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Multimodal Patient Blood Management Program Based on a Three-pillar Strategy

A Systematic Review and Meta-analysis

Althoff, Friederike C.*; Neb, Holger, MD*; Herrmann, Eva, PhD; Trentino, Kevin M.; Vernich, Lee§; Füllenbach, Christoph, PhD*; Freedman, John, MD; Waters, Jonathan H., MD||; Farmer, Shannon, MD**,††; Leahy, Michael F., MD‡‡; Zacharowski, Kai, MD, PhD*; Meybohm, Patrick, MD*; Choorapoikayil, Suma, PhD*

doi: 10.1097/SLA.0000000000003095
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Objectives: To determine whether a multidisciplinary, multimodal Patient Blood Management (PBM) program for patients undergoing surgery is effective in reducing perioperative complication rate, and thereby is effective in improving clinical outcome.

Background: PBM is a medical concept with the focus on a comprehensive anemia management, to minimize iatrogenic (unnecessary) blood loss, and to harness and optimize patient-specific physiological tolerance of anemia.

Methods: A systematic review and meta-analysis was performed. Eligible studies had to address each of the 3 PBM pillars with at least 1 measure per pillar, for example, preoperative anemia management plus cell salvage plus rational transfusion strategy. The study protocol has been registered with PROSPERO (CRD42017079217).

Results: Seventeen studies comprising 235,779 surgical patients were included in this meta-analysis (100,886 pre-PBM group and 134,893 PBM group). Implementation of PBM significantly reduced transfusion rates by 39% [risk ratio (RR) 0.61, 95% confidence interval (CI) 0.55–0.68, P < 0.00001], 0.43 red blood cell units per patient (mean difference −0.43, 95% CI −0.54 to −0.31, P < 0.00001), hospital length of stay (mean difference −0.45, 95% CI −0.65 to −0.25, P < 0,00001), total number of complications (RR 0.80, 95% CI 0.74–0.88, P <0.00001), and mortality rate (RR 0.89, 95% CI 0.80–0.98, P = 0.02).

Conclusions: Overall, a comprehensive PBM program addressing all 3 PBM pillars is associated with reduced transfusion need of red blood cell units, lower complication and mortality rate, and thereby improving clinical outcome. Thus, this first meta-analysis investigating a multimodal approach should motivate all executives and health care providers to support further PBM activities.

*Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Frankfurt, Germany

Institute of Biostatistics and Mathematical Modelling, Goethe University Frankfurt, Frankfurt, Germany

Data and Digital Innovation, East Metropolitan Health Service, Perth, Western Australia

§Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

St Michael's Hospital, University of Toronto, Toronto, Canada

||Department of Anesthesiology and Bioengineering, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA

**Medical School, CTEC and Division of Surgery, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia

††Faculty of Health Sciences, Centre for Population Health Research, Curtin University, Perth, Western Australia, Australia

‡‡Department of Hematology, School of Medicine and Pharmacology, PathWest Laboratory Medicine Royal Perth Hospital, The University of Western Australia, Perth, Western Australia.

Reprints: Patrick Meybohm MD, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany. E-mail: patrick.meybohm@kgu.de; Suma Choorapoikayil, PhD, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany. E-mail: suma.choorapoikayil@kgu.de.

F.C.A. and H.N. contributed equally to this work. P.M. and S.C. are sharing senior authorship.

Declaration: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf. P.M. and K.Z. received grants from B. Braun Melsungen, CSL Behring, Fresenius Kabi, and Vifor Pharma for the implementation of Frankfurt‘s Patient Blood Management program and honoraria for scientific lectures from B. Braun Melsungen, Vifor Pharma, Fearing, CSL Behring, and Pharmacosmos. S.F. reports personal fees from Thieme (Stuttgart, Germany) and Elsevier Science, USA, and nonfinancial support from the National Blood Authority (Australia), the Medical Society for Blood Management, and The Health Roundtable, outside the submitted work. J.H.W. is on the Advisory Board for Haemonetics, Inc (Braintree, Massachusetts) and is a consultant for LivaNova (London, UK). The remaining authors report no conflicts of interest.

