Evidence summary to support PICO question 15 on PBM ... › wp-content › uploads › 2019 › 04 › 20180401_evid… · Xydas, 2012, USA 6 Yaffee, 2014, USA 24 PBM component Intervention(s)

Evidence summary

to support

PICO question 15 on

PBM implementation:

effectiveness “comprehensive” PBM

programs

April 2018 (version 2.0)

Centre for Evidence-Based Practice (CEBaP)

Belgian Red Cross

2

Content

Flow chart ........................................................................................................................................................ 3

Overview of 20 included studies1-20 ....................................................................................................... 4

Overview of excluded studies21-37 ........................................................................................................... 6

Overview table included studies and intervention components/targeted

physicians/category/follow-up period .................................................................................................. 8

Overview evidence table GRADE software .......................................................................................... 9

GRADE domain: resource costs ............................................................................................................ 17

Detailed evidence summary................................................................................................................... 19

3

Flow chart

Scre

en

ing

Incl

ud

ed

El

igib

ility

Id

en

tifi

cati

on

Records (after removing duplicates) identified through database searching

(Pubmed, Embase, Cochrane Library, Transfusion Evidence Library)

(n = 968)

Records screened on title and

abstract

(n = 968)

Full-text articles assessed for eligibility

(n = 34)

Studies finally included

(n = 20 observational studies)

Records excluded

(n = 648)

Records excluded (n = 14) Reason for exclusion

+ Desgin (n=5)

+ Intervention (n=8)

+ Language (n=1)

4

Overview of 20 included studies1-20

1. Frank SM, Thakkar RN, Podlasek SJ, et al. Implementing a Health System-wide Patient Blood

Management Program with a Clinical Community Approach. Anesthesiology 2017;127:754-64.

2. Frew N, Alexander D, Hood J, et al. Impact of a blood management protocol on transfusion rates

and outcomes following total hip and knee arthroplasty. Ann R Coll Surg Engl 2016;98:380-6.

3. Gani F, Cerullo M, Ejaz A, et al. Implementation of a Blood Management Program at a Tertiary

Care Hospital: Effect on Transfusion Practices and Clinical Outcomes Among Patients Undergoing

Surgery. Ann Surg 2017.

4. Gross I, Seifert B, Hofmann A, et al. Patient blood management in cardiac surgery results in fewer

transfusions and better outcome. Transfusion 2015;55:1075-81.

5. Gross I, Trentino KM, Andreescu A, et al. Impact of a Patient Blood Management Program and an

Outpatient Anemia Management Protocol on Red Cell Transfusions in Oncology Inpatients and

Outpatients. Oncologist 2016;21:327-32.

6. Kansagra A, Andrzejewski C, Krushell R, et al. Blood Management Strategies to Reduce

Transfusions After Elective Lower-Extremity Joint Arthroplasty Surgeries: One Tertiary Care

Hospital's Early Experience With an Alternative Payment Model-a Total Joint "Bundle". Am J Med

Qual 2017;32:668-74.

7. Kopanidis P, Hardidge A, McNicol L, et al. Perioperative blood management programme reduces

the use of allogenic blood transfusion in patients undergoing total hip and knee arthroplasty. J

Orthop Surg Res 2016;11:28.

8. Leahy MF, Hofmann A, Towler S, et al. Improved outcomes and reduced costs associated with a

health-system-wide patient blood management program: a retrospective observational study in

four major adult tertiary-care hospitals. Transfusion 2017;57:1347-58.

9. Leahy MF, Roberts H, Mukhtar SA, et al. A pragmatic approach to embedding patient blood

management in a tertiary hospital. Transfusion 2014;54:1133-45.

10. Leahy MF, Trentino KM, May C, et al. Blood use in patients receiving intensive chemotherapy for

acute leukemia or hematopoietic stem cell transplantation: the impact of a health system-wide

patient blood management program. Transfusion 2017;57:2189-96.

11. Loftus TJ, Spratling L, Stone BA, et al. A Patient Blood Management Program in Prosthetic Joint

Arthroplasty Decreases Blood Use and Improves Outcomes. J Arthroplasty 2016;31:11-4.

12. Mehra T, Seifert B, Bravo-Reiter S, et al. Implementation of a patient blood management

monitoring and feedback program significantly reduces transfusions and costs. Transfusion

2015;55:2807-15.

13. Meybohm P, Herrmann E, Steinbicker AU, et al. Patient Blood Management is Associated With a

Substantial Reduction of Red Blood Cell Utilization and Safe for Patient's Outcome: A

Prospective, Multicenter Cohort Study With a Noninferiority Design. Ann Surg 2016;264:203-11.

5

14. Rineau E, Chaudet A, Chassier C, et al. Implementing a blood management protocol during the

entire perioperative period allows a reduction in transfusion rate in major orthopedic surgery: a

before-after study. Transfusion 2016;56:673-81.

15. Ternstrom L, Hyllner M, Backlund E, et al. A structured blood conservation programme reduces

transfusions and costs in cardiac surgery. Interact Cardiovasc Thorac Surg 2014;19:788-94.

16. Thakkar RN, Lee KH, Ness PM, et al. Relative impact of a patient blood management program on

utilization of all three major blood components. Transfusion 2016;56:2212-20.

17. Theusinger OM, Kind SL, Seifert B, et al. Patient blood management in orthopaedic surgery: a

four-year follow-up of transfusion requirements and blood loss from 2008 to 2011 at the Balgrist

University Hospital in Zurich, Switzerland. Blood Transfus 2014;12:195-203.

18. Verdecchia NM, Wisniewski MK, Waters JH, et al. Changes in blood product utilization in a seven-

hospital system after the implementation of a patient blood management program: A 9-year

follow-up. Hematology 2016;21:490-9.

19. Xydas S, Magovern CJ, Slater JP, et al. Implementation of a comprehensive blood conservation

program can reduce blood use in a community cardiac surgery program. J Thorac Cardiovasc

Surg 2012;143:926-35.

20. Yaffee DW, Smith DE, 3rd, Ursomanno PA, et al. Management of blood transfusion in aortic valve

surgery: impact of a blood conservation strategy. Ann Thorac Surg 2014;97:95-101.

6

Overview of excluded studies21-37

Butler 2015 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

21. Butler CE, Noel S, Hibbs SP, et al. Implementation of a clinical decision support system improves

compliance with restrictive transfusion policies in hematology patients. Transfusion

2015;55:1964-71.

Cohn 2014 (reason for exclusion: inappropriate study design)

22. Cohn CS, Welbig J, Bowman R, et al. A data-driven approach to patient blood management.

Transfusion 2014;54:316-22.

Collins 2014 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

23. Collins RA, Triulzi DJ, Waters JH, et al. Evaluation of real-time clinical decision support systems for

platelet and cryoprecipitate orders. Am J Clin Pathol 2014;141:78-84.

Corwin 2014 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

24. Corwin HL, Theus JW, Cargile CS, et al. Red blood cell transfusion: impact of an education

program and a clinical guideline on transfusion practice. J Hosp Med 2014;9:745-9.

Geissler 2015 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

25. Geissler RG, Kosters C, Franz D, et al. Utilisation of Blood Components in Trauma Surgery: A

Single-Centre, Retrospective Analysis before and after the Implementation of an Educative PBM

Initiative. Transfus Med Hemother 2015;42:83-9.

Geissler 2015 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

26. Geissler RG, Rotering H, Buddendick H, et al. Utilisation of blood components in cardiac surgery:

a single-centre retrospective analysis with regard to diagnosis-related procedures. Transfus Med

Hemother 2015;42:75-82.

Grau 2017 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

27. Grau JB, Fortier JH, Kuschner C, et al. Implementing a protocol to optimize blood use in a cardiac

surgery service: results of a pre-post analysis and the impact of high-volume blood users.


Grayson 2003 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

28. Grayson AD, Jackson M, Desmond MJ. Monitoring blood transfusion in patients undergoing

coronary artery bypass grafting: an audit methodology. Vox Sang 2003;85:96-101.

Norgaard 2017 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

29. Norgaard A, Stensballe J, de Lichtenberg TH, et al. Three-year follow-up of implementation of

evidence-based transfusion practice in a tertiary hospital. Vox Sang 2017;112:229-39.

Ohsaka 2008 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

30. Ohsaka A, Abe K, Ohsawa T, et al. A computer-assisted transfusion management system and

changed transfusion practices contribute to appropriate management of blood components.


Oliver 2014 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

31. Oliver JC, Griffin RL, Hannon T, et al. The success of our patient blood management program

depended on an institution-wide change in transfusion practices. Transfusion 2014;54:2617-24.

7

Patel 2016 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

32. Patel VM, Rains AW, Clark CT. Effectiveness of Provider Education Followed by Computerized

Provider Order Entry Alerts in Reducing Inappropriate Red Blood Cell Transfusion. J Blood

Transfus 2016;2016:2859720.

Ravazi 2014 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

33. Razavi SA, Carter AB, Puskas JD, et al. Reduced red blood cell transfusion in cardiothoracic

surgery after implementation of a novel clinical decision support tool. J Am Coll Surg

2014;219:1028-36.

Saag 2017 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

34. Saag HS, Lajam CM, Jones S, et al. Reducing liberal red blood cell transfusions at an academic

medical center. Transfusion 2017;57:959-64.

Sekhar 2016 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

35. Sekhar M, Clark S, Atugonza R, et al. Effective implementation of a patient blood management

programme for platelets. Transfus Med 2016;26:422-31.

Yazer 2016 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

36. Yazer MH, Deandrade DS, Triulzi DJ, et al. Electronic enhancements to blood ordering reduce

component waste. Transfusion 2016;56:564-70.

Zuckerberg 2015 (reason for exclusion: focused on transfusion strategies (1 PBM pillar only))

37. Zuckerberg GS, Scott AV, Wasey JO, et al. Efficacy of education followed by computerized

provider order entry with clinician decision support to reduce red blood cell utilization.

Transfusion 2015;55:1628-36.

