The rate of undetectable genetic causes by Cell-free DNA test in congenital heart defects.

OBJECTIVE: The study aimed to estimate the rate of genetic causes that were undetectable by Cell-free DNA (cfDNA) test in prenatally diagnosed congenital heart defect (CHD) cases based on an assumption that cfDNA would accurately detect common aneuploidies including trisomy 21/18/13/45X, and del22q11.2. METHODS: This study included prenatally diagnosed CHD cases with diagnostic genetic results. The possibility of false-positive/negative results from cfDNA testing was discarded. Thus, cfDNA results would be positive in common aneuploidies or del22q11.2 and negative in normal diagnostic genetic testing results or other genetic conditions. The rate of genetic causes that were undetectable by cfDNA test was estimated for all cases as well as for CHD subgroups. RESULTS: Of 302 cases, 98 (34.8%) had a type of genetic abnormalities, with 67 having common aneuploidies or del22q11.2 and 31 having other genetic conditions. The rate of genetic causes that were undetectable by cfDNA test in CHD cases was 13.2% among those with assumingly negative cfDNA screen results and 10.3% among the entire study population. These rates were similar between CHD subgroups (p > .05). The rate of genetic causes that were undetectable by cfDNA test was higher in the non-isolated cases than in the isolated ones among those with assumingly negative-screen results (20.5% and 9.9%, respectively, p = .025). CONCLUSION: In prenatally diagnosed CDH cases, a significant number of chromosomal abnormalities are still identified after diagnostic testing even if cfDNA screen is negative, and thus it is important to extensively counsel patients with negative cfDNA screen carrying a CHD-affected fetus.

Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project.

Delivering genetic test results to clinicians is a complex process. It involves many actors and multiple steps, requiring all of these to work together in order to create an optimal course of treatment for the patient. We used information gained from focus groups in order to illustrate the current process of delivering genetic test results to clinicians. We propose a business process model and notation (BPMN) representation of this process for a Translational Pharmacogenomics Project being implemented at the University of Maryland Medical Center, so that personalized medicine program implementers can identify areas to improve genetic testing processes. We found that the current process could be improved to reduce input errors, better inform and notify clinicians about the implications of certain genetic tests, and make results more easily understood. We demonstrate our use of BPMN to improve this important clinical process for CYP2C19 genetic testing in patients undergoing invasive treatment of coronary heart disease.