Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA.

The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state's response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.

Gene-based warfarin dosing compared with standard of care practices in an orthopedic surgery population: a prospective, parallel cohort study.

Warfarin remains a difficult drug to manage due to a narrow therapeutic range and wide interindividual variability in dose requirements. The relationship between warfarin sensitivity and CYP2C9 and VKORC1 variants is strong, and is the basis for several proposed dosing algorithms. Here a gene-based dosing algorithm was compared with standard of care dosing in patients receiving warfarin to prevent venous thromboembolism after joint replacement surgery. Participants (n = 229) were adults (> or =18 years) undergoing elective total hip or knee arthroplasty and receiving warfarin under the direction of a dedicated anticoagulation services team. Patients were assigned to genotype-based or standard of care dosing arms in an alternating fashion. Initial dose for patients was determined by validated algorithms from Sconce 2005 and Pendleton 2008. Management was based on INR, but dose was adjusted less aggressively for patients with CYP2C9 variants. The primary endpoint was reduction in the incidence of adverse events; additional endpoints included time to first therapeutic INR (1.8-2.9), time to first supratherapeutic INR, and percent of INR determinations that fell below, within, and above the therapeutic range. Endpoints did not achieve statistical significance, possibly due to the management of this study by a dedicated and experienced anticoagulation services team. Trends in the data suggest that patients with genetic variants progressed to a therapeutic INR faster than patients in whom genetic variants were not detected, and there were fewer adverse events in the genotype-based dosing arm. In addition, the results of this study confirm those of others demonstrating clear relationship of genotype for CYP2C9 and VKORC1 with warfarin dose requirements; as the number of variants in these genes increases, the dose requirement decreases. Of note, the gene-based algorithm utilized here significantly underpredicted the dose requirement for participants with no variants, indicating that patients with no variants should be managed with a different algorithm than patients who inherit genetic variants in CYP2C9 and/or VKORC1. In conclusion, gene-based dosing did not improve warfarin management as defined by INR dose response, using the described protocols for implementation. Findings suggest alternative strategies for dosing based on the presence or absence of genetic variants is needed.