8

Overview table included studies and intervention components/targeted physicians/category/follow-up period

Gu

idelin

e

Form

Au

dit

Ed

uca

tio

n

Pri

nci

ple

s o

f K

ott

er

mo

del

Deci

sio

n s

up

po

rt s

yste

m

Mo

nit

ori

ng

an

d f

eed

back

PB

M p

illa

r 1 (

op

tim

ize e

ryth

rop

oie

sis)

PB

M p

illa

r 2 (

min

imiz

e b

loo

d lo

ss)

PB

M p

illa

r 3 (

man

ag

e a

naem

ia)

Su

rgeo

ns

All

Un

clear

Card

iac

surg

ery

Ort

ho

ped

ic s

urg

ery

Gast

roin

test

inal su

rgery

Gen

era

l su

rgery

Geen

ral M

ed

ical

Malig

nan

t d

isease

Frank, 2017, USA 30

Frew, 2016, UK 60

Gani, 2017, USA 24

Gross, 2015, USA 66

Gross, 2016, USA 60

Kansagra, 2017, USA 15

Kopanidis, 2016, Australia 24

Leahy, 2014, Australia 36

Leahy, 2017, Australia (1) 54

Leahy, 2017, Australia (2) 54

Loftus, 2016, USA 12

Mehra, 2015, Switzerland 12

Meybohm, 2016, Germany 12-30

Rineau, 2016, France 6

Ternström, 2014, Sweden 12

Thakkar, 2016, USA 12

Theusinger, 2014, Switzerland 36

Verdecchia, 2016, USA 96

Xydas, 2012, USA 6

Yaffee, 2014, USA 24

PBM

component

Follo

w -

up

(m

on

ths)

Intervention(s) to

promote/monitor PBM

implementation

Targeted

physiciansCategory

9

Overview evidence table GRADE software

Certainty assessment № of patients Effect

Certaint

y

Importanc

e № of

studies

Study

design

Risk of

bias Inconsistency Indirectness Imprecision

Other

considerations

a PBM

program

no PBM

program

Relativ

e

(95%

CI)

Absolut

e

(95% CI)

Blood product utilization - number of patients/admissions receiving RBC transfusion (follow up: median 22.5 months)

10 observational

studies

not

serious

not serious not serious not serious none 48174/46414

9 (10.4%)

27930/25327

2 (11.0%)

RR 0.78

(0.73 to

0.85)

24 fewer

per

1.000

(from 17

fewer to

30 fewer)

⨁⨁◯◯

LOW

CRITICAL

Blood product utilization - number of patients receiving PLT transfusion (follow up: median 21 months)

6 observational

studies

not

serious

serious a not serious not serious none 9420/317062

(3.0%)

4451/140104

(3.2%)

RR 0.86

(0.78 to

0.95)

4 fewer

per

1.000

(from 2

fewer to

7 fewer)

⨁◯◯◯

VERY

LOW

CRITICAL

Blood product utilization - number of patients receiving FFP transfusion (follow up: median 12 months)

5 observational

studies

serious b

serious a not serious serious c none 4141/316500

(1.3%)

7004/117444

(6.0%)

RR 0.49

(0.23 to

1.06)

30 fewer

per

1.000

(from 4

more to

46 fewer)

⨁◯◯◯

VERY

LOW

CRITICAL

Blood product utilization - number of patients receiving cryprecipitate transfusion (follow up: 6 months)

10


Certaint

y

Importanc

e № of

studies

Study

design

Risk of


Other

considerations

a PBM

program

no PBM

program

Relativ

e

(95%

CI)

Absolut

e

(95% CI)

1 observational

studies

not

serious

not serious not serious serious d none 27/551 (4.9%) 76/481

(15.8%)

RR 0.31

(0.20 to

0.47)

109

fewer

per

1.000

(from 84

fewer to

126

fewer)

⨁◯◯◯

VERY

LOW

IMPORTANT

Blood product utilization - number of patients receiving any transfusion (RBC, PLT, FFP) (follow up: median 12 months)

6 observational

studies

not

serious

serious a not serious not serious none 1582/8851

(17.9%)

2393/8608

(27.8%)

RR 0.53

(0.40 to

0.70)

131

fewer

per

1.000

(from 83

fewer to

167

fewer)

⨁◯◯◯

VERY

LOW

CRITICAL

Blood product utilization - number of RBC units transfused (continuous) (follow up: median 21 months)

7 observational

studies

not

serious

serious a not serious not serious none 375543 180660 - MD 0.47

units

lower

(0.68

lower to

0.27

lower)

⨁◯◯◯

VERY

LOW

CRITICAL

Blood product utilization - number of FFP units transfused (follow up: median 18 months)

11


Certaint

y

Importanc

e № of

studies

Study

design

Risk of


Other

considerations

a PBM

program

no PBM

program

Relativ

e

(95%

CI)

Absolut

e

(95% CI)

6 observational

studies

serious e

not serious not serious not serious none 300337 126147 - MD 0.67

units

lower

(1.25

lower to

0.09

lower)

⨁◯◯◯

VERY

LOW

CRITICAL

Blood product utilization - number of PLT units transfused (follow up: median 24 months)

5 observational

studies

serious e

serious a not serious not serious none 300337 126147 - MD 0.44

units

lower

(0.68

lower to

0.19

lower)

⨁◯◯◯

VERY

LOW

CRITICAL

Blood product utilization - number of cryoprecipitate units transfused (follow up: 6 months)

1 observational

studies

serious f not serious not serious not serious none 551 481 - MD 1.2

units

lower

(1.68

lower to

0.72

lower)

⨁◯◯◯

VERY

LOW

IMPORTANT

Blood product utilization - number of any blood products transfused (RBC, FFP, PLT) (follow up: median 12 months)

12


Certaint

y

Importanc

e № of

studies

Study

design

Risk of


Other

considerations

a PBM

program

no PBM

program

Relativ

e

(95%

CI)

Absolut

e

(95% CI)

4 observational

studies

serious g

not serious not serious not serious none 4899 8316 - MD 2.32

units

lower

(3.12

lower to

1.52

lower)

⨁◯◯◯

VERY

LOW

IMPORTANT

Morbidity - composite measures (follow up: median 22.5 months)

6 observational

studies

not

serious

not serious not serious serious c none See 'Forest plot' figure 16: no evidence of effect in

5/6 studies. One study (Gani 2017) showed that a

PBM program (containing a guideline + education

+ decision support/form/monitoring) resulted in a

statistically significant reduction in postoperative

complications.

⨁◯◯◯

VERY

LOW

CRITICAL

Figure 16: Forest plot of outcome: Morbidity (composite measures)

13


Certaint

y

Importanc

e № of

studies

Study

design

Risk of


Other

considerations

a PBM

program

no PBM

program

Relativ

e

(95%

CI)

Absolut

e

(95% CI)

Morbidity - acute myocardial infarction (follow up: median 24 months)

3 observational

studies

not

serious

not serious not serious serious h none 0/1038 (0.0%) 4/972 (0.4%) RR 0.20

(0.02 to

1.73)

3 fewer

per

1.000

(from 3

more to

4 fewer)

⨁◯◯◯

VERY

LOW

CRITICAL

Morbidity - acute kidney injury (follow up: median 24 months)

4 observational

studies

not

serious

not serious not serious serious i none 148/3313

(4.5%)

58/1359

(4.3%)

RR 0.84

(0.60 to

1.17)

7 fewer

per

1.000

(from 7

more to

17 fewer)

⨁◯◯◯

VERY

LOW

CRITICAL

Mortality - hospital mortality (follow up: median 24 months)

7 observational

studies

not

serious

serious a not serious serious i none OR 0.64

(0.23 to

1.74)

1 fewer

per

1.000

(from 0

fewer to

2 fewer)

⨁◯◯◯

VERY

LOW

CRITICAL

Mortality - not defined (follow up: median 12 months)

3 observational

studies

not

serious

serious j not serious not serious none See 'Forest plots' figure 22: no deaths in 1 study

(Kopanidis 2016), no evidence of effect in 1 study

(Loftus 2016) and 1 study showed no difference in

mortality rate (Thakkar 2016).

⨁◯◯◯

VERY

LOW

CRITICAL

14


Certaint

y

Importanc

e № of

studies

Study

design

Risk of


Other

considerations

a PBM

program

no PBM

program

Relativ

e

(95%

CI)

Absolut

e

(95% CI)

Figure 22: Forest plot of outcome: Mortality: not specified

Mortality - 30-day mortality (follow up: median 9 months)

2 observational

studies

serious k

not serious not serious serious i none 36/1585

(2.3%)

32/1709

(1.9%)

RR 1.25

(0.78 to

2.02)

5 more

per

1.000

(from 4

fewer to

19 more)

⨁◯◯◯

VERY

LOW

CRITICAL

Length of hospital stay (days) (follow up: median 16.5 months)

4 observational

studies

not

serious

serious j not serious serious i none See 'Forest plots' figure 24: reduction in length of

hospital stay in 4 studies (3/4 statistical significant),

⨁◯◯◯

VERY

LOW

IMPORTANT

15


Certaint

y

Importanc

e № of

studies

Study

design

Risk of


Other

considerations

a PBM

program

no PBM

program

Relativ

e

(95%

CI)

Absolut

e

(95% CI)

Figure 24: Forest plot of outcome: Length of hospital stay (days)

no evidence of effect in 1 study (total knee

arthroplasty)

Morbidity - acute ischaemic stroke (follow up: median 18 months)

4 observational

studies

not

serious

not serious not serious serious d none 92/4247

(2.2%)

40/2387

(1.7%)

RR 1.03

(0.71 to

1.52)

1 more

per

1.000

(from 5

fewer to

9 more)

⨁◯◯◯

VERY

LOW

CRITICAL

CI: Confidence interval; RR: Risk ratio; MD: Mean difference; OR: Odds ratio

a. all parameters (statistical and visual) are positive; b. Inappropriate eligibility criteria (Xydas 2012), inappropriate methods for exposure and outcome variables (Ternström 2014),

not controlled for confounding (Gross 2015, Ternström 2014 and Thakkar 2016) and other limitations (all studies); c. Large variability in results; d. Low number of events; e.

Inappropriate eligibility criteria (Xydas 2012), inappropriate methods for exposure and outcome variables (Ternström), not controlled for confounding (Gross 2015, Yaffee 2014,

Ternström 2014), other limitations (all studies); f. Inappropriate eligibility criteria and other limitations; g. Inappropriate eligibility criteria (Xydas 2012), inappropriate methods for

exposure and outcome variables (Ternström 2014), not controlled for confounding (Yaffee 2014 and Ternström 2014) and other limitations (all studies); h. Low number of events

16

and large variability in results; i. Large variability in results; j. Widely differing estimates of the effects; k. Inappropriate eligibility criteria (Xydas 2012), inappropriate methods for

exposure and outcome variables (Ternström 2014), not controlled for confounding (Ternström 2014) and other limitations (all studies).

17

GRADE domain: resource costs

ECONOMIC OUTCOMES

Direct cost of EPO,

iron, tranexamic acid

and blood transfusion

After PBM versus before

PBM

€30.572 versus €25.097 N/A Rineau, 2016

Total direct costs (€) After PBM versus before

PBM

Statistically significant:

44.300±25.915 vs 48.375±28.053

MD: XX, 95%CI [XX;XX] (p<0.001)

In favour of PBM program

1, 2662 (2275 vs 387)

Gross, 2015

Mehra, 2015

Considering 65,925 transfused RBC units in the pre-PBM cohort at minimum mean purchase costs of 85€per

RBC unit in Germany, and an overall decrease in RBC utilization by 17%, the implementation of PBM was

associated with 952.660€ per year savings for RBC units. If costs for the RBC transfusion process would be

considered additionally (eg, for materials, labor, third-party services, and capital), the savings could reach more

than 3.000.000€ per year.