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Patient Blood Management (PBM) is a patient-centered interdisciplinary approach with the timely application of evidence-based medical and surgical interventions designed to maintain patients own blood mass. Based on the possibilities to strengthen and to preserve patients’ own blood mass and to enable safe handling of donor blood, the World Health Assembly (WHA 63.12)1 endorsed PBM, requesting the World Health Organization to provide its member states with training on the safe and rational use of allogeneic blood products and implementation of transfusion alternatives in 2010.

Many PBM programs have evolved incrementally; however, PBM is most successful when multiple interventions are combined. Goodnough, Shander, and coworkers have organized these strategies into 3 main pillars: (1) comprehensive anemia management; (2) minimization of iatrogenic (unnecessary) blood loss; and (3) harness and optimize the patient-specific physiological tolerance of anemia.2–4 So far, >100 individual PBM measures have been defined based on the broad interdisciplinary fields and temporal application.5 Even though the body of evidence is constantly growing,6–10 only few institutions adopted measures of all 3 pillars. A possible explanation could be the lack of clinical studies investigating the overall clinical and cost effectiveness of a multimodal PBM program. A PBM monitoring and feedback program at the University Hospital of Zurich for example, resulted in a reduction of allogeneic blood transfusions of 27% with savings of direct transfusion costs of $ 84 per inpatient which yielded in >§2,000,000 per year.11 A health economic model representing probabilities of complications and consequential mortality rates with a simulated cohort of 10,000 randomized surgical patients showed that savings per avoided complication and death were € 16,318 and € 70,140 for noncardiac and € 14,139 and € 57,882 for cardiac surgical patients.12 The objective of this meta-analysis was to determine whether a multimodal PBM program addressing all 3 main pillars in patients undergoing surgery is effective in improving clinical outcome.

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METHODS

Search Strategy and Selection Criteria

In this systematic review and meta-analysis, we systematically searched Cochrane Library and Medline (PubMed) databases for eligible studies investigating the effectiveness of an implemented PBM program published between January 1, 2000 and December 31, 2017. The following MeSH terminology was used to identify eligible studies for our meta-analysis: Patient Blood Management” or “restrictive transfusion strategy” or “transfusion rate” and “anemia” or “red blood cell transfusion” or “mortality” or “patient safety” or “acute kidney injury” or “myocardial infarction.” Two independent authors (F.C.A., H.N.) extracted data; discrepancies between the surveyors were resolved through consensus by joint discussion with 2 additional reviewers (S.C., P.M.). Articles were screened on title and abstract using EndNote X8 software. In addition, grey literature and reference lists of identified articles were hand searched. We focused on a selected bundle of PBM measures: (1) preoperative anemia management including treatment of iron and other hematinic deficiencies; (2) strategies to reduce blood loss and bleeding including blood-sparing surgical techniques, perioperative autologous blood collection and retransfusion (cell salvage), use of pharmacological/hemostatic agents (antifibrinolytic) and/or hemostasis management (point-of-care diagnostic); and (3) restrictive blood transfusion practice complying with guidelines for transfusion triggers. Eligible studies had to address each of the 3 main PBM pillars with at least one measure per pillar for example preoperative anemia detection and treatment plus cell salvage plus rational transfusion strategy (Supplemental table content 1, http://links.lww.com/SLA/B525).5,13 It is, however, important to notice that the individual pillars are within themselves multimodal–not monomodal.14 For instance, the rational transfusion strategy is not the only modality within the third pillar. Studies were included if they used either an observational design with independent cohorts of patients before (pre-PBM) and after (PBM) implementation of a multimodal PBM or blood conservation program or randomly assigned patients into an experimental PBM group and a control group receiving standard care (pre-PBM). When comparative analysis of a multiannual implementation spanned more than 2 periods in observational trials, baseline data were extracted for the pre-PBM cohort and data of the final year were assigned to the PBM cohort, considering that time is needed to fully comply with new standards. Furthermore, if studies comprised out- and inpatients or additional endpoints that were not considered in our meta-analysis, only data of surgical patients and respective endpoints were extracted for this meta-analysis. There were no exclusion criteria on age or type of surgery. Studies were excluded if only Jehovah's Witnesses (who decline allogeneic transfusion for religious reasons) served as a PBM group (bloodless group) or if single or too few PBM interventions were compared to a control group.

