Assessing the Feasibility of Predictive Modeling for HFE- Hereditary Hemochromatosis using Electronic Health Records

Abstract

Secondary use of electronic health records allows researchers the opportunity to test hypothe ses and gain new insights on complex disease phenotypes. Hereditary hemochromatosis is an inherited autosomal recessive disorder that causes excessive absorption of iron. Early diagnosis and disease management are critical, as iron accumulation in tissue leads to organ failure and eventually death. Diagnosis of hereditary hemochromatosis requires evidence of iron overload and a positive genetic test result. At the University of Washington there are no standard clinical guidelines for hemochromatosis genetic testing and only 7.5% of patients tested have a confirmed diagnosis. We aimed to identify potential variables for additional screening criteria and inform clinical guidelines for hemochromatosis genetic testing. We found that using established recommen dations for genetic testing of hemochromatosis from the American Association for the Study of Liver Diseases (AASLD) and the European Association for Study of the Liver (EASL) on patients screened by their physician for testing would have reduced the number of tested patients from 873 to 345 and maintained 92% of positive diagnoses. Logistic regression and association rule mining both confirmed that high transferrin satu ration is positively associated with HFE-hemochromatosis. It may not be possible to dis tinguish between hemochromatosis caused by HFE mutations and other genetic variants making a wider hemochromatosis gene panel necessary to identify all cases and discover novel variants.