T ROARN ISGFIUNSAI LO NA PR RT AI C LT EI C E Comparison of two platelet transfusion strategies to minimize ABO-nonidentical transfusion, outdating, and shortages using a computer-simulated “virtual blood bank” Ronald Jackups, Jr.1 and Steven Kymes2

BACKGROUND: Transfusion of ABO-incompatible platelets (PLTs) is associated with reduced PLT recovery and a risk of transfusion reactions. However, a policy of transfusing only ABO-identical PLTs may increase wastage due to product outdating. A prospective study attempting to compare the effects of different ABO compatibility strategies could be costly and disruptive to a blood bank’s operations. STUDY DESIGN AND METHODS: We designed a “virtual blood bank,” a stochastic computer program that models the stocking and release of products to meet demand for PLT transfusion in a simulated hospital population. ABO-nonidentical transfusions (ABOni), outdates, and inventory shortages were recorded and compared under two different transfusion strategies: ABO-First, a strategy that prioritizes transfusion of ABO-identical PLTs, and Age-First, a strategy that minimizes outdating by transfusing products closest to expiration. RESULTS: The ABO-First strategy resulted in fewer ABOni but more outdates than the Age-First strategy. Under conditions that mimic a large hospital blood bank, the ABO-First strategy was more cost-effective overall than the Age-First strategy if avoiding an ABOni is valued at more than $19 to $26. For a small blood bank, the ABO-First strategy was more cost-effective if avoiding an ABOni is valued at more than $104 to $123. CONCLUSION: Based on a virtual blood bank computer simulation, the cost of avoiding an ABOni using the ABO-First strategy varies greatly by size of institution. Individual blood banks must carefully consider these management strategies to determine the most cost-effective solution.

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he transfusion of ABO-incompatible platelets (PLTs) is a controversial issue, and it remains the subject of much study.1,2 Major mismatches, in which the transfused PLTs are incompatible with the recipient’s ABO type (e.g., group A PLTs transfused to a group O recipient) are associated with reduced posttransfusion PLT increments2-6 and PLT refractoriness.7 However, the effect of major-mismatched transfusions on clinical outcomes such as mortality is unclear.4,8 Minor mismatches, in which the donor plasma within the PLT product is incompatible with the recipient’s red blood cells (RBCs; e.g., group O PLTs transfused to a group A recipient), have been implicated in hemolytic transfusion reactions, some of which were fatal, most commonly when the unit was group O.1,9-11 However, it is unclear if such reactions are frequent enough to affect patient care on a large scale.8,12,13 ABO-nonidentical PLT transfusions (ABOni) may also be associated with higher RBC transfusion requirements and a higher cost for hospital stay.14,15 National accrediting agencies in the United States require laboratories to adopt procedures to address the transfusion of ABO-incompatible plasma, but do not specify what strategies should be employed.16,17 Laboratories in Europe, and some in the United States, measure ABO isoagglutinin titers in plateletpheresis donors to prevent the transfusion of ABO-incompatible plasma

ABBREVIATIONS: ABOni = ABO-nonidentical transfusions; MDD = mean daily demand; OUTL(s) = order-up-to limit(s). From the 1Department of Pathology & Immunology and the 2 Department of Ophthalmology & Visual Science, Washington University School of Medicine, St Louis, Missouri. Address reprint requests to: Ronald Jackups, Jr., 660 S. Euclid Avenue, Campus Box 8118, St Louis, MO 63110; e-mail: [email protected]. Received for publication November 5, 2013; revision received July 6, 2014, and accepted July 8, 2014. doi: 10.1111/trf.12831 © 2014 AABB TRANSFUSION **;**:**-**. 2015;55:348–356. Volume **, ** **

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“virtual blood bank” in which the vital processes of stocking an inventory of PLT products and dispensing them to meet patient need could be assayed under a variety of different variables. This would allow us to determine in what situations a policy of minimizing ABOni would be cost-effective, without requiring the resources needed to perform such a study in a real blood bank. The results could then provide blood bank directors and managers with the necessary preliminary information to make a prudent decision.