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Outcome Measures

Endpoints were transfusion rate (number of patients transfused), transfused red blood cell (RBC) units per patient, length of hospital stay (LOS), total number of complications (Supplemental data content 1, http://links.lww.com/SLA/B525), and mortality.

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Data Extraction and Statistical Analysis

The present study was conducted in accordance to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-analyses15 and in accordance to the MOOSE checklist.16 Authors were contacted via (e-) mail if clarification of data or additional information were required.

Statistical analysis and graphical illustrations were performed using Review Manager 5.3 (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, 2014).

We collated exposed number and total number of patients for dichotomous data such as transfusion rate and clinical outcomes, and mean and standard deviation (SD) for continuous data including RBC units per patient and LOS. RBC units reported for the entire cohort were converted to per-patient data by dividing by the total number of subjects. Risk ratio (RR) and 95% confidence intervals (CIs) were calculated for dichotomous and raw mean difference (MD) and 95% CI for continuous data. The presence of heterogeneity was detected by Cochran Q- and χ2-tests and I2 statistic. A test for subgroup differences using χ2-test (Cochrane Q) and I2 statistic was conducted to compare subtotal estimates between the subgroups and to examine source of heterogeneity. I2 values >75% were considered as high heterogeneity. Effects were summarized applying the random effects model. Total overall effects were calculated across all trials. Data are provided as mean ± SD when indicated and a P value of ≤0.05 was considered as statistically significant.

Two authors (F.C.A., S.C.) independently assessed the methodological quality of included studies using the Risk of Bias Assessment Tool for Nonrandomized Studies17 and the Cochrane Risk of Bias tool18 for randomized trials (Supplemental table content 2, http://links.lww.com/SLA/B525). Funnel plots of RR were generated to detect a possible evidence for publication bias. Discrepancies were resolved by group discussion (F.C.A., S.C., H.N., P.M., and E.H.).

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Ethical Review

We only used published statistical data and therefore ethical approval was not required in our meta-analysis.

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Registration

The present study has been registered at the PROSPERO register (http://www.crd.york.ac.uk/prospero, registration number: CRD42017079217).

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RESULTS

Study Selection and Characteristics

Cross-searching of electronic databases yielded in total 1575 reports. We found 1328 reports in Medline and 244 in the Cochrane Library. In addition, we hand searched references of relevant studies and included 3 studies for further analysis. After full-text review, 22 studies were initially selected as relevant for meta-analysis. During the following screening process, 2 studies were excluded because of duplicity and 3 were considered incompatible as the PBM group either consisted of only Jehovah's Witnesses or inseperately combined medical and surgical patients. Finally, quantitative data from a total of 17 studies19–35 were considered for final analysis (Fig. 1). Nine authors were contacted, of which 7 provided clarification36,37 or data,20–22,26,27,35,38 whereas 2 authors did not reply.

FIGURE 1

FIGURE 1

In total 235,779 surgical patients were included in this meta-analysis (100,886 pre-PBM group and 134,893 PBM group). The average age was 66.4 (±10) years19–25,28–31,33–35 with equal sex distribution (51.7% women). Study period ranged between 12 and 84 months and studies were conducted in university, teaching, and community hospitals, which were located in countries with either very high human development indices or a high human development index39 (Table 1).

TABLE 1

TABLE 1

Preoperative anemia management involved administration of intravenous or oral iron, erythropoietin, vitamin B12, and folic acid (pillar 1). In total, 16 studies applied iron therapy,19–31,33–35 whereas one study did not define treatment but stated “preoperative optimization of hemoglobin32 (Supplemental data content 2, http://links.lww.com/SLA/B525). We focused on 4 major perioperative measures to minimize blood loss and to ensure hemostasis management of which at least 1 had to be implemented within the PBM program (pillar 2) (Supplemental table content 1, http://links.lww.com/SLA/B525, Supplemental data content 3, http://links.lww.com/SLA/B525). Compliance with guidelines for restrictive transfusion policy was addressed in all studies (pillar 3). We, however, identified substantial differences in indication for RBC transfusion, as hemoglobin (Hb) thresholds varied between 6 and 8 g/dL in healthy patients, between 6 and 10 g/dL in high-risk patients; or the presence of clinical symptoms of an apparent anemic hypoxia (tachycardia, hypotension, ischemic ECG changes, lactate acidosis) was defined (Supplemental data content 4, http://links.lww.com/SLA/B525).