Meybohm

2016

1g intravenous iron: (9 x 4 doses) x £181.45 = £6,532

20,000 units erythropoietin: (9 x 4 doses) x £27.80 = £1,001

1g intravenous tranexamic acid: 406 doses x £1.30 = £528

Ferrous sulphate: 48 x £4.36 (2-month supply) = £209

Units of blood saved: (181–20) x £145.00 = -£23,345

Bed days saved: (406 x 3) x £400.00 = -£487,200

Total -£502,455

Frew, 2016

Across the study period, the total reduction in inpatient and day case transfusions translates to 1111 fewer RBC

units and 588 fewer PLT units. This represents direct product cost savings of AU$389,537 (US$364,177) and

AU$305,349 (US$289,830), respectively.

Leahy, 2017

Adopting a conservative approach, if the annual rate of transfusion remained at baseline year levels, then an

additional 50,115 units of blood would have been transfused over the study period, comprising 35,423 RBC

units, 10,721 of FFP units, and 3970 platelets units. Based on product-acquisition cost, the calculated savings

from this reduction is $18,507,092 in Australian dollars (AU$) and $18,078,258 in US dollars ($US).

Leahy, 2017

(2)

The estimated cost avoidance for this PBM program was $725,233 (range $497,466–$1,127,399) Loftus, 2016

Direct cost ($1000) After PBM versus before

PBM

Not statistically significant:

10.77±5.58 vs 10.79±5.66

MD: XX, 95%CI [XX;XX] (p=0.8673)

1, 12590 (6593 vs 5997) Loftus, 2016

Ternstrom,

2014

18

Thakkar, 2016

The mean number of RBC units saved per patient after initiation of BCS was 1.7 units, resulting in an estimated

cost savings of $1,755 per patient, or $679,045 overall (n ¼ 387), for an annual savings of approximately

$339,522 for RBC transfusion alone in AVR patients.

Yaffee, 2014

Blood acquisition cost savings were $2,120,273/yr, an approximate 400% return on investment for our patient

blood management efforts.

Frank, 2017

Depending on the costing methodology used as described in the outcomes and data collection section,

annualized savings between time periods before and after PBM implementation ranged from a low of $108 000

using acquisition cost (~$225/unit) to $480 000 when using activity-based costing (~$1000/unit).

Kansagra,

2017

19

Detailed evidence summary

Topic Patient Blood Management (PBM)

Subtopic Effectiveness PBM implementation

Intervention Patient Blood Management programs

Question (PICO) Is a PBM program [intervention] effective to improve clinical and economic

outcomes [outcomes] compared to no PBM program [comparison]?

Search Strategy Databases

The Cochrane Library (systematic reviews and controlled trials) using the

following search strategy:

1. “Patient Blood Management”:ti,ab,kw

2. [mh Education] OR educat*:ti,ab,kw OR implement*:ti,ab,kw OR

monitor*:ti,ab,kw OR [mh “information dissemination”] OR disseminat*:ti,ab,kw

OR adopt*:ti,ab,kw OR [mh “quality improvement”] OR improv*:ti,ab,kw OR [mh

“organizational innovation”] OR change*:ti,ab,kw OR program*:ti,ab,kw OR

practice*:ti,ab,kw OR scal*:ti,ab,kw OR diffusion:ti,ab,kw OR

incorporation:ti,ab,kw OR adherence:ti,ab,kw OR transformation:ti,ab,kw OR

translation:ti,ab,kw OR transfer:ti,ab,kw OR uptake:ti,ab,kw OR

sustainab*:ti,ab,kw OR institutionali*:ti,ab,kw OR routin*:ti,ab,kw OR

maintenance:ti,ab,kw OR capacity:ti,ab,kw OR integration:ti,ab,kw

3. 1 AND 2 (#hits on July 14: 29)

MEDLINE (via PubMed interface) using the following search strategy:

1. “Patient Blood Management”[TIAB]

2. Education[Mesh] OR educat*[TIAB] OR implement*[TIAB] OR monitor*[TIAB]

OR “information dissemination”[Mesh] OR disseminat*[TIAB] OR adopt*[TIAB]

OR “quality improvement”[Mesh] OR improv*[TIAB] OR “organizational

innovation”[Mesh] OR change*[TIAB] OR program*[TIAB] OR practice*[TIAB]

OR scal*[TIAB] OR diffusion[TIAB] OR incorporation[TIAB] OR adherence[TIAB]

OR transformation[TIAB] OR translation[TIAB] OR transfer[TIAB] OR

uptake[TIAB] OR sustainab*[TIAB] OR institutionali*[TIAB] OR routin*[TIAB] OR

maintenance[TIAB] OR capacity[TIAB] OR integration[TIAB]

3. 1 AND 2 (#hits on July 18: 210)

Embase (via Embase.com interface) using the following search strategy:

1. ‘Patient Blood Management’:ab,ti

2. Education/exp OR educat*:ab,ti OR implement*:ab,ti OR monitor*:ab,ti OR

‘information dissemination’/exp OR disseminat*:ab,ti OR adopt*:ab,ti OR ‘total

quality management’/exp OR improv*:ab,ti OR change*:ab,ti OR program*:ab,ti

OR practice*:ab,ti OR scal*:ab,ti OR diffusion:ab,ti OR incorporation:ab,ti OR

adherence:ab,ti OR transformation:ab,ti OR translation:ab,ti OR transfer:ab,ti

OR uptake:ab,ti OR sustainab*:ab,ti OR institutionali*:ab,ti OR routin*:ab,ti OR

maintenance:ab,ti OR capacity:ab,ti OR integration:ab,ti

3. 1 AND 2 (#hits on July 18: 507)

Transfusion Evidence Library using the following search strategy:

1. Patient blood management (#hits on July 18: 307)

2. educat* OR implement* OR monitor* OR disseminat* OR adopt* OR improv*

OR “organizational innovation” OR change* OR program* OR practice* OR

scal* OR diffusion OR incorporation OR adherence OR transformation OR

translation OR transfer OR uptake OR sustainab* OR institutionali* OR routin*

OR maintenance OR capacity OR integration

20

3. 1 AND 2 (#hits on July 18: 141)

After removing duplicates, 674 papers were screened on title and abstract

In addition to the current search strategies, the first 20 related citations of all

included papers were screened and included (if appropriate).

Search date 30 January 2018

In/Exclusion

criteria

Population: Included: patients who might need transfusion (surgical and non-

surgical patients/ acute and chronic disease patients/ adults and children).

Intervention: Included: Patient blood management (PBM) is a patient-focused,

evidence-based and systematic approach to optimize the management of patient

and transfusion of blood products for quality and effective patient care. It is

designed to improve patient outcomes through the safe and rational use of blood

and blood products and by minimizing unnecessary exposure to blood products.

Patient Blood Management focuses on three pillars of care during the pre-, intra-

and post-operative phase: 1) optimizing erythropoiesis, 2) minimizing blood loss

and 3) management of anemia. We only include PBM programs that contained at

least one intervention for 2 or 3 pillars. Excluded: programs that only focused on

interventions in 1 pillar (e.g. restrictive RBC transfusion strategies).

Comparison: no PBM program

Outcome: Included: Clinical outcomes including blood product utilization, hospital

stay, morbidity (acute myocardial infarction, acute ischaemic stroke, acute kidney

injury, acute mesenteric ischemia and acute peripheral vascular ischemia) and

mortality (30-day and in-hospital mortality), and economic outcomes including

costs.

Composite measures, if relevant, were used. If composite measure were not

relevant or available, individual measures were included. Data on relevant

subgroup analyses (e.g. type of surgery), if available, were extracted/included.

When papers reported outcomes for different periods (e.g. per year), we decided to

only include the outcomes of the longest/latest period unless it was possible to

pool the outcomes of all periods together. Exclude: outcomes with no raw data

and/or effect estimated (e.g. only p-values, percentages).

Study design: Include: a systematic review: inclusion of the studies of the systematic

review if the search strategy and selection criteria are clearly described and if at least

the Cochrane Library, MEDLINE and Embase are searched.

An experimental study: inclusion in case of one of the following study types: (quasi

or non-) randomised controlled trial, controlled before and after study or controlled

interrupted time series, and the data are available.

An observational study: inclusion in case of one of the following study types: cohort

and case-control study, (un)controlled before and after study or (un)controlled

interrupted time series, and the data are available.

Exclude: case series, cross-sectional studies, animal studies, ex vivo or in vitro studies,

studies reporting no quantitative data, studies reporting only means, but no SDs,

effect sizes, p-values.

Language: English, French and German

Characteristics of included studies

Author, year,

Country

Study design Population Comparison Study funding and

COI

Frank, 2017,

USA

Observational:

before-after

study

(multi-centre)

Patients from 5/6 hospitals of

The Johns Hopkins Health

System (2 academic centers and

3 community hospitals): 117.444

Comparison: after versus

before implementation

intervention

PBM components:

The authors would

like to acknowledge

both financial and

personnel support

from the Johns

21

inpatients before PBM versus

293.163 inpatients after PBM

Demographic characteristics

were not reported.

Targeted physicians: all

Category: orthopaedic general

surgery + medical

PBM pillar 1 (optimize

erythropoiesis)

PBM pillar 2 (minimize

blood loss)

PBM pillar 3 (manage

anemia)

Promotional

intervention(s):

- Guideline

- Education

- Monitoring (data

acquisition

system)

- CPOE

- Audit/feedback

Follow-up period before

PBM: 12 months

Follow-up period after

PBM: 30 months

Hopkins Health

System

Armstrong Institute

for Patient Safety

and Quality.

Support was

provided solely from

institutional and/or

departmental

sources.

Dr. Frank has

received consulting

fees from

Haemonetics

(Braintree,

Massachusetts),

Medtronic

(Minneapolis,

Minnesota),

and Zimmer/Biomet

(Warsaw, Indiana).

All other authors

declare no

competing interests.

Frew, 2016,

UK

Observational:

before-after

study

(prospective,

retrospective)

Patients listed for hip/knee

replacement: 717 before PBM

(median age 72 (IQR: 65-78),

42% males) versus 406 after

PBM (median age 71 (IQR: 65-

77), 45% males)

Targeted physicians: surgeons

Category: orthopaedic surgery



intervention

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Audit


PBM: 12 months


PBM: 60 months

The project received

financial support

from the Health

Foundation (an

independent charity).

The grant covered

clinician and nursing

time to set up and

administer the

programme during

the first year (2010).

Gani, 2017,

USA

Observational:

before-after

study

(prospective)

17114 patients undergoing

gastrointestinal surgery at a

single surgical department at a

tertiary care hospital: 8450 pre-

PBM (median age 56 years, (IQR

43-67), 50% males) versus 8664

post-PBM (median age 56 years,

(IQR 44-67), 51% males))


Category: gastrointestinal

surgery

Comparison: After versus

before PBM

implementation

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

One author has

received funding

from Haemonetics,

Braintree, MA, and

Medtronic,

Fridley, MN. The

received funding was

not involved in any

way in the current

study. The remaining

authors report no

conflicts of interest.

