Optimizing Preoperative Blood Ordering with Data Acquired from an Anesthesia Information Management System

THE decision to order blood typing and red cell antibody screening (T/S) or a type and crossmatch (T/C) before surgery can be controversial, especially for cases with a low or intermediate likelihood of transfusion. Several variables must be considered when making this decision, including the anticipated blood loss for a given procedure, the preoperative hemoglobin concentration, and the relative risk of transfusing emergency-release type-O blood during unexpected hemorrhage when preoperative T/S or T/C are unavailable. Previous investigations have led to proposed blood ordering protocols, but often, these protocols are designed for a single type of surgical procedure.^1–7  Ideally, a blood order algorithm could be developed, which would cover most types of procedure.

A recommended maximum surgical blood order schedule (MSBOS) for common surgical procedures was published more than 30 yr ago by Freidman,⁸  but many new procedures have since been introduced, and surgical techniques have evolved such that blood loss is now less common. Ideally, blood ordering protocols should be based on institution-specific, recent historical blood utilization data, which can be difficult to obtain. We have previously described methods for collecting such data with an anesthesia information management system (AIMS),⁹  and two recent publications by Cheng et al.¹⁰  and Dexter et al.¹¹  have proposed using AIMS-acquired data to guide preoperative blood ordering based on specific criteria. However, neither of these studies developed an MSBOS based on their recommended criteria, nor did they define criteria specific enough to create an MSBOS.

The purpose of the current study was to create an institution-specific MSBOS by using AIMS-acquired data along with a blood order algorithm. In addition, we sought to identify specific low–blood-loss procedures, for which we could eliminate blood orders, and thereby substantially reduce costs.

After receiving Institutional Review Board approval (The Johns Hopkins Medical Institutions, Baltimore, Maryland), we extracted and analyzed data from our institution’s AIMS (Metavision; iMdSoft, Needham, MA) to assess transfusion requirements and preoperative blood orders. We collected data from a 27-month period (January, 2010–March, 2012) that included 76,106 patients having 1,909 types of surgical procedures. A detailed account of our methods for data extraction from the AIMS system, as well as data validation, have been described previously.⁹ 

We excluded 22,586 cases, in which patients were under 18 yr old or had ophthalmic surgery, leaving 53,526 surgical patients having 1,632 types of surgical procedures for the analysis. We excluded pediatric patients because in children, blood is often transfused according to body mass (ml/kg) and is not measured in units transfused. Therefore, transfusion data from adults and children are not comparable. Ophthalmic surgery was excluded because these patients never receive blood transfusions.

Each surgical procedure was then grouped into one of 135 different procedure categories determined by surgical subspecialty and relevant anatomical operative site. For patients having combined procedures, the primary posted procedure was used for categorization purposes. For the purposes of grouping procedures into categories, we considered the percentage of patients transfused for procedures that could potentially fall under more than one category. Procedures with similar transfusion rates (±5%) were grouped together; otherwise a new procedure category was created. Two investigators (Drs. Frank and Rothschild) carried out the grouping process. Details of category assignments for every procedure, for each of the nine surgical services, can be accessed electronically (see Supplemental Digital Content 1, https://links.lww.com/ALN/A924, which is a table outlining the categorization of all surgical procedures).

For each procedure category, we analyzed the following variables: number of patients transfused with erythrocytes; number of units of erythrocytes transfused; median estimated blood loss (EBL); and transfusion index^3,12  (total number of erythrocyte units transfused divided by the total number of patients having a particular procedure). The number of patients who had a preoperative T/S and/or T/C ordered was also recorded. The T/C–to–transfusion ratio was calculated as the number of patients who had a T/C ordered, divided by the number of patients who were transfused with erythrocytes. The T/S–to–transfusion ratio was calculated as the number of patients who had a T/S or T/C ordered, divided by the number of patients who were transfused with erythrocytes. We used this definition because every T/C includes a T/S as well.

Using previously proposed criteria, we developed an algorithm (fig. 1) to determine the appropriate preoperative blood order for each procedure category. These criteria included: 5% or more of patients transfused with erythrocytes;^11,13,14  median EBL more than 50ml;¹¹  and a transfusion index 0.3 or more.¹⁵  An additional parameter, which we called “risk of major bleeding,” was assigned to each procedure category as either “yes” or “no” for the purposes of the algorithm. This risk was determined by consensus among the investigators after discussion with surgeons by considering the proximity of surgery to large blood vessels, and thus, the potential for major surgical bleeding. A detailed description of the computer code used to create and execute this algorithm can be accessed electronically (see Supplemental Digital Content 2, https://links.lww.com/ALN/A925, which outlines the specific computer code used in the algorithm).