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Fig. 1. Schematic of our virtual blood bank computer program with respect to PLT transfusion. Orders for PLT transfusion occur periodically throughout the day according to a predetermined mean daily demand (MDD), while the blood bank fills those orders according to a specific matching strategy, either ABO-First or Age-First (described under Materials and Methods). At the end of the day, expired units are removed, and the inventory is restocked to a predetermined order-up-to limit (OUTL). The ABO types of both donors and recipients are randomly selected from a predetermined ABO frequency distribution (Table 1).

from a “high-titer” donor.11,18 However, among 2585 North American laboratories surveyed, only 2% use this strategy, compared with 26% that transfuse only ABO-compatible PLT products, 10% that set a volume limit on ABOincompatible plasma per recipient, and 6.5% that perform volume reduction on incompatible products.9 Overall, a majority of the surveyed laboratories did issue PLT products containing ABO-incompatible plasma, and 17% reported that routine exceptions to their policy of minimizing ABO-incompatible plasma transfusion in PLTs would be made to avoid product wastage due to outdating.9 As PLT products have only a 5-day shelf life in the United States, a blood bank that adopts an ABO identical–only transfusion policy must stock a larger supply of products to be prepared for situations in which demand for a particular ABO group increases unexpectedly; as a result, the chance that these excess products reach their expiration date is higher. As PLT products are among the most expensive blood products used today, such wastage can present a financial burden to a hospital blood bank. Henrichs and colleagues19 reported the consequences of adopting an ABO identical– only policy, which included a decrease in febrile and allergic transfusion reactions and a decrease in de novo RBC alloimmunization rates, but an increase in wastage of whole blood–derived PLT products from 10.8% to 16.4%. To further investigate the costs associated with ABO compatibility policies for PLT transfusion, we designed a 2

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MATERIALS AND METHODS We designed a virtual blood bank using the Perl programming language to simulate the processes of a typical hospital blood bank in a probabilistic manner, employing the following discrete event simulation algorithm (Fig. 1):

PLT units are stocked at the beginning of each “day” according to the order-up-to limit (OUTL). The OUTL is a constant defined at the beginning of each simulation. The number of units added to inventory is the OUTL minus the number of units present in inventory at the end of the previous day. The ABO type of each unit is selected randomly and independently from the ABO frequency distribution of a historical American donor population (Table 1). Throughout the day, single units of PLTs will be ordered for each “patient” at random intervals. The average number of units that will be ordered per day is called the mean daily demand (MDD), which, like the OUTL, is a constant defined at the beginning of each simulation. Random intervals will be generated according to a Poisson process, as described below. The ABO type of each recipient is selected randomly and independently from the ABO distribution of a historical American donor population, dependent on race (Table 1). The blood bank will fill each order according to a prespecified matching strategy, as described below. The unit chosen will be removed from inventory. Steps 2 and 3 will be repeated until the end of the day, at which point units that have reached their expiration date are discarded from inventory. As our hospital quarantines PLT units for 2 days while awaiting donor viral and bacterial testing, and the shelf life of a Volume 55, February 2015 TRANSFUSION 349

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TABLE 1. US donor ABO distributions (%) used as inputs to the virtual blood bank model (adapted from Garratty et al.20)* ABO type O A B AB

All donors 46.6 37.1 12.2 4.1

Caucasian 45.2 39.7 10.9 4.1

TABLE 2. Prioritization schedule for each matching strategy for a group A recipient*

African American 50.2 25.8 19.7 4.3

Unit ABO A A A AB AB AB B B B O O O

* The “all donors” distribution was used to randomly select products added to inventory at the beginning of each day in our model, while the Caucasian and African American distributions were used to randomly select recipients, using an allCaucasian, all-African American, or 50% Caucasian/50% African American population.

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Comparing simulation runs Each simulation was run for 10,000 “days” to ensure that the outcomes measured converged around a stable expected value. Each simulation was performed using different values for the input variables MDD and OUTL, as well as using different populations of transfusion recipients (Table 1). The MDDs chosen were 5, 10, and 30 units/ day, corresponding to annual PLT transfusion volumes of 1825, 3650, and 10,950 units. The OUTLs were selected so that the possible OUTL : MDD ratios were 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2.0. For instance, if the MDD was 30 units/day and the OUTL : MDD ratio was 1.5, the OUTL would be set at 45 units. This would be equivalent to a blood bank ensuring that there were 45 units at the beginning of each day, expecting 30 units to be ordered throughout the day on average. When an MDD of 5 was used, only the OUTL : MDD ratios 1.0, 1.2, 1.4, 1.6, 1.8, and 2.0 were assessed, corresponding to OUTLs of 5, 6, 7, 8, 9, and 10, respectively. Each string of simulated orders was run twice in parallel so that two different matching strategies could be used to fill those orders: 1.