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Subgroup Characteristics and Outcomes

Subgroup analysis was performed in the fields of orthopedic, cardiac, vascular, general surgery, and other fields (Supplemental table content 3–4, http://links.lww.com/SLA/B525, supplemental data content 5, http://links.lww.com/SLA/B525). In total, n = 5 studies implemented 3,24,25,28,33,34 n = 5 studies implemented 4,19,21,29,31 n = 2 studies implemented 5,22,35 and n = 5 studies implemented 623,26,27,30,32 measures, respectively (Supplemental table content 1, http://links.lww.com/SLA/B525). In order to visualize a possible association between the number of implemented measures and changes in effect size, studies were sorted in the forest plots in ascending order from 3 to 6 measures. No evident association between number of implemented measures and outcome was detected.

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Red Blood Cell Transfusion Rate

Sixteen studies19–25,27–35 provided data on the transfusion rate in 207,006 patients. Implementation of multimodal PBM measures resulted in an overall decrease in transfusion rate by 39% (RR 0.61, 95% CI 0.55–0.68, P < 0.00001). Highest reduction in RBC transfusion rate was detected in orthopedic surgery by 55% (RR 0.45, 95% CI 0.35–0.59, P < 0.00001), followed by cardiac surgery by 50% (RR 0.50, 95% CI 0.36–0.70, P < 0.0001), and vascular surgery by 8% (RR 0.92, 95% CI 0.88–0.96, P = 0.0006) (Fig. 2).

FIGURE 2

FIGURE 2

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Red Blood Cell Units Per Patient

Nine studies20,21,23,25,26,29,30,34,35 provided data on transfused RBC units per patient in 216,657 patients. Here, implementation of multimodal PBM measures resulted overall in an average saving of 0.43 RBC units per patient (MD −0.43, 95% CI −0.54 to −0.31, P < 0.00001). Highest significant reduction was observed in cardiac surgery with 0.87 fewer RBC units per patient (MD −0.87, 95% CI −1.00 to −0.74, P < 0.00001), followed by 0.66 RBC units in vascular surgery (MD −0.66, 95% CI −1.29 to −0.04, P = 0.04), 0.25 RBC units in general surgery (MD −0.25, 95% CI −0.36 to −0.13, P < 0.0001), 0.20 RBC units in surgeries of other fields (MD −0.20, 95% CI −0.27 to −0.13, P < 0.00001), and 0.18 RBC units in orthopedic surgery (MD −0.18, 95% CI −0.26 to −0.09, P < 0.0001) (Fig. 3).

FIGURE 3

FIGURE 3

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Length of Hospital Stay

Eight studies19,21,23,26,28–30,33 provided data on LOS in 219,850 patients. Overall, implementation of multimodal PBM measures resulted in a significant decrease in LOS by 0.45 days (MD −0.45, 95% CI −0.65 to −0.25, P < 0.00001). Highest significant decrease was observed in cardiac surgery by 1.34 days (MD −1.34, 95% CI −2.34 to −0.34, P = 0.009) followed by orthopedic surgery by 0.41 days (MD −0.41, 95% CI −0.60 to −0.22, P < 0.0001), and surgery in other fields by 0.40 days (MD −0.40, 95% CI −0.59 to −0.21, P < 0.0001) (Fig. 4).

FIGURE 4

FIGURE 4

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Total Number of Complications