The three primary blood orders determined by the algorithm were “no T/S or T/C”, “T/S”, and “T/C”. The minimum number of T/C units was two (2U). If four or more units of erythrocytes were transfused in more than 10% of patients, then the recommendation was a T/C for four units (4U), and for the category of procedures considered to be “major vascular or transplant,” the recommendation was a T/C of six to 15 units (6–15U). For example, this category included liver transplants and thoracoabdominal aortic aneurysms.

The value for EBL was missing for 53% of cases in the anesthesia record. For these cases, a value of zero was assumed, as described and justified by Dexter et al.¹¹  These investigators explain in detail that this method is valid because most cases with missing values are those in which blood loss was minimal and transfusion did not occur. Because AIMSs do not allow text entries in a numeric field (e.g., minimal), most providers choose not to enter a value when blood loss is very low. Furthermore, because EBL is known to be a somewhat unreliable measurement,¹⁶  we included the parameters transfusion index and percentage of patients transfused in the algorithm. EBL is not routinely measured in cardiac surgery cases, and all patients who undergo cardiac surgery are at risk for major bleeding. Therefore, we omitted EBL in the analysis and assigned all cardiac surgery procedures, except for automatic implantable cardiac defibrillator/pacemaker placement, to the T/C group, according to the algorithm (fig. 1).

We compared the T/C–to–transfusion ratio and the T/S–to–transfusion ratio among the nine surgical services to identify services with the highest rates of overordering. We also performed an analysis to calculate the reduction in hospital charges, as well as actual cost savings that could be achieved by eliminating all T/S and T/C tests that were ordered at our institution for patients having procedures determined by the algorithm to be in the “no T/S or T/C” category, and by eliminating any T/C tests that were ordered for cases assigned to the “T/S” category. We used the current laboratory charge at our institution for T/S and T/C, which were $37 and $52, respectively. Actual costs for these tests, taking into account reagents, technician time, laboratory equipment maintenance, and depreciation were calculated to be $7.56 and $10.61 for each T/S and T/C, respectively. Hospital charge and actual cost reductions were proportionally adjusted for the 27-month period of data collection, to determine these values in U.S. dollars per year. The two-tailed hypothesis tested was that blood ordering guided by the MSBOS would have a financial impact as measured by patient charges and actual costs.

All data were processed and analyzed with the software programs Excel, v. 14.1.0 (Microsoft Inc., Redmond, WA) and JMP, v. 9.0.0 (SAS Institute, Cary, NC). Comparisons were made by ANOVA and chi-square tests where appropriate. Nonparametric data were compared by the Kruskal–Wallis test. The blood order algorithm was constructed using JMP. Data are given as mean ± SD or median (interquartile range) as appropriate. P value less than 0.05 defined significance.

The 135 categories of surgical procedures were assigned to one of five blood order groups, according to the algorithm outlined in figure 1, and the results of these assignments are shown in table 1. Fifty two percent of patients had surgical procedures that met criteria for the “no T/S or T/C” recommendation, 8.7% had procedures that met criteria for the “T/S” recommendation, 33.9% had procedures that met criteria for the “T/C 2U” recommendation, 5.2% had procedures that met criteria for the “T/C 4U” recommendation, and 0.2% had procedures that met criteria for the “T/C 6-15U” recommendation. The percentage of patients transfused, median EBL, and transfusion index were all greater in the “T/C” groups than in the “T/S” group, and greater in the “T/S” group than in the “no T/S or T/C” group (P < 0.01 for all differences among the groups for these parameters). Of 27,825 patients who were determined as not requiring any blood orders, 9,099 (32.7%) had a T/S ordered, and 2,643 (9.5%) had a T/C ordered (table 1). Of 4,644 patients that were determined to need a T/S only, 1,509 (32.5%) had a T/C ordered.

The 135 categories of surgical procedures and the associated blood order recommendations, along with detailed transfusion data, are shown in appendix 1, tables 2–10. Each of these tables represents the MSBOS for a particular surgical service. Surgical procedures that were noted to have extremely low transfusion rates and very high T/C– and T/S–to–transfusion ratios were: breast procedures, thyroid/parathyroid procedures, cerebrospinal fluid shunt procedures, cystoscopy/ureter/urethral procedures, robotic prostate/kidney/adrenal procedures, robotic or laparoscopic uterus/ovary procedures, open shoulder procedures, and peripheral nerve procedures.

We compared the T/S–to–transfusion ratios and the T/C–to–transfusion ratios among the nine surgical services (fig. 2). The services with the highest ratios were those that potentially had the greatest number of unnecessary preoperative blood orders. All surgical services, except for cardiac and vascular/transplant, had T/C–to–transfusion ratios of 3 or greater. A ratio of 2 has been proposed as optimal.^3,17 

The economic implications of following the recommended blood orders are shown in figure 3. The greatest potential savings would be realized by the general surgery, otolaryngology, neurosurgery, and urology services. By using the MSBOS to eliminate unnecessary blood orders, we calculated a potential reduction in hospital charges and actual costs of $211,448 and $43,135 per year, respectively. When calculated for all surgical patients as the denominator, this change in practice would reduce hospital charges and actual costs by $8.89 and $1.81 per patient, respectively.