ABO-First: ABO compatibility is given priority over the time to expiration. In other words, an ABOidentical unit that will expire later would be selected over an ABO-nonidentical unit that will expire sooner.

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Age-First Shelf life (days) 1 1 1 1 2 2 2 2 3 3 3 3

* A unit in inventory whose ABO type and remaining shelf life are highest on the list will be issued to a group A recipient during the simulation. Similar schedules for the other ABO groups were designed as described under Materials and Methods. Unit ABO = ABO type of unit; shelf life = remaining shelf life of unit (in days until expiration).

PLT unit is 5 days from collection, an effective expiration date of 3 days from entering inventory is used. Steps 1 to 4 are repeated for the next full day.

A Poisson process was selected to model the behavior of units being ordered in a hospital setting. Under a Poisson process, each “event” (in this case, a PLT transfusion order) occurs independently, and the expected average number of events in a given time period (in this case, each “day” of the simulation) is equal for each time period. Poisson processes are frequently used to model random discrete processes.21

ABO-First Shelf life (days)

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Age-First: Time to expiration is given priority over ABO compatibility. In other words, an ABOnonidentical unit that will expire sooner would be selected over an ABO-identical unit that will expire later.

As an example, if a unit was ordered for a group O patient and the only two available units in inventory were a group O unit that will expire in 2 days and a group A unit that will expire in 1 day, the group O unit would be given under the ABO-First strategy, while the group A unit would be given under the Age-First strategy. With either strategy, an ABO-identical unit that expires at the end of the day would be given the highest priority, while an ABOnonidentical unit that was freshly added to inventory would be given the lowest priority. In addition, given that blood banks are generally more concerned with avoiding a minor mismatch (particularly a group O unit for a non-O recipient) than a major mismatch, priority was given to issuing major mismatches when no appropriate ABOidentical unit was available and to issuing non-O minor mismatches for non-O recipients when no ABO-identical or major-mismatched unit was available. The prioritization schedule for group A recipients under each strategy is shown in Table 2 as an example. A third strategy, Age-Only, which is similar to AgeFirst except that ABO compatibility is given no priority, was tested separately and compared with the Age-First strategy, to determine the effect of giving no regard to ABO compatibility on the rate of ABOni. The outcomes measured with each run and matching strategy were ABOni, outdates (units discarded because they reached their expiration date without being transfused), and shortages (the number of times a unit was Volume **, ** **

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algorithm) based on the number of units issued on each date in that period, rather than using a Poisson random process, and repeated the period for 10,000 virtual days. We used a different MDD for different days of the week: 25, 40, 40, 40, 40, 40, and 25 units/day for Sunday through Saturday, respectively, to approximate the actual MDDs recorded in that period: 27.4, 42.9, 43.0, 40.5, 40.1, 42.3, and 26.7. By using MDDs divisible by 5, we were able to assess MDD : OUTL ratios of 1.0, 1.2, 1.4, 1.6, 1.8, and 2.0.

