79-0.95), and an AUC of 0.89 (CI: 0.82-0.96) was obtained on LGBM model when ECG, ECHO, and clinical risk factors were used together. The final model classified cardiomyopathy with 80% sensitivity, 82% specificity, precision of 32%, and NPV of 98%. SHAP results showed that ECG-AI based risk, LV Global Longitudinal Peak Strain, LV Diastolic Volume, LV systolic volume, and cumulative anthracycline dose (>200 mg/m 2 ) were associated with an increased risk, and LVOT Peak gradient was associated with decreased risk. Conclusion: Artificial intelligence-based analyses with ECG and ECHO images may aid in cardiomyopathy risk prediction for at-risk survivors of childhood cancer.

After a 20-year-old woman suddenly died, autopsy showed characteristic findings of biventricular arrhythmogenic cardiomyopathy. Screening of her family members revealed the same desmoplakin gene mutation and imaging abnormalities predominantly involving the left ventricle. We describe the variable phenotypic expression in a family that shares a common gene variant. (Level of Difficulty: Advanced.), Graphical abstract

Background: Survivors of childhood cancer have an increased risk of therapy-related cardiovascular disease. It is not known whether family history of cardiovascular disease further increases risk of adverse cardiovascular outcomes among survivors. Methods: Family history of cardiovascular disease was collected from 1,260 survivors [median age at diagnosis, 8 years (range, 0–23); age at last follow-up, 35 years (range, 18–66)] of childhood cancer in the St. Jude Lifetime Cohort Study. Multivariable risk models evaluated associations with cardiovascular disease (Common Terminology Criteria for Adverse Events grade 2–4 events) and cardiovascular risk factors. Results: Among survivors exposed to chest-directed radiation and/or anthracycline chemotherapy (n = 824), 7% reported a first-degree family history of heart failure, 19% myocardial infarction, 11% stroke, 26% atherosclerotic disease (myocardial infarction and/or stroke), 62% hypertension, and 31% diabetes mellitus. Eighteen percent of exposed survivors developed heart failure, 9% myocardial infarction, 3% stroke, 11% atherosclerotic disease, 30% hypertension, and 9% diabetes mellitus. Having a first-degree family history of atherosclerotic disease was independently associated with development of treatment-related heart failure [RR, 1.38; 95% confidence interval (CI), 1.01–1.88; P = 0.04] among exposed survivors. Risk for hypertension was increased among exposed survivors with a first-degree family history of hypertension (RR, 1.55; 95% CI, 1.26–1.92; P < 0.0001) or of any cardiovascular disease [myocardial infarction, stroke, or heart failure (RR, 1.30; 95% CI, 1.06–1.59; P = 0.01)]. Conclusions: Family history of cardiovascular disease and cardiovascular risk factors independently increased risk of heart failure and hypertension among survivors of childhood cancer exposed to cardiotoxic therapies. Impact: These data show the importance of cardiovascular family history as a risk factor for cardiovascular disease in survivors of childhood cancer.

Background Studies indicate that uninterrupted anticoagulation (UA) is superior to interrupted anticoagulation (IA) in the periprocedural period during catheter ablation of atrial fibrillation. Still IA is followed in many centers considering the bleeding risk. This meta‐analysis compares interrupted and uninterrupted direct oral anticoagulation during catheter ablation of atrial fibrillation. Methods A systematic search into PubMed, EMBASE, and the Cochrane databases was performed and five studies were selected that directly compared IA vs UA before ablation and reported procedural outcomes, embolic, and bleeding events. The primary outcome of the study was major adverse cerebro‐cardiovascular events. Results The meta‐analysis included 840 patients with UA and 938 patients with IA. Median follow‐up was 30 days. Activated clotting time (ACT) before first heparin bolus was significantly longer with UA (P = .006), whereas mean ACT was similar between the two groups (P = .19). Total heparin dose needed was significantly higher with IA (mean, ‒1.61; 95% CI, ‒2.67 to ‒0.55; P = .003). Mean procedure time did not vary between groups (P = .81). Overall complication rates were low, with similar major adverse cerebro‐cardiovascular event (P = .40) and total bleeding (P = .55) rates between groups. Silent cerebral events (SCEs) were significantly more frequent with IA (log odds ratio, ‒0.90; 95% CI, ‒1.59 to ‒0.22; P, This meta‐analysis compares interrupted and uninterrupted direct oral anticoagulation during catheter ablation of atrial fibrillation. Uninterrupted anticoagulation during atrial fibrillation ablation has similar bleeding event rates, procedural times, and mean activated clotting times as interrupted anticoagulation, with fewer silent cerebral events.

Patients with left ventricular noncompaction (LVNC) are at risk of clinically significant arrhythmias and sudden death. We evaluated whether implantable loop recorders could detect significant arrhythmias that might be missed in these patients during annual Holter monitoring. Selected pediatric and adult patients with LVNC who consented to implantable loop recorder placement were monitored for 3 years (study duration, 10 April 2014–9 December 2019). Fourteen subjects were included (age range, 6.5–36.4 yr; 8 males). Of 13 patients who remained after one device extrusion, one underwent implantable cardioverter-defibrillator placement. Four patients (31%) had significant arrhythmias: atrial tachycardia (n=2), nonsustained ventricular tachycardia (n=1), and atrial fibrillation (n=1). All 4 events were clinically asymptomatic and not associated with left ventricular ejection fraction. In addition, a high frequency of benign arrhythmic patterns was detected. Implantable loop recorders enable continuous, long-term detection of important subclinical arrhythmias in selected patients who have LVNC. These devices may prove to be most valuable in patients who have LVNC and moderate or greater ventricular dysfunction.