A summary of the recommended preoperative blood orders for each surgical procedure category organized by surgical service, is shown in appendix 2 (fig. 4). These recommendations are derived from data in appendix 1, tables 2–10, and represents our institution-specific surgical blood order schedule, which has been implemented into practice.

Using AIMS-acquired data, we were able to collect detailed information on transfusion requirements for a large number of surgical procedures, while taking into account institution-specific factors. Before the adoption of the current AIMS at our institution, the development of an MSBOS based on detailed and accurate blood utilization data would have been prohibitive. However, with the AIMS, we were able to apply an algorithm based on previously published parameters^3,11,13–15  to create an institution-specific MSBOS. We then were able to identify specific procedures with very low transfusion rates, for which preoperative blood orders for T/S and T/C were considered unnecessary. If these blood orders were eliminated, significant reduction in charges and costs would be achieved. In addition, it is likely that following the MSBOS recommendations would improve operating room efficiency and patient safety because the blood bank staff often faces the challenge to process multiple blood samples simultaneously on the day of surgery. As a result, the start of cases can be delayed,¹⁸  and/or surgical cases begin without blood available,¹⁸  a practice which is contrary to the Joint Commission (JCAHO) recommended guidelines.¹⁹  Furthermore, patients who truly require blood readily available would also benefit from MSBOS-guided blood ordering.

Creating an MSBOS is much more complex than it was three decades ago. Procedures now have many more variations, often with differing bleeding risks and transfusion requirements. The complexity of creating a modern, universal MSBOS list is evident when one considers that 1,909 different surgical procedures were performed at our institution in the past 3 yr, in contrast to the 63 procedures from Friedman 1979 MSBOS list.⁸  The original MSBOS was released before the advent of laparoscopic and robotic techniques, which as shown by our data, are clearly associated with lower transfusion requirements than traditional surgical techniques. The increased use of cell salvage, hemostatic agents, and new methods of cautery for certain procedures has also decreased transfusion requirements.^20,21  Although the original MSBOS was derived by collecting transfusion data from multiple institutions,⁸  our opinion is that blood ordering decisions should be institution-specific, procedure-specific, and perhaps even surgeon-specific, all of which are possible using AIMS-acquired data.

Dexter et al.¹¹  and Cheng et al.¹⁰  have recently described using AIMS-acquired data to guide the process of preoperative blood ordering. However, our methods differ from theirs. In their “letter to the editor”, Cheng et al. used the transfusion index (average number of erythrocyte units transfused per patient) as the sole criterion for determining whether a procedure warrants a T/C. If the transfusion index was 0.5 or more, they recommended a T/C, and if not, they recommended a T/S. Using these criteria, even procedures that are rarely or never transfused would have unnecessary T/S tests ordered. In the study by Dexter et al.,¹¹  two parameters were used to determine whether a T/S was necessary—median EBL and percentage of patients transfused. EBL is known to be relatively inaccurate,^16,22  but was one of the primary determinates in their study. Our algorithm is based on a greater number of measured parameters and includes indications not just for T/S, but for T/C as well.

On the basis of MSBOS recommendations, we found that a substantial number of preoperative blood orders were unnecessary. One explanation for the overordering of blood products for surgery is that many providers are unfamiliar with certain procedures and the associated blood requirements. Another reason for overordering may be that multiple providers are allowed to order blood (surgeons, anesthesia providers, and advance practice nurses). If any one of these individuals ascribes to the “better-safe-than-sorry” approach, it is likely that blood will be ordered unnecessarily. An estimated reduction in hospital charges of over $200,000 per year at our institution would be achieved if unnecessary blood orders were eliminated. Because the actual costs of performing these tests are known to differ from hospital charges,²³  we calculated the true cost savings that could be recognized, which was approximately 20% of the hospital charges.

Another indication of excessive blood ordering is the T/C–to–transfusion ratio. The ideal ratio has been described as 2:1,^3,4,17  and a ratio above this is thought to represent excessive blood ordering. In our institution, only the cardiac surgery service was close to the ideal ratio, and the ratios for the other services were between 3:1 and 5:1. In addition to this ratio, we assessed the T/S–to–transfusion ratio, which has not been previously reported. When considering unnecessary blood orders, we believe this ratio is also useful for estimating utilization of blood bank resources. These ratios can also be used to modify subsequent releases of the MSBOS as surgical procedures are refined and changes in transfusion rates are recognized. Another advantage of using AIMS-acquired data is the ability to compare surgical services, or even individual medical providers, to determine where efforts should be focused to reduce excessive blood orders.

An issue that is rarely discussed is the relative safety of using emergency-release type-O blood as a backup plan when unexpected bleeding is encountered and no blood products are available. The risk of a major, acute, ABO group hemolytic reaction should be zero, barring clerical error and the wrong unit being given (which would be the same risk as for crossmatched units).^24,25  Dutton et al.²⁶  reported no acute hemolytic reactions in 161 patients given 581 units of uncrossmatched, type-O erythrocytes. The risk of a delayed, slower onset reaction has been described as one in 1,000 units of emergency-release blood.²⁷  This type of reaction is usually mild, slower in onset (4–21 days), associated with a mild increase in bilirubin, and often goes undetected. Thus, if a given case (thyroidectomy for example) has a two in 1,000 chance of transfusion, the calculated risk of a mild delayed reaction would be approximately two in a million (2:1,000×1:1,000) in the typical patient presenting for this procedure without blood orders.

Patient blood management has seen an increased level of attention in the past few years.^28–30  Although much focus has been on reducing the incidence of unnecessary transfusion, reducing unnecessary preoperative blood orders is also part of the effort to conserve blood. Blood management encompasses allocating resources appropriately and decreasing the unnecessary use of those resources. Using AIMS-acquired data to create an institution-specific MSBOS is an example of patient blood management, which we believe should be incorporated into a hospital’s blood management program. The utility of AIMS-acquired data for guiding a blood management program has also been described in our previous study, where providers were compared with their peers in terms of blood utilization and hemoglobin transfusion triggers, an exercise that has proven useful in our institution.⁹ 

One factor not accounted for in our algorithm is the preoperative hemoglobin concentration. Previous studies have accounted for preoperative hemoglobin, when describing surgical blood order equations^1,7  and other transfusion predictors,^5,13,14  which are used to determine the need for T/C. Hemoglobin concentration should be considered, and our MSBOS can be modified for patients with anemia. Ideally, multiple factors should be considered when ordering blood—the preoperative hemoglobin, the planned surgical procedure, and medical need to transfuse based on coexisting conditions, such as cardiovascular disease.

Our study has certain limitations that should be recognized. First, some common surgical procedures are infrequently performed in our hospital because they are performed at an affiliated hospital where an AIMS is not used. Hip and knee arthroplasty, both primary and revision, are such procedures that are underrepresented in our database. Most physicians, however, would agree that these orthopedic procedures do require a T/C, except for perhaps the primary total knee arthroplasty, which our algorithm assigned to the T/S category. Second, the blood order algorithm is based solely on intraoperative transfusion requirements and does not account for postoperative transfusions. Certain procedures (hip arthroplasty and multilevel lumbar spine fusions) are associated with significant postoperative blood loss that would justify the T/C, despite the intraoperative blood requirements. Because these types of cases were determined as requiring a T/C, the results would be unchanged, if we accounted for postoperative bleeding. Lastly, we report data from only one institution, which may or may not represent blood requirements in other institutions. However, any center with an AIMS could use our algorithm to create an institution-specific MSBOS, similar to ours in appendix 2, figure 4.

In conclusion, we have shown that an MSBOS created with AIMS-acquired data can be used to optimize the process of preoperative blood ordering. As a result of this analysis, we plan to implement preoperative blood ordering protocols that eliminate or decrease unnecessary blood orders. In addition, these protocols should reduce the number of cases with missing blood orders, when these orders are truly indicated. Implementing the algorithm and the MSBOS that we have described offers the potential to improve operating room efficiency, enhance patient safety, and reduce costs.

The authors thank Claire Levine, M.S., E.L.S. (Manager, Editorial Services, Department of Anesthesiology/Critical Care Medicine, Johns Hopkins Medicine, Baltimore, Maryland), for editorial assistance, and Sharon Paul, B.S., M.S. (Systems Development Manager, Department of Anesthesiology/Critical Care Medicine, Johns Hopkins Medicine), for assistance with data acquisition. The authors also thank Joan S. Boyd, M.T.(A.S.C.P.)S.B.B. (Manager, Transfusion Medicine Division, Department of Pathology, Johns Hopkins Medicine, Baltimore, Maryland), Allen Valentine, M.B.A., C.(A.S.C.P.) (Administrative Director Medical Laboratories, Department of Pathology, Johns Hopkins Medicine), and Christy Marshall, M.S. (Pathology Laboratory Supervisor, Department of Pathology, Johns Hopkins Medicine), for assistance in accessing the blood bank’s database.

Using data acquired from the anesthesia information management system and the algorithm shown in figure 1, the following MSBOS lists were created.

An MSBOS list was generated for each surgical specialty service, and recommendations were given for preoperative blood orders for each category of surgical procedure.