Critical congenital heart disease (CCHD) represents a significant subset of congenital heart disease (CHD). While the association between maternal diabetes mellitus and offspring CHD is well established, the specific relationship between maternal diabetes and CCHD remains underexplored. This study aims to investigate the association between maternal diabetes and the incidence of offspring CCHD. We analyzed natality data from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) from 2016 to 2021. The data set included information on maternal and paternal attributes, pregnancy history, prenatal care, and congenital anomalies among newborns. We included all live births in the United States, focusing on single births at a gestational age of ≥20 weeks. Multivariable logistic regression was used to explore the relationship between gestational diabetes, pregestational diabetes, and CCHD. Among 22,646,079 live births, 13,533 cases of CCHD were identified, with an incidence of 6 per 10,000 live births. Pregestational diabetes was associated with a 4.33-fold higher risk of CCHD (aOR: 4.33; 95% CI: 3.93-4.76), and gestational diabetes with a 1.47-fold higher risk (aOR: 1.47; 95% CI: 1.38-1.57). Additional risk factors included pregestational hypertension, gestational hypertension, and late initiation of antenatal care. A longer gestational age was associated with a lower risk of CCHD. Maternal diabetes, both pregestational and gestational, significantly increases the risk of CCHD. These findings highlight the need for targeted interventions and monitoring of diabetic mothers to mitigate the risk of CCHD in their offspring.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment but are associated with rare immune-related adverse events, particularly cardiotoxicity. We present the case of a 63-year-old man with a history of metastatic melanoma treated with ipilimumab and nivolumab, who developed recurrent pericarditis complicated by cardiac tamponade on 2 separate occasions with 1 episode resulting in cardiac arrest. Despite ICI disruption and standard pericarditis treatment of a prolonged course of colchicine and prednisone, the patient experienced recurrent pericarditis. The introduction of rilonacept led to significant clinical improvement. This case demonstrates the potential effectiveness of rilonacept in managing ICI-induced recurrent pericarditis, especially when conventional treatment fails.

Introduction: Cardiovascular disease (CVD) is a leading cause of non-cancer death among cancer survivors, attributable to cardiotoxic therapies and cardiovascular risk factors. General population risk prediction tools, including ASCVD (Atherosclerotic cardiovascular disease), Framingham’s Score, and PREVENT (Predicting Risk of Cardiovascular Disease EVENTS), lack cancer-specific variables. We evaluated whether these models, even after statistical optimization, could predict cardiovascular mortality in cancer survivors. Methods: Using the National Health and Nutrition Examination Survey (NHANES) 2001–2018, linked with National Death Index (NDI) mortality data, we conducted a retrospective analysis of 634 and 429 cancer survivors, respectively, across model-specific cohorts free of baseline cardiovascular disease. Discrimination was assessed for ASCVD, Framingham Score, and PREVENT using standardized thresholds of 7.5% and 20%, as well as Youden-optimized cutoffs. Area under the curve (AUC) comparisons were performed using the DeLong non-parametric method. Results: Standard thresholds showed suboptimal discrimination across all models (AUCs: ASCVD 0.56, Framingham 0.53, PREVENT 0.64). In contrast, Youden-optimized AUCs (ASCVD: 0.68; PREVENT: 0.71; all p < 0.001, DeLong test). Optimization increased the “low-risk” group’s mortality rate from 2.8% to 4.1% (RR = 1.47), suggesting improved statistical fit came at the cost of overestimating the risk. Optimized thresholds outperformed conventional cutoffs, underscoring the necessity for recalibrated, cohort-specific risk stratification in cancer survivors. Conclusions: Standard risk scores have inadequate discrimination for cardiovascular mortality prediction in cancer survivors. Threshold recalibration improves statistical metrics but does not resolve the structural failure of these models to account for cardiotoxic exposure. Development of cardio-oncology-specific risk models incorporating oncologic exposures is therefore warranted.