22

- Education

- Audit

- Monitoring

(monthly

dashboards using

IMPACT online)


PBM: 24 months


PBM: 24 months

Gross, 2015,

USA

Observational:

before-after

study

2662 cardiac surgery patients:

387 in the pre-PBM epoch (July

2006-March 2007, 65±11 years

and 71% males) versus 2275 in

the PBM epoch (April 2007-

September 2012, 65±11 years

and 72% males).


Category: cardiac surgery


before PBM

implementation

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline


PBM: 9 months


PBM: 66 months

This study was

supported by

departmental funds

only.

Several authors

reported conflicts of

interest.

Gross, 2016,

USA

Observational:

before-after

study (time

interrupted

series)

1390 patients admitted with a

primary diagnosis

of malignancy treated at Eastern

Maine Medical Center as

inpatients (2846 admissions) or

outpatients (25.100 admissions),

or both, from January 2008

through

July 2013: mean age 66.5±12.4

years, 51.7% male

Targeted physicians: unclear

Category: Malignant disease

(cancer of the bronchus,

secondary malignancies, cancer

of the prostate, cancer of the

breast and pneumonia.


before PBM

implementation

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline


PBM: 12 months


PBM: 60 months

Irwin Gross:

Accumen LLC (E);

Shannon Farmer:

principal investigator

of a government-

funded research

grant randomized

controlled trial

(RF). The other

authors indicated no

financial

relationships.

Kansagra,

2017, USA

Observational:

before-after

study

(retrospective)

2511 patients undergoing an

elective hip or total knee

replacement in a tertiary

academic care center: 937 pre-

intervention (mean age 65.6

years, 41.8% males) versus 1574

peri/post intervention (mean

age 65.5 years, 40% males)


Comparison: After

(peri/post) versus before

PBM implementation

PBM components:


erythropoiesis)


blood loss)

The author(s)

received no financial

support for the

research, authorship,

and/or publication of

this article.

23

Category: orthopedic surgery


anemia)

Promotional

intervention(s):

- Guideline

- Education

- Audit/feedback


PBM: 9 months


PBM: 15 months

Kopanidis,

2016,

Australia

Observational:

before-after

study

(retrospective)

200 patients undergoing total

knee arthroplasty and total hip

arthroplasty (Australian teaching

hospital): 100 before PBM

(68.1±9.7 years, 74% males)

versus 100 after PBM (67.2±10.9

years, 68% males)

Anemia was defined according

to the WHO criteria: Hb level of

less than 13 g/dL for men or

less than 12 g/dL for women.

Targeted physicians: not

specified



before PBM

implementation

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline


PBM: 24 months


PBM: 24 months

Funding for this

research was

received from the

Department of

Anaesthesia,

Austin Hospital

Research Fund.

The authors declare

that they have no

competing interests.

Leahy, 2014,

Australia

Observational:

before-after

study

(retrospective)

450-bed acute care teaching

hospital with a full range of

medical and surgical specialties

including cardiothoracic,

orthopaedic, vascular, colorectal

and trauma surgery: 57.327

admissions pre-PBM (49% >60

years) versus 69.920 admissions

post-PBM (48% >60 years)


Category: general surgery +

medical



intervention

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Form

- Education

- Audit

- Decision support

system


PBM: 12 months


PBM: 36 months

The 5-year program

received executive

support and funding

from theWA State

Health Executive

Forum and the

Health Department

of

WA and was

endorsed by the

Australian Red Cross

Blood

Service.

The authors declare

that they have no

conflict of interest

relevant

to the manuscript

submitted to

TRANSFUSION

Leahy, 2017,

Australia (1)

Observational:

before-after

695 admissions at 2 tertiary

hospitals receiving intensive

chemotherapy for acute



intervention

CM, SS, and HC have

disclosed no

24

study

(retrospective)

leukemia or undergoing

haematopoietic stem cell

transplantation: 133 inpatient

admissions before PBM

(48.14±16.61 years) versus 562

admissions after PBM (2011-

2012 (n=143): 47.99±12.99;

2012-2013 (n=166):

50.33±14.04 years; 2013-2014

(n=153): 50.19±14.93; 2014-

2015 (n=100): 49.45±14.76

years)


Category: Medical - Oncology

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Form

- Education

- Audit

- Principles of

Kotter model


PBM: 18 months


PBM: 54 months


ML reports

honorarium from

Vifor Pharma. KT

reports nonfinancial

support from

Medical Society for

Blood Management,

nonfinancial support

from The Health

Roundtable, outside

the submitted work.

SF reports personal

fees fromThieme

(Stuttgart, Germany)

and Elsevier Science

USA and


fromthe National

Blood Authority

(Australia), the

Medical Society for

Blood Management,

and The Health

Roundtable, outside

the submitted work.

Leahy, 2017,

Australia (2)

Observational:

before-after

study

(retrospective)

605.046 patients admitted to

four major adult tertiary-care

hospitals


Category: general surgery and

medical settings



intervention

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Form

- Education

- Audit

- Principles of

Kotter model


PBM: 18 months


PBM: 54 months

Axel Hofmann

reports personal fees

from Vifor Pharma

AG and TEM

International GmbH,

outside the

submitted work.

Simon Towler


support from the

Medical Society for

Blood Management

and the National

Blood Authority

(Australia),

outside the

submitted work.

Kevin M. Trentino


support from the

Medical Society for

Blood Management

and The Health

Roundtable, outside

the submitted work.

Shannon L. Farmer

reports personal fees

from Thieme

(Stuttgart, Germany)

and Elsevier Science

USA and


from the National

Blood Authority

(Australia), the

Medical Society for

25

Blood Management,

and The Health

Round Table, outside

the submitted work.

Michael Leahy

reports honorarium

from Vifor Pharma.

The remaining

authors made no

disclosures.

Loftus, 2016,

USA

Observational:

before-after

study

(retrospective)

12.590 patients from 16

different hospitals undergoing

total hip arthroplasty or total

knee arthroplasty: 5.997 before

PBM (69.64±11.52 years, 37.3%

males) versus 6.593 after PBM

(69.51±11.54 years, 38.3%

males)

Targeted physicians: not

specified




intervention

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Decision support

system


PBM: 12 months


PBM: 12 months

One or more of the

authors of this paper

have disclosed

potential or

pertinent conflicts

of interest, which

may include receipt

of payment, either

direct or indirect,

institutional

support, or

association with an

entity in the

biomedical field

which may be

perceived to have

potential conflict of

interest with this

work

Mehra, 2015,

Switzerland

Observational:

cohort study

(prospective)

101,794 patients aged 18 years

or older were included: 66525

(median age 55 years (37-69)

and 49.5% males) before PBM

and 35269 after PBM (median

age 55 years (37-70) and 48.7%

males)


Category: general surgery and

medical



intervention

PBM components:


erythropoiesis)


blood loss)


anemia)

Intervention(s):

- Monitoring and

feedback (via

mandatory

transfusion

documentation in

electronic

medical patient

records

- Guideline

- Audit


implementation

Intervention: 20 months


implementation: 12 months

Senior author

(Spahn) reports

conflict of interest,

the remaining

authors have

disclosed no conflict

of interest.

26

Meybohm,

2016,

Germany

Observational:

before-after

study

129.719 surgical inpatients from

4 German University Hospitals

discharged between July 2012

and June 2015 with different

inclusion periods for pre-PBM

(54.513 patients): 55.7±0.8

years, 51% males and PBM

(75.206 patients): 55.6±0.8

years, 51% males.

All types of surgical procedures

(according to the German

surgery and procedure

classification) were included,

except ophthalmologic surgery,

dermatologic and non-specified

surgery






Category: general surgery


before PBM

implementation

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Education

- Other: handouts,

laminated cards,

posters,

checklists, and

web-based

information


PBM: 12 months


PBM: 12-30 months

This work is

supported by

internal

departmental

funding of each

center and by

research grants

provided by B. Braun

Melsungen AG,

Melsungen,

Germany; CSL

Behring, Marburg,

Germany; Fresenius

Kabi, Bad Homburg,

Germany; and Vifor

Pharma Deutschland

GmbH, Muenchen,

Germany. The

Funders of the study

had no role in design

and conduct of the

study; collection,

management,

analysis, and

interpretation of the

data; and

preparation, review,

or

approval of the

article.

Independence of

researchers from

funders is confirmed

Rineau, 2016,

France

Observational:

uncontrolled

before-after

study

(prospective)

367 patients admitted for

total hip or knee arthroplasty

(University Hospital of Angers,

France): 184 during Phase 1

(103 THA and 81 TKA):

71.1±10.5 years and 47% males

and 183 during Phase 2 (115

THA and 68 TKA): 70.1 ± 10.8

years and 43% males.







Comparison: after: Phase 2

(PBM protocol) versus

before: Phase 1 (no PBM

protocol)

PBM components:


erythropoiesis)


anemia)

Phase 1: EPO, iron and

tranexamic acid (with a

non-written protocol by

anesthesiologists,

physicians were not

involved) (November 2011

– December 2012)

Phase 2: combination EPO,

iron and tranexamic acid in

peri-operative period with

a protocol (posters at the

anesthesia consultation). A

restrictive transfusion

trigger (7-8 g/dL) or higher

triggers in case of poor

clinical or biologic

Senior author

(Sigismond Lasocki)

received consulting

fees from Vifor

Pharma, all other

authors have

disclosed no conflict

of interest.

27

tolerance of anemia or

acute coronary syndrome

(June 2012 – December

2012)

Promotional

intervention(s):

- Guideline

- Form

- Education


PBM: 4 months


PBM: 6 months

Ternström,

2014, Sweden

Observational:

before-after

study

(prospective)

2162 adult patient who

underwent cardiac surgery:

1128 before PBM (65.5±12.1

years, 75% males) and 1034

after PBM (65.4±12.3 years, 75%

males).



Comparison: after PBM

implementation versus

before PBM

implementation

PBM components:


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Education

- Form


PBM: 12 months


PBM: 12 months

This work was

supported by the

Swedish Heart-Lung

Foundation; and

the Göteborg

Medical Society.

Conflict of interest:

none declared.

Thakkar, 2016,

USA

Observational:

before-after

study

(retrospective)

428-bed academic medical

center: 20.531 patients before

PBM (50±23 years, 47.9% males)

versus 19.477 patients after

PBM (50±24 years, 46.7% males)


Category: general surgery -

medical



before PBM

implementation

PBM components:


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Education

- Audit/feedback

- Decision support

system


PBM: 12 months


PBM: 12 months

The authors have

disclosed no


Theusinger,

2014,

Switzerland

Observational:

before-after

study

8871 preoperative adult elective

orthopaedic surgery patients:



This study was

supported by

departmental funds

28

(prospective

and

retrospective)

hip (n=3062): 2265 after PBM

(59±17 years, 47.4% males)

versus 797 before PBM (64±14

years, 43.70% males)

knee (n=2953): 2243 after PBM

(61±16 years, 46.8% males)

versus 710 before PBM (67±12

years, 38.5% males)

spine (n=2856)): 2213 after PBM

(62±15 years, 47% males) versus

643 before PBM (60±15 years,

43% males)



before PBM

implementation

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Guideline


PBM: 12 months


PBM: 36 months

and by a grant from

the

Gesundheitsdirektion

des Kantons

Zürichs for Highly

Specialized

Medicine.