Fourteen studies19–26,28–30,32–34 provided data on complication rates in 214,298 patients. Few studies reported adverse events additionally to total number of complications (Supplemental data content 1, http://links.lww.com/SLA/B525, Supplemental table content 5, http://links.lww.com/SLA/B525). Multimodal PBM measures resulted overall in a significant decrease in total number of complications by 20% (RR 0.80, 95% CI 0.74–0.88, P < 0.00001). The highest significant reduction was detected in cardiac surgery by 27% (RR 0.73, 95% CI 0.56–0.94, P = 0.01), followed by orthopedic surgery by 22% (RR 0.78, 95% CI 0.66–0.92, P = 0.003), vascular surgery by 17% (RR 0.83, 95% CI 0.74–0.92, P = 0.0007), and surgery in other fields by 8% (RR 0.92, 95% CI 0.88–0.96, P < 0.0001) (Fig. 5). Overall, a significant decrease in acute renal failure by 26% (RR 0.74, 95% CI 0.66–0.83, P < 0.00001) was detected in 5 trials including 166,955 patients.20,23,25,30,32 Four21,26,30,32 studies including 192,987 patients showed a significant reduction in infection rate by 9% (RR 0.91, 95% CI 0.84–0.99, P = 0.03). No significant differences were observed in cardiac events between pre-PBM and PBM group (Supplemental table content 6, http://links.lww.com/SLA/B525). Eight trials20,23–25,29,30,32,34 including 170,189 patients provided data on thromboembolic events and showed a significant decrease by 25% (RR 0.75, 95% CI 0.67–0.84, P < 0.00001). The highest reduction in thromboembolic events was detected in general surgery by 42% (RR 0.58, 95% CI 0.41–0.81, P = 0.001), followed by surgery in other fields by 32% (RR 0.68, 95% CI 0.56–0.84, P = 0.0004). Two cardiac surgery studies20,32 including 2181 patients assessed bleeding events and showed a significant decrease by 70% (RR 0.30, 95% CI 0.15–0.64, P = 0.002) (Supplemental table content 6, http://links.lww.com/SLA/B525).

FIGURE 5

FIGURE 5

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Mortality

Nine studies20,21,23,25,26,28,30,32,33 provided data on mortality in 221,528 patients. Multimodal PBM measures resulted overall in a significant decrease in mortality rate by 11% (RR 0.89, 95% CI 0.80–0.98, P = 0.02). Highest significant reduction was detected in orthopedic surgery by 27% (RR 0.73, 95% CI 0.64–0.83, P < 0.00001). Mortality tended to be reduced in cardiac surgery by 8% (RR 0.92, 95% CI 0.73–1.16, P = 0.47), in vascular surgery by 9% (RR 0.91, 95% CI 0.80–1.04, P = 0.17), and in general surgery by 11% (RR 0.89, 95% CI 0.62–1.29, P = 0.55), although not significant (Fig. 6).

FIGURE 6

FIGURE 6

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Between-study Variance and Quality of Studies

PBM was associated with a decrease in RBC utilization, LOS, and complication rates compared to standard care; however, evidence of high statistical heterogeneity (I2 > 75%) in overall summary effect measures was observed for transfusion rate, transfused RBC units per patient, total number of complications, and cardiac events. We performed additional subgroup analysis with different group sizes (<1000, 1000–5000, and >5000 patients) and sensitivity analysis to investigate whether sample size was a possible source of between-study variation (Supplemental data content 6, http://links.lww.com/SLA/B525, Supplemental table content 7–8, http://links.lww.com/SLA/B525). Overall a significant reduction in transfusion rate, transfused RBC units per patient, LOS, total number of complication, acute renal failure, and mortality could be observed in orthopedic surgery (Supplemental table content 7, http://links.lww.com/SLA/B525). Similar results were obtained for cardiac surgical patients in that transfusion rate, number of RBC units transfused per patient, LOS, total number of complications, and bleeding events were reduced after implementation of multimodal PBM measures (Supplemental table content 7, http://links.lww.com/SLA/B525).

The assessment of methodological quality predominantly revealed low risk of bias for intervention measurement, outcome assessment, incomplete outcome data, and selective reporting. The main categories for high risk were selection of participants and confounding variables. Eleven studies reported various risk adjustment techniques for confounding factors20,21,25–32,34 (Supplemental figure content 1–3, http://links.lww.com/SLA/B525). Twelve out of 17 studies were judged with high risk of bias for selection of participants, even though all of them reported similar demographic data between the pre-PBM and PBM cohort. However, patients were examined at different time points due to the before-after design of respective studies. Confounding variables including adjustment analysis for confounding, treatments that differ from initial study protocol, noncompliance with the PBM protocol, and indications for further factors that may have affected results, were graded as high risk in 11 studies. Because of the study design, blinding of outcome assessment was not performed in nonrandomized trials. As RBC utilization and clinical outcomes were, however, extracted from secure records and objective measurement was unlikely to be influenced, 16 studies were judged to be at low risk (Supplemental figure content 1, 2, http://links.lww.com/SLA/B525). Inspection of the funnel plots showed no indications of publication bias for the outcomes RBC units transfused per patient, complication rate, and mortality, whereas it did show potential risk for transfusion rate and LOS (Supplemental figure content 3, http://links.lww.com/SLA/B525).