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Figures 2 and 3 illustrate the outcomes (ABOni, outdates, and shortages) of the Fig. 2. The effect of different MDD on the study outcomes, under an ABO-First stratABO-First strategy, by OUTL : MDD egy, with an all-Caucasian recipient population: (A) MDD, 5 units/day; (B) MDD, 10 ratio, using different input variables. units/day; (C) MDD, 30 units/day. ABOni (●), outdates (■), and shortages (▲) are The OUTL : MDD ratio was used so that reported as percentage of total number of transfusions. OUTL : MDD ratio reprethe results could be compared across sents the ratio of blood bank inventory size to expected demand for transfusion. different MDD values (Fig. 2). Regard*Note the different scale for outcome rate for (A) than for (B) and (C). less of MDD or recipient ABO distribution, it is clear from the results displayed ordered when the blood bank had no units left in inventhat as the OUTL : MDD ratio increases, the ABOni rate tory). These outcomes are reported as percentage of total decreases, the outdate rate increases, and the shortage transfusions given. rate decreases. In other words, if the blood bank stocks more products at the beginning of the day, it expects to have more ease in avoiding both ABOni and shortages, but Cost-effectiveness analysis more difficulty in avoiding outdates. For the purpose of determining which of the two matching As the MDD increases from 5 to 30 units/day (Fig. 2), strategies is the most cost-effective for a given MDD and all three negative outcomes decrease. In other words, the recipient population, we calculated the total cost due to larger the population served by the blood bank, the easier the three outcomes measured. As all three outcomes are it is to minimize ABOni without adversely affecting considered negative, we define the “cost” of each outcome outdate and shortage rates. As the racial characteristics of as the amount one would be willing to pay to prevent it. the transfusion recipient population change from allOutdates were priced as the cost of purchasing the unit, Caucasian to all-African American (Fig. 3), the ABOni rate which we estimated as $535.17, the average acquisition increases, although this effect is relatively small in the 50% cost reported in the National Blood Collection and UtiliCaucasian/50% African American mixed population. zation Survey for 2011.22 The costs of ABOni and shortages Since the donor population in the United States is more were varied to determine which combinations would Caucasian than African American, avoiding ABOni is more result in the two matching strategies being equally difficult when the recipient population contains mostly cost-effective. African Americans.

Validation

Comparison of matching strategies and validation

To demonstrate the utility of the simulation under realworld conditions, PLT transfusion data from our hospital were compiled for the period of January to June 2011. During this period, the number of units issued each day, outdates, and the number of ABOni were recorded. We then ran our simulation (stocking and matching

Figure 4A compares the ABOni and outdate rates between the two matching strategies, ABO-First (in which priority is given to minimizing ABOni) and Age-First (in which priority is given to transfusing those units closest to expiration), using an MDD of 30 units/day and an allCaucasian recipient ABO frequency distribution. The

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hand, the outdate rate increases at high OUTL : MDD ratios under the ABO-First 15 15 strategy, but remains close to 0% under the Age-First strategy, confirming that 10 10 the emphasis of the Age-First strategy 5 5 on reducing outdate rates is relatively successful. 0 0 Figure 4B compares the ABOni and 1.0 1.2 1.4 1.6 1.8 2.0 1.0 1.2 1.4 1.6 1.8 2.0 OUTL:MDD OUTL:MDD outdate rates between the two matching strategies using the actual transfusion events at our hospital from January C 20 100% AA to June 2011. We assumed a 50% 15 Caucasian/50% African American recipient population, which is similar to 10 our patient population. The outcomes 5 coincided relatively well with the experience at our institution, which nomi0 nally uses an ABO-First strategy and 1.0 1.2 1.4 1.6 1.8 2.0 OUTL:MDD OUTL : MDD ratio of approximately 1.5 and experienced a 7.5% ABOni rate and Fig. 3. The effect of different recipient ABO frequency distributions on the study out2.6% outdate rate for PLT transfusions comes, under an ABO-First strategy with an MDD of 30 units/day: (A) 100% Caucasian from January to June 2011. This com(CAU), (B) 50% Caucasian/50% African American (AA), (C) 100% African American. pares with a 7.9% ABOni rate and 0.5% ABO frequencies are shown in Table 1. ABOni (●), outdates (■), and shortages (▲) are outdate rate predicted by the virtual reported as percentage of total number of transfusions. OUTL : MDD ratio represents blood bank, using an OUTL : MDD ratio the ratio of blood bank inventory size to expected demand for transfusion. of 1.6. We believe that the actual outdate rate was much higher than the preA 20 B 20 dicted rate because our hospital accepts units that are closer to expiration than 3 15 15 days; during the validation period, 33% 10 10 of the units entered inventory with less than 2 days of remaining shelf life. 5 5 Figure 5 shows a breakdown of ABOni by type for the two strategies 0 0 1.0 1.2 1.4 1.6 1.8 2.0 1.0 1.2 1.4 1.6 1.8 2.0 shown in Fig. 4A. Despite efforts to OUTL:MDD OUTL:MDD avoid minor mismatches in our matching strategies, as illustrated in Table 2, Fig. 4. Study outcomes under two different transfusion matching strategies, using they represent the majority of ABOni (A) an MDD of 30 units/day and an all-Caucasian recipient population and (B) actual transfusions under both strategies. transfusion events at our hospital, as described under Materials and Methods. OutAdditionally, group O PLTs comprise comes are reported as percentage of total number of transfusions. ABOni rates were a significant fraction of minorlower under the ABO-First strategy (●) than under the Age-First strategy (○). mismatched units issued. These results Outdating rates, however, were higher under the ABO-First strategy (■) than under are primarily due to the fact that group the Age-First strategy (□). The shortage rate for A is the same for both strategies and O is the most common blood type in the is shown in Fig. 3A. donor population, while the universal plasma group AB is the least common. Because some blood banks issue PLTs without regard shortage rate is not displayed for the sake of visualization, to ABO compatibility, we also ran our simulation under an but is already displayed in Fig. 3A. The shortage rate for “Age-Only” strategy, in which the units closest to their both strategies is the same, because under both strategies, expiration are issued first, but unlike the Age-First stratunits will be issued until the entire inventory is empty. egy, no prioritization is given to ABO-identical units that As the OUTL : MDD ratio increases, the ABOni rate have the same shelf life as ABO-nonidentical units. decreases under the ABO-First strategy, but does not Because the outdate rate is determined only by the shelf change for the Age-First strategy. This confirms that the life prioritization, the Age-Only and Age-First strategies Age-First strategy, which puts low priority on ABO comhave the same outdate rates. However, the Age-Only patibility, results in higher rates of ABOni. On the other A