Verdecchia,

2016, USA

Observational:

before-after

study

(retrospective)

7 hospitals in a regional, multi-

hospital healthcare system:

number of

patients/admissions/age/gender

per group was not reported


Category: general surgery -

medical



before PBM

implementation

PBM components:


erythropoiesis)


blood loss)


anemia)

Promotional

intervention(s):

- Audit/feedback

- Decision support

system (CPOE)

- Education


PBM: 12 months


PBM: 96 months

No funding

No conflict of

interest to disclose

Xydas, 2012,

USA

Observational:

before-after

study

(prospective)

1032 patients undergoing

coronary and valve procedures:

481 patients (median age 69,

range 22-99 years, 69% males)

before PBM versus 551 patients

after PBM (median age 71,

range 31-92 years, 67% males)





before PBM

implementation

PBM components:


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Decision support

system

(algorithm-driven

decision)

No information

available

29


PBM: 6 months


PBM: 6 months

Yaffee, 2014,

USA

Observational:

before-after

study

(retrospective)

778 patients who underwent

aortic valve replacement: 391

before PBM (71.9±13.2 years,

62.1% males) versus 387 after

PBM (71.6±13.4 years, 56.6%

males)

Targeted physicians: cardiac

surgical care providers




before PBM

implementation

PBM components:


blood loss)


anemia)

Promotional

intervention(s):

- Guideline

- Education


PBM: 24 months


PBM: 24 months

No information

available

Synthesis of findings

Outcome Comparison Effect Size #studies, # participants Reference

BLOOD PRODUCT UTILIZATION

Number of patients receiving RBC transfusion (figure 1)

Patients receiving RBC After PBM versus before

PBM


11431/75206 vs 9392/54513

RR: 0.88, 95%CI [0.86;0.90]

(p<0.00001)


1, 129719 Meybohm,

2016

Patients receiving RBC

transfusion


PBM


473/2275 vs 152/387

RR: 0.53, 95%CI [0.46;0.61]

(p<0.001)


1, 2662 (2275 vs 387)

Gross, 2015

Proportion of

admissions receiving

RBC transfusion


PBM


2097/69920 vs 1874/57327

RR: 0.92, 95%CI [0.86;0.98] (p<0.05)


1, 127.247 (69.920 vs

57.327)

Leahy, 2014

Number of RBC

transfusions


PBM


391/562 vs 111/133

RR: 0.83, 95%CI [0.76.0.92]

(p<0.05)


1, 695 (562 vs 133) Leahy, 2017

(1)

Proportion of patients

that received RBC

transfusion


PBM


470/1034 vs 656/1128

RR: 0.78, 95%CI [0.72;0.85]

(p<0.05)


1, 1034 vs 1128 Ternstrom,

2014


that received RBC

transfusion


PBM


1398/19477 vs 1579/20531

RR: 0.93, 95%CI [0.87;1.00]

(p>0.05)

1, 40008 (19477 vs 20531) Thakkar, 2016


receiving transfusion

with RBCs


PBM


258/551 vs 288/481

RR: 0.78, 95%CI [0.70;0.88]

(p<0.05)


1, 1032 (551 vs 481) Xydas, 2012

30


receiving any RBC

transfusion


PBM


263/387 vs 324/391

RR: 0.82, 95%CI [0.76;0.89]

(p<0.05)


1, 778 (387 vs 391) Yaffee, 2014

Number of RBC

transfusions


PBM


31133/293163 vs 13210/117444

RR: 0.94, 95%CI [0.93;0.96]

(p<0.05)


1, 293163 vs 117444 Frank, 2017

Total patients with

RBC transfusion


PBM


260/1574 vs 344/937

RR: 0.45, 95%CI [0.39;0.52]

(p<0.05)


1, 1574 vs 937 Kansagra,

2017

Number of patients receiving platelet transfusion (figure 2)

Patients receiving

PLTs transfusion


PBM


222/2275 vs 68/387

RR: 0.56, 95%CI [0.43;0.71]

(p<0.001)


1, 2662 (2275 vs 387)

Gross, 2015

Number of PLT

transfusions


PBM


435/562 vs 121/133

RR: 0.85, 95%CI [0.79;0.91]

(p<0.05)


1, 695 (562 vs 133) Leahy, 2017


that received platelet

transfusion


PBM


167/1034 vs 231/1128

RR: 0.79, 95%CI [0.66;0.94]

(p<0.05)



2014


that received platelet

transfusion


PBM


284/19477 vs 296/20531

RR: 1.01, 95%CI [0.86;1.19]

(p=XX)

1, 40008 (19477 vs 20531) Thakkar, 2016



with platelets


PBM


148/551 vs 125/481

OR: XX, 95%CI [XX;XX]

(p>0.05)

1, 1032 (551 vs 481) Xydas, 2012

Number of PLT

transfusions


PBM


8164/293163 vs 3610/117444

RR: 0.91, 95%CI [0.87;0.94]

(p<0.05)


1, 293163 vs 117444 Frank, 2017

Number of patients receiving plasma transfusion (figure 3)

Patients receiving FFP

transfusion


PBM


147/2275 vs 70/387

RR: 0.36, 95%CI [0.27;0.46]

(p<0.001)


1, 2662 (2275 vs 387)

Gross, 2015


that received plasma

transfusion


PBM


199/1034 vs 347/1128

RR: 0.63, 95%CI [0.54;0.73]

(p<0.05)



2014



transfusion


PBM


314/19477 vs 411/20531

RR: 0.81, 95%CI [0.70;0.93] (p<0.05)


1, 40008 (19477 vs 20531) Thakkar, 2016



with FFP


PBM


110/551 vs 115/481

RR: 0.84, 95%CI [0.66;1.05]

1, 1032 (551 vs 481) Xydas, 2012

31

(p>0.05)

Number of plasma

transfusions


PBM


3371/293163 vs 7004/117444

RR: 0.19, 95%CI [0.19;0.20]

(p<0.05)


1, 293163 vs 117444 Frank, 2017

Number of patients receiving cryoprecipitate transfusion (figure 4)



with cryoprecipitate


PBM


27/551 vs 76/481

RR: 0.31, 95%CI [0.20;0.47]

(p<0.05)


1, 1032 (551 vs 481) Xydas, 2012

Number of patients receiving any transfusion (RBC, plasma or platelets) (figure 5)

Transfusion for total

hip/knee replacement


PBM


9/406 vs 107/717

RR: 0.15, 95%CI [0.08;0.29]

(p<0.05)


1, 1123 (406 vs 717) Frew, 2016

Number of patients

who received

transfusions


PBM


6/100 vs 20/100

RR: 0.30, 95%CI [0.13;0.72]

(p<0.05)

In favour of PBM

1, 200 Kopanidis,

2016

Number of patients

who received

transfusions


PBM


5/167 vs 24/185

RR: 0.23, 95%CI [0.09;0.59]

(p<0.05)

In favour of PBM

1, 352 Rineau, 2016

Transfusion rate After PBM versus before

PBM


771/6593 vs 1253/5997

RR: 0.56, 95%CI [0.52;0.61]

(p<0.001)


1, 12590 (6593 vs 5997) Loftus, 2016


that received

transfusion (RBC, FFP,

PLT)


PBM


499/1034 vs 686/1128

RR: 0.79, 95%CI [0.73;0.86]

(p<0.05)



2014



with any blood

product use


PBM


292/551 vs 303/481

RR: 0.84, 95%CI [0.76;0.93]

(p<0.05)


1, 1032 (551 vs 481) Xydas, 2012

Number of RBC units transfused (figure 6a and figure 6b)

RBC units per patients After PBM versus before

PBM


0.61±1.57 vs 1.28±2.34

MD: -0.67, 95%CI [-0.91;-0.43]

(p<0.001)


1, 2662 (2275 vs 387)

Gross, 2015

RBC units per patient After PBM versus before

PBM


1.00±13.7 vs 1.21±11.6

MD: -0.21, 95%CI [-0.35;-0.07]

(p<0.001)


1, 129719 (75206 vs

54513)

Meybohm,

2016

Number of RBC units

transfused


PBM


2.8±6.6 vs 3.6±7.5

MD: -0.80, 95%CI [-1.39;-0.21]

(p<0.05)



2014

Allogeneic PRBCs After PBM versus before

PBM


1.5±2.2 vs 2.1±2.4

MD: -0.60, 95%CI [-0.88;-0.32]

1, 1032 (551 vs 481) Xydas, 2012

32

(p<0.05)


RBCs After PBM versus before

PBM


2.9±4.3 vs 4.6±5.6

MD: -1.70, 95%CI [-2.40;-1.00]

(p<0.05)


1, 778 (387 vs 391) Yaffee, 2014

RBC units transfused After PBM versus before

PBM


5±8 vs 5±10

MD: 0, 95%CI [-0.38;0.38]

(p>0.05)


1, 9243 (2927 vs 6316) Mehra, 2015

RBC units per

admission


PBM


0.112 (95%CI: 0.109-0.114) vs 0.083

(95%CI: 0.081-0.085)

MD: 0.029 (p<0.001)


1, 127247 (69.920 vs

57.327)

Leahy, 2014

Proportion of 2-unit

RBC use per

admission


PBM


25.870/69.920 vs 27.516/57.327

RR: 0.87, 95%CI [0.86;0.88]

(p<0.001)


1, 127.247 (69.920 vs

57.327)

Leahy, 2014

Proportion of ≥3-unit

RBC use per

admission


PBM


23.772/69.920 vs 22.357/57.327

RR: 0.77, 95%CI [0.76;0.78]

(p<0.001)


1, 127.247 (69.920 vs

57.327)

Leahy, 2014

Number of RBC units

transfused


PBM


No raw data available

RR: 0.59, 95%CI [0.58;0.60]

(p<0.001)


1, 493 (182 vs 311) Leahy, 2017

(2)

RBC units per 1000

patients


PBM


262.51/1000 vs 447.48/1000

RR: 0.59, 95%CI [0.52;0.66]

(p<0.0001)


1, 12590 (6593 vs 5997) Loftus, 2016

Number of RBC units

per patient


PBM


0.217 vs 0.247 (p=0.035)


1, 40008 (19477 vs 20531) Thakkar, 2016

Number of RBC units

transfused


PBM



29.9% decrease (p=0.002)


1, ~60000 (year 9) vs

~80000

Verdecchia,

2016

RBC units per patient After PBM versus before

PBM


3.5±4.9 vs 4.0±6.5

MD: -0.50, 95%CI [-0.54;-0.46]

(p<0.0001)


1, 293163 vs 117444 Frank, 2017

Proportion of ≥1 RBC-

unit transfusions


PBM


6593/8664 vs 6785/8450

RR: 0.95, 95%CI [0.93;0.96]

