A community nurse referral system for HMRs: Can it work?

The HMR model contains a mechanism whereby anyone who is concerned about the risk of medication misadventure can request a HMR from the patient's GP. Since nurses are widely involved in a range of triage and gatekeeping roles, utilising their primary care skills to identify patients for a HMR is a logical extension of this role. Furthermore, community nurses visit their clients in the home situation and see many difficulties the client may be experiencing at first hand. They are therefore well placed to request specialist assistance for the client. Blue Care in Brisbane, a community nursing service, approached its local Division of General practice to determine how best to request HMRs for its clients. The Division contacted The University of Queensland which initiated this study to engage the health care team to tailor the established HMR request process to the needs of community nurses and test the system developed. (non-author abstract)

Morphology of breast cancer as a means of triage of patients for BRCA1 genetic testing

Background: Women who have germline mutations in the BRCA1 gene are at substantially increased lifetime risk of developing breast and ovarian cancer but are otherwise normal. Currently. early age of onset of cancer and a strong family history are relied upon as the chief clues as to who should be offered genetic testing. Certain morphologic and immunohistochemical features are overrepresented in BRCA1-associated breast cancers but these differences have not been incorporated into the current selection criteria for genetic testing. Design: Each of the 4 pathologists studied 30 known cases of BRCA1- and BRCA2-associated breast cancer from kConFab families. After reviewing the literature, we agreed on a semiquantitative scoring system for estimating the chances of presence of an underlying BRCA1 mutation, based on the number of the reported prototypic features present. After a time lag of 12 months, we each examined a series of 62 deidentified cases of breast cancer, inclusive of cases of BRCA1-associated breast cancer and controls. The controls included cases of BRCA2-associated breast cancer and sporadic cases. Results: Our predictions had a sensitivity of 92%, specificity of 86%, positive predictive value of 61%, and negative predictive value of 98%. For comparison the sensitivity of currently used selection criteria are in the range of 25% to 30%. Conclusion: The inclusion of morphologic and immunohistochemical features of breast cancers in algorithms to predict the likelihood of presence of germline mutations in the BRCA1 gene improves the accuracy of the selection process.