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DISCUSSION

More than 10 years ago, an observational benchmark study on blood utilization in elective surgical patients in 18 hospitals revealed a high predictability (>95%) of blood transfusion based on (1) the level of preoperative anemia, (2) the volume of perioperative blood loss, and (3) the transfusion threshold.40 Implementation strategies should therefore target complementary measures of all 3 PBM pillars to specifically minimize risk factors associated with anemia and transfusion. Over the last decade, single interventions of PBM have been implemented into clinical practice10; however, only few institutions adopted measures of all 3 pillars.

To the best of our knowledge, this is the first meta-analysis including studies that targeted at least 1 measure of each pillar. In total, 17 studies were included and analysis of 235,779 surgical patients suggests that a multidisciplinary, multimodal PBM program is highly effective in reducing RBC utilization and is associated with improved postoperative outcomes in various surgical disciplines. PBM was associated with a significant decrease of LOS, overall complications, and mortality rate. Our analysis revealed that PBM had the highest impact on patients undergoing orthopedic and cardiac surgical procedures in that the relative risk for RBC transfusion decreased by 55% and 50%, respectively. Recent clinical trials and meta-analyses only evaluated safety and efficacy of individual PBM pillars rather than all 3 pillars together. The prevalence of preoperative anemia varied between 10.5% and 47.9% in 18 large observational studies encompassing more than 650,000 surgical patients.41 Iron deficiency anemia is the most common cause of anemia in both economically developed and underdeveloped countries. Iron supplementation is an effective therapeutic agent to treat iron deficiency (anemia) and to reduce RBC transfusion need. The most effective increments of Hb levels (ΔHb of 1.9 and 3.9 g/dL) were detected between 2 and 4 weeks after administration.42,43 Few studies including a small number of patients showed an association between treatment of preoperative anemia and reduction in transfusion rate. For example, Froessler et al43 treated 72 abdominal surgical patients with iron preoperatively and found a 60% reduction in transfusion rate. Similar results were shown by Diez-Lobo et al44 where, treatment with iron in 75 patients undergoing abdominal hysterectomy resulted in a reduced transfusion rate (32% vs 0%, for control and iron group). However, it is noteworthy, that adequate preoperative anemia management is often hindered as elective surgery is scheduled in many hospitals on short notice.

Intraoperative autologous RBC recovery is also effective to reduce the number of patients exposed to allogeneic RBCs by 39% as demonstrated by a recent meta-analysis.45 Tranexamic acid is a widely used pharmacological agent to minimize surgical blood loss in various surgical disciplines. Two recent randomized controlled trials showed that usage of tranexamic acid was associated with a significant lower rate of hemorrhage and reduced transfusion rate by 46%.46,47 Furthermore, a meta-analysis conducted by Ker et al48 including 129 trials compromising >10,000 patients showed a reduction in transfusion rate by 38% (RR 0.62, 95% CI 0.58–0.65; P < 0.001) after application of tranexamic acid.

The use of allogeneic blood products and its potential side effects has been the focus of many discussions and whether a liberal transfusion strategy is superior to a restrictive one is still under debate. Several trials, however, showed that outcome measures were similar in patients of critical care,49 cardiac surgery,50 hip fracture surgery,51 or with acute upper gastrointestinal hemorrhage52 either assigned to a transfusion Hb threshold of <7 to 8 or 9 to 10 g/dL, respectively.

As we particularly focused on a PBM program that covered all 3 pillars of PBM, our data do not allow to reveal, which PBM measure was most effective in reducing transfusion and complication rate. We hypothesize that the required minimum of 3 measures should address the most-widely researched and complementary PBM measures, including detection and treatment of anemia (first pillar), any strategy to reduce blood loss and bleeding by perioperative autologous cell salvage, use of an antifibrinolytic agent, or hemostasis management (second pillar), and compliance with restrictive transfusion thresholds (third pillar). Several studies demonstrated that single measures, such as treatment with iron 2 to 4 weeks’ before surgery or the use of cell salvage devices, are very effective. The successful implementation of single pillar programs, however, might be compromised if surgery is scheduled short term or if hospitals do not have the necessary resources. No significant association between number of implemented measures and outcome was detected probably due to heterogeneity and different impact on clinical outcomes. It is noteworthy to mention that simple addition of 2 different combinations does not double any effect size. We therefore propose that clinicians and policy makers should concentrate their efforts on the initial adoption of the 3-pillar framework, to promote a step-by-step implementation of further PBM measures that fit best to the individual conditions. Furthermore, our data indicate that PBM implementation is feasible and successful in different types of hospitals and different types of surgical disciplines.