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excess cost per unit transfused by OUTL : MDD ratio, using an ABOni cost 15 15 of $25, outdate cost of $535.17, and shortage cost of $500, based on the con10 10 ditions used for Fig. 4A (MDD, 30 units/ 5 5 day, with an all-Caucasian recipient ABO frequency). This is the total excess 0 0 “cost” of each transfusion due to the 1.0 1.2 1.4 1.6 1.8 2.0 1.0 1.2 1.4 1.6 1.8 2.0 three negative outcomes, provided that OUTL:MDD OUTL:MDD a blood bank is willing to pay $25 to Fig. 5. Breakdown of types of ABOni for Fig. 4A using (A) an ABO-First and (B) an avoid an ABOni, $535.17 to avoid an Age-First strategy. Circles represent total ABOni, squares represent minor misoutdate (i.e., the cost of obtaining or matches only, and triangles represent minor mismatches in which the unit ABO purchasing the unit), and $500 to avoid type is O. a shortage event (when a patient’s transfusion must be delayed because no units are presently available in inventory). These values TABLE 3. Total excess cost per unit transfused were selected to illustrate a situation in which the two (in $) due to ABOni, outdates, and shortages for matching strategies resulted in fairly similar total costs. both matching strategies (described under Materials and Methods) using an MDD of 30 The total excess cost does not account for the acquisition units/day, an all-Caucasian recipient population, cost of units that were transfused or any overhead costs and the following costs per outcome: ABOni, borne by the blood bank, as these are the same across both $25; outdate, $535.17; shortage, $500* matching strategies. OUTL : MDD ABO-First cost ($) Age-First cost ($) As shown in Table 3, the optimal (lowest costing) 1.0 39.94 40.52 1.1 20.50 21.48 OUTL : MDD ratio under the ABO-First strategy is 1.4, at 1.2 10.16 11.44 an excess cost of $4.41/unit, while the optimal 1.3 5.56 6.86 OUTL : MDD ratio under the Age-First strategy is 1.7, at an 1.4 4.41 5.28 1.5 4.51 4.74 excess cost of $4.56/unit (although the costs are similar for 1.6 5.31 4.61 OUTL : MDD ratios of 1.6-2.0, due to stable ABOni and 1.7 6.62 4.56 outdate rates). As the ABO-First strategy has the lowest 1.8 8.12 4.57 1.9 10.88 4.60 cost overall, it is the optimal strategy for the given combi2.0 15.13 4.57 nation of outcome costs. Figure 6 shows the “decision * Total excess cost accounts only for costs due to these three space” using ABOni costs from $15 to $30, shortage costs outcomes and does not account for the acquisition cost of from $250 to $1000, and fixing outdate costs at $535.17. units that were transfused. Generally, a higher cost of ABOni and lower cost of shortages favor the ABO-First strategy with a midrange OUTL : MDD (1.4-1.5), while the reverse favors the AgeFirst strategy with a high OUTL : MDD (1.7-1.8). strategy was not surprisingly observed to have a much Finally, Table 4 lists the “decision point” for ABOni higher rate of ABOni. For an MDD of 30 units/day, the cost using different shortage costs, based on the condiAge-Only strategy had ABOni rates of 58% to 64% across all tions used for Fig. 4A (MDD, 30 units/day, with an allOUTL : MDD ratios, compared with 17% to 21% for the Caucasian recipient ABO frequency). The decision point Age-First strategy (data not shown). Therefore, a blood is the cost of ABOni at which the two transfusion matchbank that places any value on preventing ABOni would ing strategies result in the same optimal cost per unit; a prefer an Age-First strategy over an Age-Only strategy. higher ABOni cost than the decision point favors the ABO-First strategy, whereas a lower ABOni cost favors the Age-First strategy. The decision point does not vary much Cost-effectiveness analysis with widely different shortage costs, suggesting that if a blood bank with an MDD of 30 units/day is willing to pay Because shortages are unacceptably high at low $19 to $26 to prevent an ABOni, then using an ABO-First OUTL : MDD ratios (Figs. 2 and 3), a blood bank would be strategy would be cost-effective. However, for smaller expected to use a midrange or high OUTL : MDD ratio, blood banks (MDD of 5 or 10 units/day), the decision where outdating becomes a risk. Therefore, it is necessary point for ABOni cost is much higher, suggesting that to perform a cost-effectiveness analysis to calculate and these blood banks must be willing to spend more compare the combined costs of ABOni, outdates, and to prevent an ABOni or otherwise accept an Age-First shortages, to determine the optimal matching strategy strategy. and OUTL : MDD ratio. Table 3 shows the combined A