(p<0.001)


1, 8664 vs 8450 Gani, 2017

Proportion of 2 RBC-

unit transfusions


PBM


2434/8664 vs 2560/8450

RR: 0.93, 95%CI [0.88;0.97]

(p<0.001)


1, 8664 vs 8450 Gani, 2017

RBC units transfused

per 1000 patients


PBM


270/1000 vs 626/1000

RR: 0.43, 95%CI [0.39;0.48]

1, 1574 vs 937 Kansagra,

2017

33

(p<0.001)


Number of plasma units transfused (figure 7)

FFP units per patient After PBM versus before

PBM


0.23±1.05 vs 0.78±1.98

MD: -0.55, 95%CI [-0.75;-0.35]

(p<0.001)


1, 2662 (2275 vs 387)

Gross, 2015

FFP units transfused After PBM versus before

PBM


1±7 vs 2±12

MD: -1.00, 95%CI [-1.39;-0.61]

(p<0.05)


1, 9243 (2927 vs 6316) Mehra, 2015

Number of plasma

units transfused


PBM


1.9±7.3 vs 2.4±7.9

MD: -0.50, 95%CI [-1.14;0.14]

(p>0.05)


2014

FFP After PBM versus before

PBM


0.9±2.3 vs 1.5±3.1

MD: -0.60, 95%CI [-0.94;-0.26]

(p<0.05)


1, 1032 (551 vs 481) Xydas, 2012

Fresh frozen plasma After PBM versus before

PBM


1.1±2.8 vs 0.9±2.1

MD: 0.20, 95%CI [-0.15;0.55]

(p>0.05)

1, 778 (387 vs 391) Yaffee, 2014

Number of FFP units

transfused


PBM



RR: 0.53, 95%CI [0.51;0.55]

(p<0.001)


1, 144 (50 vs 94) Leahy, 2017

(2)

Number of plasma

units per patient


PBM


0.063 vs 0.073 (p=0.298)


1, 40008 (19477 vs 20531) Thakkar, 2016

Number of plasma

units transfused


PBM



24.8% decrease (p=0.036)


1, ~30000 (year 9) vs

~40000

Verdecchia,

2016

Plasma units per

patient


PBM


4.6±13.5 vs 6.1±14.9

MD: -1.50, 95%CI [-1.60;-1.40]

(p=0.0014)


1, 293163 vs 117444 Frank, 2017

Number of platelet units transfused (figure 8)

PLTs units per patient After PBM versus before

PBM


0.17±0.65 vs 0.39±1.03

MD: -0.22, 95%CI [-0.33;-0.11]

(p<0.001)


1, 2662 (2275 vs 387)

Gross, 2015

PLT units transfused After PBM versus before

PBM


1±5 vs 2±5

MD: -1, 95%CI [-1.22;-0.78]

(p<0.05)


1, 9243 (2927 vs 6316) Mehra, 2015

Number of platelet

units transfused


PBM


0.57±1.7 vs 0.77±2.3

MD: -0.20, 95%CI [-0.37;-0.03]

(p<0.05)



2014

Platelets After PBM versus before

PBM


0.5±0.9 vs 0.4±0.9

MD: 0.1, 95%CI [-0.01;0.21]

1, 1032 (551 vs 481) Xydas, 2012

34

(p>0.05)

Platelets After PBM versus before

PBM


0.8±1.7 vs 2.1±3.5

MD: -1.30, 95%CI [-1.69;-0.91]

(p<0.05)


1, 778 (387 vs 391) Yaffee, 2014

Number of platelet

units transfused


PBM



RR: 0.73, 95%CI [0.70;0.76]

(p<0.001)


1, 38 vs 53 Leahy, 2017

(2)

Number of platelet

units per patient


PBM


0.031 vs 0.028 (p=0.53)


1, 40008 (19477 vs 20531) Thakkar, 2016

Number of platelet

units transfused


PBM



25.7% decrease (p=0.003)


1, ~60000 (year 9) vs

~75000

Verdecchia,

2016

Platelet units per

patient


PBM


4.8±8.1 vs 5.1±8.1

MD: -0.30, 95%CI [-0.35;-0.25]

(p=0.002)


1, 293163 vs 117444 Frank, 2017

Number of cryoprecipitate units transfused (figure 9)

Cryoprecipitate After PBM versus before

PBM


0.6±2.9 vs 1.8±4.7

MD: -1.20, 95%CI [-1.68;-0.72]

(p<0.00001)


1, 1032 (551 vs 481) Xydas, 2012

Number of any blood products transfused (figure 10)

Blood products

transfused all types


PBM


7±14 vs 9±19

MD: -2.00, 95%CI [-2.69;-1.31]

(p<0.00001)


1, 9243 (2927 vs 6316) Mehra, 2015

Number of units

transfused (RBC, FFP,

PLT)


PBM


5.3±15 vs 6.8±17

MD: -1.50, 95%CI [-2.85;-0.15]

(p<0.05)



2014

Total blood product

use


PBM


3.5±6.9 vs 5.8±9.8

MD: -2.30, 95%CI [-3.35;-1.25]

(p<0.0001)


1, 1032 (551 vs 481) Xydas, 2012

All blood products After PBM versus before

PBM


5.1±8.8 vs 9.0±12.1

MD: -3.90, 95%CI [-5.39;-2.41]

(p<0.00001)


1, 778 (387 vs 391) Yaffee, 2014

Number of units of

blood transfused


PBM


2 [2:4.75] (median, IQR) vs 2 (1:2.75]

(p=0.126)

1, 200 (100 vs 100) Kopanidis,

2016

BLOOD PRODUCT UTILIZATION – SUBGROUP: indication

Number of patients receiving RBC transfusion (figure 11)

Patients transfused

(Neurosurgery)


PBM


1433/9886 vs 1098/7135

RR: 0.94, 95%CI [0.88;1.01]

(p>0.05)

1, 17021 Meybohm,

2016

Patients transfused

(Otorhinolaryngology)


PBM


1415/11701 vs 1129/9257

RR: 0.99, 95%CI [0.92;1.07]

1, 20958 Meybohm,

2016

35

(p>0.05)

Patients transfused

(Thoracic)


PBM


1145/2609 vs 881/1951

RR: 0.97, 95%CI [0.91;1.04]

(p>0.05)

1, 4560 Meybohm,

2016

Patients transfused

(Cardiac)


PBM


4038/7904 vs 3406/5630

RR: 0.84, 95%CI [0.82;0.87]

(p<0.05)


1, 13534 Meybohm,

2016

Patients transfused

(Vascular)


PBM


2090/5823 vs 1755/4377

RR: 0.90, 95%CI [0.85;0.94]

(p<0.05)


1, 10200 Meybohm,

2016

Patients transfused

(Visceral and

endocrine)


PBM


2838/13649 vs 2052/9164

RR: 0.93, 95%CI [0.88;0.98]

(p<0.05)


1, 22813 Meybohm,

2016

Patients transfused

(Urology)


PBM


999/7243 vs 800/4791

RR: 0.83, 95%CI [0.76;0.90]

(p<0.05)


1, 12034 Meybohm,

2016

Patients transfused

(Gynaecology)


PBM


279/5174 vs 213/4029

RR: 1.02, 95%CI [0.86;1.21]

(p>0.05)

1, 9203 Meybohm,

2016

Patients transfused

(Obstetric)


PBM


138/6308 vs 111/4145

RR: 0.82, 95%CI [0.64;1.05]

(p>0.05)

1, 10453 Meybohm,

2016

Patients transfused

(Oral and

maxillofacial)


PBM


136/3105 vs 118/2361

RR: 0.88, 95%CI [0.69;1.11]

(p>0.05)

1, 5466 Meybohm,

2016

Patients transfused

(Trauma/Orthopedic)


PBM


2852/16298 vs 2438/12633

RR: 0.91, 95%CI [0.86;0.95]

(p<0.05)


1, 28931 Meybohm,

2016

Proportion of


RBC transfusion

(Cardiothoracic

surgery)


PBM


34/266 vs 81/295

RR: 0.47, 95%CI [0.32;0.67]

(p<0.05)


1, 561 (266 vs 295) Leahy, 2014

Proportion of


RBC transfusion

(Gastroenterology)


PBM


27/3980 vs 11/3778

RR: 2.33, 95%CI [1.16;4.69]

(p<0.05)

In favour of no PBM program

1, 7758 (3980 vs 3778) Leahy, 2014

Proportion of


RBC transfusion

(General surgery)


PBM


56/1276 vs 61/1853

RR: 1.33, 95%CI [0.93;1.90]

(p>0.05)

1, 3129 (1276 vs 1853) Leahy, 2014

Proportion of


RBC transfusion

(Gerontology)


PBM


12/753 vs 20/777

RR: 0.62, 95%CI [0.30;1.26]

(p>0.05)

1, 1530 (753 vs 777) Leahy, 2014

Proportion of



PBM


478/2238 vs 263/1997

1, 4235 (2238 vs 1997) Leahy, 2014

36

RBC transfusion

(Hematology)

RR: 1.62, 95%CI [1.41;1.86]

(p<0.05)


Proportion of


RBC transfusion

(Nephrology)


PBM


28/14135 vs 46/11519

RR: 0.50, 95%CI [0.31;0.79]

(p<0.05)


1, 25654 (14.135 vs

11.519)

Leahy, 2014

Proportion of


RBC transfusion

(Oncology)


PBM


54/2888 vs 65/2836

RR: 0.82, 95%CI [0.57;1.17]

(p>0.05)

1, 5724 (2888 vs 2836) Leahy, 2014

Proportion of


RBC transfusion

(Orthopedics)


PBM


63/2343 vs 96/2669

RR: 0.75, 95%CI [0.55;1.02]

(p>0.05)

1, 5012 (2343 vs 2669) Leahy, 2014

Proportion of


RBC transfusion

(Urology)


PBM


24/2474 vs 7/1947

RR: 2.70, 95%CI [1.17;6.25]

(p<0.05)


1, 4421 (2474 vs 1947) Leahy, 2014

Proportion of


RBC transfusion

(Vascular surgery)


PBM


17/406 vs 19/471

RR: 1.04, 95%CI [0.55;1.97]

(p>0.05)

1, 877 (406 vs 471) Leahy, 2014


that received RBC

transfusion (surgery)


PBM


570/6153 vs 683/6430

RR: 0.87, 95%CI [0.78;0.97]

(p<0.05)


1, 12583 (6153 vs 6430) Thakkar, 2016


that received RBC

transfusion (medical)


PBM


830/13325 vs 895/14101

RR: 0.98, 95%CI [0.90;1.08]

(p>0.05)

1, 27426 (13325 vs 14101) Thakkar, 2016

Number of patients receiving plasma transfusion (figure 12)





PBM


154/6153 vs 190/6430

RR: 0.85, 95%CI [0.69;1.04]

(p>0.05)

