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Purpose: Describe outcomes of patients (pt) with pre-tx COVID-19. Method(s): Multicenter study of SOT/HCT candidates who had a positive (pos) SARS-CoV-2 PCR pre-tx. Result(s): Pre-tx: Of 208 pt, median age was 56 (range 3-76). 87.8% were SOT candidates (40.5% kidney, 40.5% liver, 9.8% lung, 6.9% heart, 2.3% pancreas) and 13.9% were HCT candidates (54.2% allo, 45.8% auto). Pt underwent a median of 2 tests (range 1 - 14). In 41% of pt, > 1 neg PCR was required by the tx center before reactivation. Neg PCR was documented in 67.4% of pt at a median of 41 days (18-68) after pos PCR. Waitlist mortality was 11.0%;deaths were due to COVID-19 in 60% (12/20). Post-tx (all pt): 78 pt underwent tx at a median of 65.5 days (range 17-324) from COVID-19;71/78 have completed 4-weeks of follow-up. 24/78 (30.7%) pt were still PCR pos at time of tx (details below). 54/78 (69.2%) pt underwent routine PCR testing post-tx;62% were tested regularly for 8 weeks. Only 1 pt, who remained asymptomatic, developed recurrent pos PCR on surveillance testing 18 days post-tx. 1 pt had graft loss. There were no deaths at 4 weeks post-tx. Pt transplanted without a negative PCR: 24 pt with COVID-19 did not have neg PCR at time of tx: 9 (37.5%) kidney, 9 (37.5%) liver, 2 (8.3%) SLK, 1 (4.2%) lung, 1 heart (4.2%), 2 auto-HSCT (8.3%), 2 allo-HSCT (8.3%). Of 24 pt who were reactivated at a median of 21 days (range 8 - 38) from COVID-19 diagnosis, 7 underwent tx emergently (5 liver, 1 lung, 1 heart). 20/24 completed 4-weeks of follow-up;all were alive. PCR Cycle thresholds (Ct) increased over time, suggesting a reduction in SARS-CoV-2 viral loads with time elapsed since COVID-19 diagnosis. Conclusion(s): Short-term outcomes of transplantation in SOT/HCT candidates with prior COVID-19 were promising in this small cohort, even with a positive PCR going into transplant. Whether documentation of a negative PCR should be required for all tx candidates with a history of COVID-19 prior to transplantation should be investigated further, particularly among lung tx candidates. For certain tx candidates with COVID-19, relying time-based strategy instead of a test-based strategy may be safe.
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Background/Case Studies: One of the essential tasks of operating a transfusion medicine service is the management of blood inventory. In response to the uncertainty in blood use and blood supply brought about by the COVID-19 pandemic, a Blood Inventory Management Dashboard (BIMD) for the University Hospitals Healthcare System (UHHS) was developed and implemented. Study Design/Methods: A multidisciplinary working group consisting of blood bank medical directors, laboratory managers, and information technology specialists were involved in the design of the BIMD. Hospital IT resources were prioritized to develop the dashboard. The dashboard informatics design pulls data from our blood bank information system (HCLL version 2015, SP1, WellSky® (pka Mediware®) (Overland Park, KS)). Reports are then generated using Microsoft SQL ServerData Tools 2015 (Redmond, WA). Data elements include: available blood components, total number of uncrossmatched red blood cells (RBC) divided based on ABO group and Rh type, patients who are high-volume users, and number of blood components issued and used. Inventory was trended over 7 and 30 days and graphically displayed. As a refinement, the dashboard was modified to display color coded inventory per level status (adequate, guarded, and critical). The dashboard display is configured in a tabular and graphical format via Microsoft Excel 2016 (Redmond, WA). Results/Findings: Beginning on March 22, 2020, the dashboard has been automatically distributed prior to 8 AM daily via email to the health system Incident Command Center, Blood Bank medical directors, and other organizational leadership. It immediately became a crucial tool for planning and served as a mechanism to identify the need to move blood inventory throughout the system over the course of the pandemic and permitting identification of high volume users for interventions of ordering additional inventory based on anticipated needs. The number of blood component units are now tracked across all the hospitals within our system. The breakout by ABO and Rh type is a novel and valuable feature of the dashboard and presents valuable summary data that are not available in the laboratory information system. Conclusions: The development of an automated dashboard enables monitoring blood inventory levels across a multiple-hospital health system. The dashboard provides data broken down by ABO and Rh type, data not readily retrievable in the laboratory information system. The dashboard provides tracking of blood product utilization, and the ability to respond dynamically to blood needs throughout the system over the course of the pandemic. The BIMD has proven to be a key tool to inform decision making by organizational and blood bank leadership in the COVID-19 pandemic and in developing future contingency plans in managing blood bank inventory during unforeseen emergencies.