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Fig. 6. “Decision space” grid displaying the most cost-effective strategies for the results in Fig. 4A (MDD, 30 units/day, with an all-Caucasian recipient population) using a “cost” of $535.17 for an outdate, $250 to $1000 for a shortage, and $15 to $30 for an ABOni. Boxes shaded in gray represent combinations for which the ABO-First strategy is most cost-effective, while unshaded boxes represent combinations for which the Age-First strategy is most cost-effective. The number inside the box is the optimal OUTL : MDD ratio for the most costeffective strategy.

TABLE 4. Decision points for ABOni cost using different shortage costs (in $) and different MDDs, with an all-Caucasian recipient population* Shortage cost ($) 250 500 750 1000

Decision point for ABOni ($) MDD of MDD of MDD of 5 units/day 10 units/day 30 units/day 104.30 53.06 19.01 115.55 66.27 23.31 115.55 77.17 25.40 123.22 82.28 26.30

* A blood bank willing to pay more than the decision point to prevent an ABOni should use an ABO-First strategy, while one not willing to pay more should use an Age-First strategy. In general, the optimal OUTL : MDD ratio was lower for the ABOFirst strategy than for the Age-First strategy, as exemplified in Fig. 6.

DISCUSSION The purpose of this study, rather than define the risks associated with ABOni, is to provide hospital blood banks with a tool to determine the best strategy to prevent ABOni without an unacceptably costly effect on inventory management. According to the model, a large blood bank willing to spend about $25 or more to prevent an ABOni would be best served by a protocol that prioritizes ABOni over the standard “first in, first out” (FIFO) protocol, in which products nearest to their expiration date are preferentially used to fill a transfusion request. The slight increase in outdating would be balanced by the large 354 TRANSFUSION Volume 55, February 2015

