Lympho-hematopoietic malignancies risk after exposure to low dose ionizing radiation during cardiac catheterization in childhood.

Pediatric patients with congenital heart disease (CHD) often undergo low dose ionizing radiation (LDIR) from cardiac catheterization (CC) for the diagnosis and/or treatment of their disease. Although radiation doses from a single CC are usually low, less is known about the long-term radiation associated cancer risks. We aimed to assess the risk of lympho-hematopoietic malignancies in pediatric CHD patients diagnosed or treated with CC. A French cohort of 17,104 children free of cancer who had undergone a first CC from 01/01/2000 to 31/12/2013, before the age of 16 was set up. The follow-up started at the date of the first recorded CC until the exit date, i.e., the date of death, the date of first cancer diagnosis, the date of the 18th birthday, or the 31/12/2015, whichever occurred first. Poisson regression was used to estimate the LDIR associated cancer risk. The median follow-up was 5.9 years, with 110,335 person-years. There were 22,227 CC procedures, yielding an individual active bone marrow (ABM) mean cumulative dose of 3.0 milligray (mGy). Thirty-eight incident lympho-hematopoietic malignancies were observed. When adjusting for attained age, gender and predisposing factors to cancer status, no increased risk was observed for lympho-hematopoietic malignancies RR/mGy = 1.00 (95% CI: 0.88; 1.10). In summary, the risk of lympho-hematopoietic malignancies and lymphoma was not associated to LDIR in pediatric patients with CHD who undergo CC. Further epidemiological studies with greater statistical power are needed to improve the assessment of the dose-risk relationship.

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data.

1H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly.