Blood transfusion trends by disease category in the United States, 2000 to 2014

Continued stabilization of blood collections and transfusions in the United States: Findings from the 2021 National Blood Collection and Utilization Survey

BACKGROUND

National Blood Collection and Utilization Surveys (NBCUS) have reported decreases in U.S. blood collections and transfusions since 2008. The declines began to stabilize in 2015-2017, with a subsequent increase in transfusions in 2019. Data from the 2021 NBCUS were analyzed to understand the current dynamics of blood collection and use in the United States.

METHODS

In March 2022, all community-based (53) and hospital-based (83) blood collection centers, a randomly selected 40% of transfusing hospitals performing 100-999 annual inpatient surgeries, and all transfusing hospitals performing ≥1000 annual inpatient surgeries were sent a 2021 NBCUS survey to ascertain blood collection and transfusion data. Responses were compiled, and national estimates were calculated for the number of units of blood and blood components collected, distributed, transfused, and outdated in 2021. Weighting and imputation were applied to account for non-responses and missing data, respectively.

RESULTS

Survey response rates were 92.5% (49/53) for community-based blood centers, 74.7% (62/83) for hospital-based blood centers, and 76.3% (2102/2754) for transfusing hospitals. Overall, 11,784,000 (95% confidence interval [CI], 11,392,000-12,177,000) whole blood and apheresis red blood cell (RBC) units were collected in 2021, a 1.7% increase from 2019; 10,764,000 (95% CI, 10,357,000-11,171,000) whole blood-derived and apheresis RBC units were transfused, a 0.8% decrease. Total platelet units distributed increased by 0.8%; platelet units transfused decreased by 3.0%; plasma units distributed increased by 16.2%; and plasma units transfused increased by 1.4%.

DISCUSSION

The 2021 NBCUS findings demonstrate a stabilization in U.S. blood collections and transfusions, suggesting a plateau has been reached for both.

From demand forecasting to inventory ordering decisions for red blood cells through integrating machine learning, statistical modeling, and inventory optimization

BACKGROUND

The demand and supply of blood are highly variable over time. Blood inventory management that relies heavily on experience-based decisions may not be adaptive to real demand, leading to high operational costs, wastage, and shortages.

METHODS

We combined statistical modeling, machine learning, and optimization methods to develop a data-driven demand forecasting and inventory management strategy for red blood cells (RBCs). We then used the strategy to inform daily blood orders. A secondary semi-weekly (twice per week) ordering strategy was developed to handle the last-mile split delivery problem for blood suppliers, characterized by multi-deliveries to the same location multiple times during a short period of time. Both strategies were evaluated using the TRUST database including all patient data across four hospitals in Hamilton, Ontario.

RESULTS

We identified 227,944 RBC transfusions for 40,787 patients in Hamilton, Ontario from 2012 to 2018. The predicted daily demand from the hybrid demand forecasting model was not significantly different from the actual daily demand (paired t-test p-value = 0.163); however, the proposed daily ordering quantity from the model was significantly lower than the actual ordering quantity (p-value <0.001). The proposed daily ordering strategy reduced inventory levels by 38.4% without risk of shortages, leading to an overall cost reduction of 43.0% (95% confidence interval [CI]: 42.3%, 43.7%) compared with the actual cost. The semi-weekly ordering strategy reduced ordering frequency by 62.6% (95% CI: 61.5%, 63.7%).

CONCLUSION

The proposed data-driven ordering strategy combining demand forecasting and inventory optimization can achieve significant cost savings for healthcare systems and blood suppliers.