decrease in ABOni. On the other hand, a smaller blood bank must be willing to spend much more to prevent an ABOni to make the effort worthwhile, as much as $125 or more for a blood bank with an annual utilization of fewer than 2000 PLT units. Although the decision point cost of ABOni is small per unit for a large blood bank, it may be considered large in aggregate, and individual transfusion services may consider ABOni to be of insignificant enough risk to justify the increased outdate costs. The decision on how to “price” the avoidance of ABOni must take into account the clinical consequences reported in the literature. For instance, Pavenski and coworkers3 reported that the median corrected count increment (CCI) in noncancer patients receiving an ABO major-mismatched PLT transfusion was 15.2, compared with 18.6 for an ABO-compatible transfusion. A major-mismatched transfusion may therefore be considered inferior to a compatible transfusion by a proportion of (18.6-15.2)/18.6 = 18%, and if a blood bank pays $535.17 to purchase or obtain a PLT unit, then it would be willing to spend 18% of $535.17, or $98, to avoid a majormismatched transfusion. However, the effect on clinical bleeding of major mismatches with lower CCI has been shown to be marginal.4 Similarly, determination of the cost of a minormismatched PLT transfusion would need to take into account the clinical costs associated with the risk of transfusion reactions, including the rare risk of hemolytic reactions.9-12 This cost may be mitigated by the use of strategies to reduce the volume of ABO-incompatible plasma in a minor-mismatched transfusion, such as volume reduction or avoiding transfusion of plasma with high-titer ABO antibodies, although these strategies themselves may impact inventory management. While we varied the cost of ABOni and shortages, we set the outdate cost at $535.17, which is the average acquisition cost for apheresis PLT products reported in a national survey.22 A transfusion service that has a lower acquisition cost than $535.17 would have a lower decision point for ABOni cost, due to the relatively lower cost of outdating, thus making it more likely that the ABO-First strategy would be optimal. Our model was developed using a virtual blood bank, a computer program in which the basic processes of a blood bank, stocking and releasing products to meet transfusion demand, can be simulated without the cost, time, or resources required to determine the consequences of different transfusion strategies in a real blood bank. Our model produces results that would be intuitively expected in real life, as evidenced by Figs. 2 and 3: 1) maintaining a larger blood product inventory leads to higher outdating but fewer shortages; 2) serving a smaller population with low transfusion demand (MDD) makes it more difficult to manage inventories, leading to both more outdates and shortages; and 3) serving a recipient Volume **, ** **

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population with ABO type frequencies different from those in the donor population leads to more outdates and shortages. Additionally, a comparison of our model with experience at our institution suggests that our virtual blood bank provides a reasonable approximation to a real hospital blood bank. Our model was designed to be as simple as possible to answer the desired question, and thus it does not account for certain complicated transfusion issues that blood banks may encounter, including older products being added to inventory with less than 3 days of remaining shelf life, transfusion of D– recipients with only D– PLT products, the discarding of units for reasons other than outdating (e.g., improper storage), and meeting orders for more than 1 unit at the same time for the same recipient (which violates the requirement of independent transfusion events under the Poisson model). Despite these limitations, we feel that they would have minimal effects on addressing the purpose of this particular study, since we are comparing two transfusion strategies under the same conditions. While our base model assumes that the MDD is uniform over all days of the week, experience at our institution has shown that this is not necessarily true for real transfusion services. Utilization of PLT units by our hospital was 54% higher on weekdays (Monday through Friday) than on weekends (Saturday and Sunday). We adjusted the MDD and OUTL by day of week for our validation model based on actual transfusion events at our hospital and found the results to be reasonably predictive and fairly similar to our base model with a similar overall MDD (i.e., comparing Figs. 4A and 4B). Our model is highly customizable, so that features can be added or changed to address numerous questions in blood bank inventory management, particularly concerning the inverse relationship between outdating and shortages. The model can be made to approximate a real blood bank’s processes as much as is practical and can then be perturbed in different ways to assess impact on clinical outcomes and cost. It is our intention to use this model to address future challenges that blood banks may face. ACKNOWLEDGMENTS We thank Drs Brenda J. Grossman and Charles Eby for helpful discussions. CONFLICT OF INTEREST The authors have disclosed no conflicts of interest.