1, 12583 (6153 vs 6430) Thakkar, 2016





PBM


160/13325 vs 221/14101

RR: 0.77, 95%CI [0.63;0.94]

(p<0.05)


1, 27426 (13325 vs 14101) Thakkar, 2016

Number of patients receiving platelet transfusion (figure 13)


that received platelets



PBM


147/6153 vs 158/6430

RR: 0.97, 95%CI [0.78;1.21]

(p>0.05)

1, 12583 (6153 vs 6430) Thakkar, 2016


that received platelets



PBM


137/13325 vs 137/14101

RR: 1.06, 95%CI [0.84;1.34]

(p>0.05)

1, 27426 (13325 vs 14101) Thakkar, 2016

Number of patients receiving any transfusion (RBC, plasma or platelets) (figure 14)

Patients transfused

(RBC/FFP/PLT)

(Hip surgery)


PBM


355/2265 vs 173/797

RR: 0.72, 95%CI [0.61;0.85]

(p<0.05)

In favour of PBM group

1, 3062 (2265 vs 797) Theusinger,

2014

37

Patients transfused

(RBC/FFP/PLT)

(Knee surgery)


PBM


109/2243 vs 137/710

RR: 0.25, 95%CI [0.20;0.32]

(p<0.05)


1, 2953 (2243 vs 710) Theusinger,

2014

Patients transfused

(RBC/FFP/PLT)

(Spine surgery)


PBM


190/2213 vs 120/643

RR: 0.46, 95%CI [0.37;0.57]

(p<0.05)


1, 2856 (2213 vs 643) Theusinger,

2014

Number of RBC units transfused (figure 15)

RBC units per patient

(Neurosurgery)


PBM


0.75±3.9 vs 0.88±3.3

MD: -0.13, 95%CI [-0.24;-0.02]

(p<0.05)


1, 17021 (9886 vs 7135) Meybohm,

2016




PBM


1.74±24.8 vs 1.90±22.1

MD: -0.16, 95%CI [-0.80;0.48]

(p>0.05)

1, 20958 (11701 vs 9257) Meybohm,

2016


(Thoracic)


PBM


6.38±14.3 vs 7.40±14.5

MD: -1.02, 95%CI [-1.87;-0.17]

(p<0.05)


1, 4560 (2609 vs 1951) Meybohm,

2016


(Cardiac)


PBM


3.68±40.8 vs 4.65±34.5

MD: -0.97, 95%CI [-2.24;0.30]

(p>0.05)

1, 13534 (7904 vs 5630) Meybohm,

2016


(Vascular)


PBM


3.77±39.6 vs 4.90±35.0

MD: -1.13, 95%CI [-2.58;0.32]

(p>0.05)

1, 10200 (5823 vs 4377) Meybohm,

2016


(Visceral and

endocrine)


PBM


2.09±18.6 vs 2.39±15.3

MD: -0.30, 95%CI [-0.74;0.14]

(p>0.05)

1, 22813 (13649 vs 9164) Meybohm,

2016


(Urology)


PBM


1.00±10.2 vs 1.15±8.3

MD: -0.15, 95%CI [-0.48;0.18]

(p>0.05)

1, 12034 (7243 vs 4791) Meybohm,

2016


(Gynaecology)


PBM


0.28±5.0 vs 0.32±4.4

MD: -0.04, 95%CI [-0.23;0.15]

(p>0.05)

1, 9203 (5174 vs 4029) Meybohm,

2016


(Obstetric)


PBM


0.09±2.3 vs 0.37±5.7

MD: -0.28, 95%CI [-0.46;-0.10]

(p<0.05)


1, 10453 (6308 vs 4145) Meybohm,

2016


(Oral and

maxillofacial)


PBM


0.22±5.0 vs 0.37±4.3

MD: -0.15, 95%CI [-0.40;0.10]

(p>0.05)

1, 5466 (3105 vs 2361) Meybohm,

2016


(Trauma/Orthopedic)


PBM


1.24±20.4 vs 1.46±17.98

MD: -0.22, 95%CI [-0.66;0.22]

(p>0.05)

1, 28931 (16298 vs 12633) Meybohm,

2016


(Hip Surgery)


PBM


0.62±1.96 vs 0.82±1.94

MD: -0.20, 95%CI [-0.36;-0.04]

(p<0.05)

1, 3062 (2265 vs 797) Theusinger,

2014

38



(Knee surgery)


PBM


0.16±0.90 vs 0.53±1.27

MD: -0.37, 95%CI [-0.47;-0.27]

(p<0.05)


1, 2953 (2243 vs 710) Theusinger,

2014


(Spine surgery)


PBM


0.22±0.89 vs 0.66±1.80

MD: -0.44, 95%CI [-0.58;-0.30]

(p<0.05)


1, 2856 (2213 vs 643) Theusinger,

2014

MORBIDITY (composite measures) (figure 16)

Postoperative

complication (transient

ischemic attacks,

cerebrovascular accidents,

myocardial infarctions,

ventilator-associated

pneumonia,

urinary tract infections,

surgical site infections,

sepsis, drug-resistant

infections, Clostridium

difficile infections, deep

venous thrombosis,

pulmonary embolism,

disseminated intravascular

coagulation,

and acute renal failure)


PBM


1240/8664 vs 1382/8450

OR: 0.85, 95%CI [0.79;0.93]

(p<0.05)


1, 8664 vs 8450 Gani, 2017

In-hospital

complications

(cardiac, vascular,

acute kidney injury,

respiratory,

neurologic, digestive)


PBM

Not statistically significant

14/183 vs 11/184

OR: 1.30, 95%CI [0.58;2.95]

(p>0.05)

1, 367 Rineau, 2016

In-hospital

complications (death,

myocardial infarction,

ischemic stroke, acute

renal failure,

pneumonia, sepsis)


PBM

Not statistically significant

4768/75206 vs 3559/54513

Adjusted OR: 0.96, 95%CI [0.92;1.00]

(p=0.079)

1, 129719 Meybohm,

2016

Major post-operative

complications (acute

coronary syndrome,

exacerbation of

chronic lung disease,

exacerbation of heart

failure, acute kidney

injury, pulmonary

embolism, narcotised

from analgesia)


PBM


7/100 vs 5/100

OR: 1.43, 95%CI [0.44;4.67] (p>0.05)

1, 200 Kopanidis,

2016

Any morbidity

(infection, thrombotic

event, kidney injury,

respiratory event,

ischemic event)


PBM


205/19477 vs 207/20531

OR: 1.04, 95%CI [0.86;1.27] (p>0.05)

1, 40008 (19477 vs 20531) Thakkar, 2016

Major complications

(death, renal failure

requiring dialysis,

respiratory failure,

sepsis)


PBM


42/387 vs 35/391

OR: 1.24, 95%CI [0.77;1.99] (p>0.05)

1, 778 (387 vs 391) Yaffee, 2014

MORBIDITY (composite measures) – SUBGROUP: indication (figure 17)

In-hospital

complications

(Neurosurgery)


PBM


948/9886 vs 642/7135

Adjusted OR: 1.05, 95%CI [0.95;1.17]

1, 17021 Meybohm,

2016

39

(p>0.05)

In-hospital

complications



PBM


1116/11701 vs 870/9257

Adjusted OR: 1.00, 95%CI [0.91;1.09]

(p>0.05)

1, 20958 Meybohm,

2016

In-hospital

complications

(Thoracic)


PBM


671/2609 vs 514/1951

Adjusted OR: 0.99, 95%CI [0.86;1.13]

(p>0.05)

1, 4560 Meybohm,

2016

In-hospital

complications

(Cardiac)


PBM


1293/7904 vs 957/5630

Adjusted OR: 0.96, 95%CI [0.87;1.05]

(p>0.05)

1, 13534 Meybohm,

2016

In-hospital

complications

(Vascular)


PBM


946/5823 vs 836/4377

Adjusted OR: 0.82, 95%CI [0.74;0.91]

(p<0.05)


1, 10200 Meybohm,

2016

In-hospital

complications

(Visceral and

endocrine)


PBM


1532/13649 vs 1071/9164

Adjusted OR: 0.94, 95%CI [0.87;1.03]

(p>0.05)

1, 22813 Meybohm,

2016

In-hospital

complications

(Urology)


PBM


413/7243 vs 300/4791

Adjusted OR: 0.90, 95%CI [0.77;1.05]

(p>0.05)

1, 12034 Meybohm,

2016

In-hospital

complications

(Gynaecology)


PBM


47/5174 vs 35/4029

Adjusted OR: 1.05, 95%CI [0.68;1.62]

(p>0.05)

1, 9203 Meybohm,

2016

In-hospital

complications

(Obstetric)


PBM


8/6308 vs 12/4145

Adjusted OR: 0.57, 95%CI [0.25;1.28]

(p>0.05)

1, 10453 Meybohm,

2016

In-hospital

complications (Oral

and maxillofacial)


PBM


180/3105 vs 136/2361

Adjusted OR: 0.99, 95%CI [0.90;1.10]

(p>0.05)

1, 5466 Meybohm,

2016

In-hospital

complications

(Trauma/Orthopedic)


PBM


946/16298 vs 728/12633

Adjusted OR: 0.99, 95%CI [0.90;1.10]

(p>0.05)

1, 28931 Meybohm,

2016

MORBIDITY (individual measures)

Acute myocardial infarction (figure 18)

Acute coronary

syndrome


PBM


0/100 vs 3/100

RR: 0.14, 95%CI [0.01;2.73] (p>0.05)

1, 200 Kopanidis,

2016

Myocardial infarction After PBM versus before

PBM


0/551 vs 1/481

RR: 0.29, 95%CI [0.01;7.13] (p>0.05)

1, 1032 (551 vs 481) Xydas, 2012

Myocardial infarction After PBM versus before

PBM


0/387 vs 0/391

RR not estimable

1, 778 (387 vs 391) Yaffee, 2014

Acute ischaemic stroke (figure 19)

CVA After PBM versus before

PBM


58/2275 vs 13/387

RR: 0.76, 95%CI [0.42;1.37] (p>0.05)

1, 2662 (2275 vs 387)

Gross, 2015

Stroke After PBM versus before

PBM


18/1034 vs 12/1128

RR: 1.64, 95%CI [0.79;3.38]

(p>0.05)


2014

40

Transient ischemic

attack


PBM


4/551 vs 3/481

RR: 1.16, 95%CI [0.26;5.17] (p>0.05)

1, 1032 (551 vs 481) Xydas, 2012

Stroke After PBM versus before

PBM


12/387 vs 12/391

RR: 1.01, 95%CI [0.46;2.22] (p>0.05)

1, 778 (387 vs 391) Yaffee, 2014

Acute kidney injury (figure 20)

Kidney injury After PBM versus before

PBM


113/2275 vs 29/387

RR: 0.66, 95%CI [0.45;0.98] (p<0.05)

1, 2662 (2275 vs 387)

Gross, 2015

Acute kidney injury After PBM versus before

PBM


2/100 vs 1/100

RR: 2.00, 95%CI [0.18;21.71] (p<0.05)