Critical evaluation of the appropriateness and assessment of cost-effectiveness are crucial to support further dissemination and implementation of PBM. However, comparison between studies that provided cost analysis data is challenging because cost effectiveness of PBM is determined in different ways. Cost savings ranged between ¥ 1638 ($ 23981),29 $ 4000,23 € 952660 (1102070 $),30 and $1495000021 up to $ 3255426 depending on time periods, number of investigated patients, patient populations, and implemented PBM measures. Future studies should not only focus on blood acquisition costs but also consider cost-effectiveness for anemia management, use of cell salvage, complication rate, duration of hospital stay, adverse events to transfusion, reduced workload in hospital blood transfusion laboratories, and reduced material and general personal costs, for example. Nevertheless, we would like to point out that the primary objective of this meta-analysis was the impact of PBM on clinical outcomes.

Only a few regulatory authorities support the implementation of PBM worldwide. For example, the European Commission previously released an EU PBM implementation and dissemination guide53,54; however, PBM measures are not an obligatory part of clinical routine yet. The National Blood Authority supported the first worldwide implementation of PBM in Western Australia in 200813 and the National Institute for Health and Care Excellence guidelines in the UK postulate treatment with iron in iron-deficiency anemic patients 2 weeks before surgery.55 At this time, Italy is the only country in which implementation of PBM is mandatory by law.56

Although this meta-analysis provides important and novel data, there are a few limitations. Due to the current body of evidence, we provide specified results for orthopedic, cardiac, vascular, and general surgery. The extent to which our conclusions can be generalized or applied to other surgical or medical fields demands further investigations. We cannot exclude that additional references might have been missed by our systematic search of databases, particularly during the years before the term PBM was introduced in 2007. Furthermore, differences in clinical implementation, treatment duration, study designs, and characteristics of included studies may have contributed to heterogeneity. Differences in perioperative care apart from PBM interventions may have contributed to positive clinical outcomes. Two studies indicated additional physiotherapeutic treatments (enhanced recovery program,28 more aggressive mobilization29) and were consequently judged as high risk for confounding variables. Loftus et al stated that the participation rate was similar between the pre-PBM and the PBM group, and Ma et al argued that the improved mobilization rate was the result of fewer symptoms of anemia. A meta-analysis of large prospective randomized controlled trials would be preferred to relate changes in outcome parameters resulting from blood conservation and management. In view of the current literature that supports the beneficial effects of individual PBM measures and published guidelines,53,54,57 some national authorities may decline any approval of a prospective and randomized comparison between multimodal PBM interventions versus a control treatment without attention to prevent anemia, to minimize blood loss, and to respect physiological transfusion thresholds.

In conclusion, our study has several implications for clinical practice. A comprehensive PBM program is associated with a reduction of RBC transfusion rates by 39% and an average saving of 0.43 RBC units per patient. In addition, the successful implementation of PBM was associated with a significant reduction in complication rate and mortality. In this manner, results of this first meta-analysis investigating a multimodal approach should enable all executives and health care providers to support and strengthen further activities in the field of PBM.

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ACKNOWLEDGMENTS

The authors would like to thank the following authors of included studies for providing clarification of data or additional information: J. Brevig (Providence Regional Medical Center, Everett, US), S. Frank (Johns Hopkins Hospital, Baltimore, US), R. Jin (Providence Health & Services, Portland, US), O. Theusinger (Balgrist University Hospital, Zürich, CH), and L. Weinberg (Austin Hospital, Melbourne, AU). The cooperative collaboration was essential to include all available data in our meta-analysis.

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Keywords:

blood transfusion; complication rate; effectiveness; mortality; Patient Blood Management

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