REFERENCES 1. Dunbar NM, Ornstein DL, Dumont LJ. ABO incompatible platelets: risks versus benefit. Curr Opin Hematol 2012;19: 475-9. 8

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2. Shehata N, Tinmouth A, Naglie G, et al. ABO-identical versus nonidentical platelet transfusion: a systematic review. Transfusion 2009;49:2442-53. 3. Pavenski K, Warkentin TE, Shen H, et al. Posttransfusion platelet count increments after ABO-compatible versus ABO-incompatible platelet transfusion in noncancer patients: an observational study. Transfusion 2010;50:155260. 4. Triulzi DJ, Assmann SF, Strauss RG, et al. The impact of platelet transfusion characteristics on posttransfusion platelet increments and clinical bleeding in patients with hypoproliferative thrombocytopenia. Blood 2012;119:555362. 5. Julmy F, Ammann RA, Taleghani BM, et al. Transfusion efficacy of ABO major-mismatched platelets (PLTs) in children is inferior to that of ABO-identical PLTs. Transfusion 2009;49:21-33. 6. Heal JM, Rowe JM, McMican A, et al. The role of ABO matching in platelet transfusion. Eur J Haematol 1993;50: 110-7. 7. Carr R, Hutton JL, Jenkins JA, et al. Transfusion of ABOmismatched platelets leads to early platelet refractoriness. Br J Haematol 1990;75:408-13. 8. Lin Y, Callum JL, Coovadia AS, et al. Transfusion of ABOnonidentical platelets is not associated with adverse clinical outcomes in cardiovascular surgery patients. Transfusion 2002;42:166-72. 9. Fung MK, Downes KA, Shulman IA. Transfusion of platelets containing ABO-incompatible plasma: a survey of 3156 North American laboratories. Arch Pathol Lab Med 2007; 131:909-16. 10. Fontaine MJ, Mills AM, Weiss S, et al. How we treat: risk mitigation for ABO-incompatible plasma in plateletpheresis products. Transfusion 2012;52: 2081-5. 11. Josephson CD, Castillejo MI, Grima K, et al. ABOmismatched platelet transfusions: strategies to mitigate patient exposure to naturally occurring hemolytic antibodies. Transfus Apher Sci 2010;42:83-8. 12. Menis M, Izurieta HS, Anderson SA, et al. Outpatient transfusions and occurrence of serious noninfectious transfusion-related complications among US elderly, 20072008: utility of large administrative databases in blood safety research. Transfusion 2012;52:1968-76. 13. Mair B, Benson K. Evaluation of changes in hemoglobin levels associated with ABO-incompatible plasma in apheresis platelets. Transfusion 1998;38:51-5. 14. Blumberg N, Heal JM, Hicks GL Jr, et al. Association of ABO-mismatched platelet transfusions with morbidity and mortality in cardiac surgery. Transfusion 2001;41: 790-3. 15. Refaai MA, Fialkow LB, Heal JM, et al. An association of ABO non-identical platelet and cryoprecipitate transfusions with altered red cell transfusion needs in surgical patients. Vox Sang 2011;101:55-60.

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16. American Association of Blood Banks. Standards for blood banks and transfusion services. 28th ed. Bethesda (MD): American Association of Blood Banks; 2012. p. 35. 17. College of American Pathologists. Transfusion medicine checklist, version 01.04.2012. Northfield (IL): College of American Pathologists; 2011. p. 31-2. 18. Quillen K, Sheldon SL, Daniel-Johnson JA, et al. A practical strategy to reduce the risk of passive hemolysis by screening plateletpheresis donors for high-titer ABO antibodies. Transfusion 2011;51:92-6. 19. Henrichs KF, Howk N, Masel DS, et al. Providing ABO-

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patients: approach, logistics, and associated decreases in transfusion reaction and red blood cell allommunization incidence. Transfusion 2012;52:635-40. 20. Garratty G, Glynn SA, McEntire R. ABO and Rh(D) phenotype frequencies of different racial/ethnic groups in the United States. Transfusion 2004;44:703-6. 21. DeGroot MH. Probability and statistics. 2nd ed. Reading (MA): Addison-Wesley Publishing; 1986. 22. Department of Health and Human Services. The 2011 national blood collection and utilization survey report. Washington, DC: DHHS, 2013, p. 56.

identical platelets and cryoprecipitate to (almost) all

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Comparison of two platelet transfusion strategies to minimize ABO-nonidentical transfusion, outdating, and shortages using a computer-simulated "virtual blood bank".

Transfusion of ABO-incompatible platelets (PLTs) is associated with reduced PLT recovery and a risk of transfusion reactions. However, a policy of tra...
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