Development of an optimal maximum surgical blood ordering schedule to achieve a prespecified crossmatch-to-transfusion ratio.

BACKGROUND

The Maximum Surgical Blood Ordering Schedule (MSBOS) is a procedure-specific lookup table for preoperative blood product ordering based on historical institutional transfusion rates.

METHODS

We developed an optimal MSBOS algorithm that minimizes the probability that an adult patient will need a red blood cell transfusion that exceeds the MSBOS reservation, constrained by a prespecified overall crossmatch-to-transfusion ratio (e.g., 1.50 or 2.00). We provide Microsoft Excel 365 and Stata implementations. We tested the mathematics using 192,822 surgical cases across 2430 procedure codes over 7.8 years at a teaching hospital.

RESULTS

The probability distribution of units of red blood cells transfused did not follow Poisson distributions (i.e., suitability of MSBOS reservations cannot accurately be calculated from each patient's probability of transfusion or not). However, for 100% of procedures, there were monotonic decreases in the probabilities of extra units transfused (e.g., most patients 0 units, some 1-2 units, and few 3-4 units). We used this general shape for calculating the optimal MSBOS. Comparing this priority-based model with an alternative policy based on the mean units transfused per patient, MSBOS reservations were identical for the vast majority of procedures. When differences occurred, the priority-based assignment usually recommended higher reservation levels than the alternative heuristic: 288 procedures higher, and 14 lower, mostly by one unit. With the overall crossmatch-to-transfusion (CT) ratio preset at ≤2.00, the mean (standard deviation) of the ratio across the 135 categories each with at least one crossmatched unit was 1.787 (0.977). In other words, priority-based assignment results in large inequality of the crossmatch to transfusion ratio among procedures. The priority-based assignment maintained the ≤2.00 crossmatch-to-transfusion ratio while achieving a 37.9% decrease in the total hospital blood bank units (surgical and non-surgical) transfused but unreserved. Therefore, inventory par levels can be lower. This approach would have the greatest benefit for hospitals with blood banks using electronic crossmatching that are remotely located from the operating rooms suites and do not have red cell dispensing kiosks.

CONCLUSIONS

Our methodology provides an automated, optimal MSBOS for hospital blood bank inventory management that minimizes the probability that a patient will need a red blood cell transfusion that exceeds the reserved units, subject to the desired overall crossmatch to transfusion ratio. The optimal MSBOS can be used with our earlier methodology for automated decision-making for which patients have blood type and antibody screening.