Modeled residual current cancer risk after clinical investigation of a positive multicancer early detection test result.

BACKGROUND: Positive results of a multi-cancer early detection (MCED) test require confirmatory diagnostic workup. Here, residual current cancer risk (RR) during the process of diagnostic resolution, including situations where the initial confirmatory test does not provide resolution, was modeled. METHODS: A decision-tree framework was used to model conditional risk in a patient's journey through confirmatory diagnostic options and outcomes. The diagnostic journey assumed that cancer signal detection (a positive MCED test result) had already led to a transition from screening to diagnosis and began with an initial positive predictive value (PPV) from the positive result. Evaluation of a most probable (top) predicted cancer signal origin (CSO) and then a second-most probable predicted CSO followed. Under the assumption that the top- and second-predicted CSOs were each followed by a targeted confirmatory test, the RR was estimated for each subsequent scenario. RESULTS: For an initial MCED test result with typical performance characteristics modeled (PPV, 40%; top-predicted CSO accuracy, 90%), after a negative initial confirmatory test (sensitivity, 70%, 90%, or 100%) the RR ranged from 6% to 20%. A second-predicted CSO (accuracy, 50%), after a negative second confirmatory test, still provided a significant RR (3%-18%) in comparison with the National Institute for Health and Care Excellence-recommended cancer risk threshold warranting investigation in symptomatic individuals (3%). With a 40% PPV for an MCED test and 90% specificity for a confirmatory test, the risk of incidental findings after one or two confirmatory tests was 6% and 12%, respectively. CONCLUSIONS: These results may illustrate the impact of a positive MCED test on follow-up decision-making.

CM-SHARE: Development, Integration, and Adoption of an Electronic Health Record-Linked Digital Health Solution to Support Care for Diabetes in Primary Care.

IN BRIEF Chronic conditions such as diabetes are largely managed by primary care providers (PCPs), with significant patient self-management. This article describes the development, pilot testing, and fine-tuning of a Web-based digital health solution to help PCPs manage patients with cardiometabolic diseases during routine office encounters. It shows that such products can be successfully integrated into primary care settings when they address important unmet needs and are developed with input from end-users.