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Sudden Cardiac Arrest Among Young Competitive Athletes Before and During the COVID-19 Pandemic
Get PDFThis cohort study compares the prevalence of sudden cardiac arrest or death among young athletes in the 3 years before vs the first 3 years of the COVID-19 pandemic
Evaluating the intersection of climate vulnerability and cancer burden in North Carolina
Get PDFClimate-related extreme weather events disrupt health-care systems and exacerbate health disparities, particularly affecting individuals diagnosed with cancer. This study explores the intersection of climate vulnerability and cancer burden in North Carolina (NC). Using county-level data from the US Climate Vulnerability Index (CVI) and the NC Department of Health and Human Services, we analyzed cancer incidence and mortality rates from 2017 to 2021. Our findings reveal a robust correlation between CVI percentiles and cancer mortality (r = 0.72). Counties with high area deprivation like Scotland, Robeson, and Halifax had the highest CVI percentiles of 0.68, 0.67, and 0.66, with respective cancer mortality rates of 193, 195, and 196 per 100 000 person-years. Correlations between CVI and cancer incidence were modest (r = 0.22). These results underscore the need for targeted public health interventions to mitigate climate-related health disparities. Future work could focus on exploring specific climate hazards and cancer outcomes to enhance preparedness and resilience in cancer care
Machine learning enhanced immunologic risk assessments for solid organ transplantation
Get PDFThe purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML). Prediction of crossmatch compatibility is complex and requires an understanding of the recipient and donor human leukocyte antigen (HLA) alleles and recipient HLA antibodies. An HLA allele imputation system that converts HLA antigens to alleles was developed to enhance the prediction’s performance. The imputed and known HLA alleles were combined for recipient and donor with a recipient’s HLA antibody profile. After processing, donor-specific antibodies were input into various ML models. Next, an ML model was developed and characterized based on determining donor-specific antibodies using the full HLA antibody profile of the recipient without laboratory interpretation. The models achieved an ROC-AUC of 0.975. These results demonstrate that the models can predict crossmatch reactivity and yield insight into the importance of specific HLA antibodies in the transplant-matching process. These data represent our understanding of personalized histocompatibility risk assessments
