Feasibility of a Traceback Approach for Using Pathology Specimens to Facilitate Genetic Testing in the Genetic Risk Analysis in Ovarian Cancer (GRACE) Study Protocol.
Tia L KauffmanYolanda K PradoAna A ReyesJamilyn M ZeppJennifer SawyerLarissa Lee WhiteJessica MartucciSuzanne Bianca SalasSarah VertreesAlan F RopeSheila WeinmannNora B HenriksonSandra Soo-Jin LeeHeather Spencer FeigelsonJessica Ezzell HunterPublished in: Journal of personalized medicine (2021)
Guidelines currently state that genetic testing is clinically indicated for all individuals diagnosed with ovarian cancer. Individuals with a prior diagnosis of ovarian cancer who have not received genetic testing represent missed opportunities to identify individuals with inherited high-risk cancer variants. For deceased individuals, post-mortem genetic testing of pathology specimens allows surviving family members to receive important genetic risk information. The Genetic Risk Assessment in Ovarian Cancer (GRACE) study aims to address this significant healthcare gap using a "traceback testing" approach to identify individuals with a prior diagnosis of ovarian cancer and offer genetic risk information to them and their family members. This study will assess the potential ethical and privacy concerns related to an ovarian cancer traceback testing approach in the context of patients who are deceased, followed by implementation and evaluation of the feasibility of an ovarian cancer traceback testing approach using tumor registries and archived pathology tissue. Descriptive and statistical analyses will assess health system and patient characteristics associated with the availability of pathology tissue and compare the ability to contact and uptake of genetic testing between patients who are living and deceased. The results of this study will inform the implementation of future traceback programs.
Keyphrases
- healthcare
- risk assessment
- end stage renal disease
- genome wide
- primary care
- copy number
- chronic kidney disease
- ejection fraction
- randomized controlled trial
- study protocol
- health information
- kidney transplantation
- machine learning
- quality improvement
- big data
- patient reported outcomes
- papillary thyroid
- decision making