1, 200 Kopanidis,

2016

Renal failure After PBM versus before

PBM


17/551 vs 15/481

RR: 0.99, 95%CI [0.50;1.96] (p>0.05)

1, 1032 (551 vs 481) Xydas, 2012

Renal failure requiring

dialysis


PBM


16/387 vs 13/391

RR: 1.24, 95%CI [0.61;2.55] (p>0.05)

1, 778 (387 vs 391) Yaffee, 2014

MORTALITY (individual measures)

Hospital mortality (figure 21)

In-hospital mortality After PBM versus before

PBM


0/183 vs 1/184

OR: 0.33, 95%CI [0.01;8.24]

(p>0.05)

1, 367 (183 vs 184) Rineau, 2016


PBM


124/8664 vs 109/8450

OR: 1.11, 95%CI [0.86;1.44]

(p<0.05)

1, 8664 vs 8450 Gani, 2017


PBM


100/2275 vs 15/387

OR: 1.14, 95%CI [0.66;1.98] (p>0.05)

1, 2662 (2275 vs 387)

Gross, 2015


PBM


11/562 vs 7/133

Adjusted OR: 0.31, 95%CI [0.06;1.56]

(p=0.154)

1, 695 (562 vs 133) Leahy, 2017

Hospital mortality After PBM versus before

PBM


348/6316 vs 748/2927

OR: XX, 95%CI [XX;XX] (p=XX)

1, 9243 (6316 vs 2927) Mehra, 2015


PBM


5/551 vs 4/481

OR: 1.09, 95%CI [0.29;4.09] (p>0.05)

1, 1032 (551 vs 481) Xydas, 2012


PBM


13/387 vs 10/391

OR: 1.32, 95%CI [0.57;3.06] (p>0.05)

1, 778 (387 vs 391) Yaffee, 2014

Mortality – not specified (figure 22)

Mortality After PBM versus before

PBM


0/100 vs 0/100

RR: not estimable

1, 200 (100 vs 100) Kopanidis,

2016


PBM


8/5997 vs 15/6593

RR: 0.59, 95%CI [0.25;1.38] (p>0.05)

1, 12590 (5997 vs 6593) Loftus, 2016


PBM


360/19477 vs 359/20531

RR: 1.06, 95%CI [0.91;1.22] (p>0.05)

1, 40008 (19477 vs 20531) Thakkar, 2016

30-day mortality (figure 23)

30-day mortality After PBM versus before

PBM


27/1034 vs 28/1128

RR: 1.15, 95%CI [0.68;1.93]

(p>0.05)


2014

41

30 day mortality After PBM versus before

PBM


9/551 vs 4/481

RR: 1.96, 95%CI [0.61;6.34] (p>0.05)

1, 1032 (551 vs 481) Xydas, 2012

LENGTH OF HOSPITAL STAY (figure 24)

Length of stay (days)

THA


PBM


8.8±2.3 vs 9.4±2.5

MD: -0.60, 95%CI [-1.24;0.04]

(p>0.05)

1, 218 (115 vs 103) Rineau, 2016

Length of stay (days)

TKA


PBM


10.1±3.4 vs 9.3±2.5

MD: 0.80, 95%CI [-0.17;1.77] (p>0.05

1, 149 (68 vs 81) Rineau, 2016

115 THA and

68 TKA

Hospital length of

stay (days)


PBM


10.2±46.6 vs 10.4±39.7

MD: -0.20, 95%CI [-0.67;0.27]

1, 129719 (75206 vs

54513)

Meybohm,

2016

Hospital length of

stay (days)


PBM


5 [3:9] (median, IQR) vs 5 (2:9]

(p>0.999)

1, 8664 vs 8450 Gani, 2017

Hospital length of

stay (days)


PBM


10.4±8.0 vs 12.2±9.6

MD: -1.80, 95%CI [-2.81;-0.79]

(p<0.001)


1, 2662 (2275 vs 387)

Gross, 2015

Hospital length of

stay (days)


PBM


3.02±1.76 vs 3.32±1.91

MD: -0.30, 95%CI [-0.36;-0.24]

(p<0.0001)


1, 12590 (6593 vs 5997) Loftus, 2016

Length of stay (hours) After PBM versus before

PBM


102 [84:125] (median, IQR) vs 101.3

(98:128]

(p=0.892)

1, 200 (100 vs 100) Kopanidis,

2016

Length of stay for

total hip replacement

(days)


PBM


6 (median), (5-8, IQR) vs 3 (2-4)

Median difference: 3, (p<0.001)


1, 547 (361 vs 186) Frew, 2016

Length of stay for

total knee

replacement (days)


PBM


6 (median), (5-8, IQR) vs 3 (3-5)

Median difference: 3, (p<0.001)


1, 576 (356 vs 220) Frew, 2016

Hospital length of

stay (days)


PBM


7 (6-8) vs 7 (6-9) (p=0.013)



2014

Hospital length of

stay (days)


PBM


7 (1-90) vs 7 (1-90) (p=0.3)

1, 778 (387 vs 391) Yaffee, 2014

ECONOMIC OUTCOMES

Direct cost of EPO,

iron, tranexamic acid

and blood transfusion


PBM

€30.572 versus €25.097 N/A Rineau, 2016

Total direct costs (€) After PBM versus before

PBM


44.300±25.915 vs 48.375±28.053

MD: XX, 95%CI [XX;XX] (p<0.001)


1, 2662 (2275 vs 387)

Gross, 2015

42

Mehra, 2015

Considering 65,925 transfused RBC units in the pre-PBM cohort at minimum mean purchase costs of 85€per

RBC unit in Germany, and an overall decrease in RBC utilization by 17%, the implementation of PBM was

associated with 952.660€ per year savings for RBC units. If costs for the RBC transfusion process would be

considered additionally (eg, for materials, labor, third-party services, and capital), the savings could reach more

than 3.000.000€ per year.

Meybohm

2016

1g intravenous iron: (9 x 4 doses) x £181.45 = £6,532

20,000 units erythropoietin: (9 x 4 doses) x £27.80 = £1,001

1g intravenous tranexamic acid: 406 doses x £1.30 = £528

Ferrous sulphate: 48 x £4.36 (2-month supply) = £209

Units of blood saved: (181–20) x £145.00 = -£23,345

Bed days saved: (406 x 3) x £400.00 = -£487,200

Total -£502,455

Frew, 2016

Across the study period, the total reduction in inpatient and day case transfusions translates to 1111 fewer RBC

units and 588 fewer PLT units. This represents direct product cost savings of AU$389,537 (US$364,177) and

AU$305,349 (US$289,830), respectively.

Leahy, 2017

Adopting a conservative approach, if the annual rate of transfusion remained at baseline year levels, then an

additional 50,115 units of blood would have been transfused over the study period, comprising 35,423 RBC

units, 10,721 of FFP units, and 3970 platelets units. Based on product-acquisition cost, the calculated savings

from this reduction is $18,507,092 in Australian dollars (AU$) and $18,078,258 in US dollars ($US).

Leahy, 2017

(2)

The estimated cost avoidance for this PBM program was $725,233 (range $497,466–$1,127,399) Loftus, 2016

Direct cost ($1000) After PBM versus before

PBM


10.77±5.58 vs 10.79±5.66

MD: XX, 95%CI [XX;XX] (p=0.8673)

1, 12590 (6593 vs 5997) Loftus, 2016

Ternstrom,

2014

Thakkar, 2016

43

The mean number of RBC units saved per patient after initiation of BCS was 1.7 units, resulting in an estimated

cost savings of $1,755 per patient, or $679,045 overall (n ¼ 387), for an annual savings of approximately

$339,522 for RBC transfusion alone in AVR patients.

Yaffee, 2014

Blood acquisition cost savings were $2,120,273/yr, an approximate 400% return on investment for our patient

blood management efforts.

Frank, 2017

Depending on the costing methodology used as described in the outcomes and data collection section,

annualized savings between time periods before and after PBM implementation ranged from a low of $108 000

using acquisition cost (~$225/unit) to $480 000 when using activity-based costing (~$1000/unit).

Kansagra,

2017

44

Forest plots

Figure 1: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received RBC

transfusion

45

Figure 2: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received PLT

transfusion

46

Figure 3: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received FFP

transfusion

Figure 4: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received

cryprecipitate transfusion

47

Figure 5: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received any

transfusion (RBC, PLT, FFP)

48

Figure 6a: Forest plot of outcome: Blood product utilization – Number of RBC units transfused (continuous)

Figure 6b: Forest plot of outcome: Blood product utilization – Number of RBC units transfused (dichotomous)

49

Figure 7: Forest plot of outcome: Blood product utilization – Number of FFP units transfused

50

Figure 8: Forest plot of outcome: Blood product utilization – Number of PLT units transfused

Figure 9: Forest plot of outcome: Blood product utilization – Number of cryoprecipitate units transfused

51

Figure 10: Forest plot of outcome: Blood product utilization – Number of any blood products transfused

Figure 11: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received RBC

transfusion (subgroup analysis: type of indication)

52

Figure 12: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received FFP


Figure 13: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received PLT


Figure 14: Forest plot of outcome: Blood product utilization – Number of patients/admissions that received any

transfusion (RBC, FFP, PLT) (subgroup analysis: type of indication)

53

Figure 15: Forest plot of outcome: Blood product utilization – Number of RBC units transfused (subgroup analysis:

type of indication)

Figure 16: Forest plot of outcome: Morbidity (composite measures)

54

Figure 17: Forest plot of outcome: Morbidity (composite measures) (subgroup: type of indication)

Figure 18: Forest plot of outcome: Morbidity: acute myocardial infarction

55

Figure 19: Forest plot of outcome: Morbidity: acute ischaemic stroke

56

Figure 20: Forest plot of outcome: Morbidity: acute kidney injury

57

Figure 21: Forest plot of outcome: Mortality: hospital mortality

58

Figure 22: Forest plot of outcome: Mortality: not specified

Figure 23: Forest plot of outcome: Mortality: 30-day mortality

59

Figure 24: Forest plot of outcome: Length of hospital stay (days)

60

Quality of evidence

Author, Year Inappropriate

eligibility

criteria

Inappropriate

methods for

exposure and

outcome

variables

Not controlled

for

confounding

Incomplete or

inadequate

follow-up

Other limitations

Frank, 2017,

USA

Unclear

Data source:

online data

acquisition

system (IMPACT

Online)

Demographic

variables

(pre/post) not

reported

No

No indication for

difference in

outcome

measurements

between groups

Yes

No adjustment

for

confounding

variables in the

statistical

analysis

Unclear

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 30

months

Yes

PBM programs are usually

introduced in stages, as we

described. It is therefore

possible that we

underestimated the actual

blood savings because we

had already introduced

some degree of PBM to

the main campus during

what we call the pre-PBM

period.

Potential Hawthorne effect

on practice

Frew, 2016,

UK

No

Data source:

theatre database

(retrospective)

Similar

age/gender

across groups

No

No indication for

difference in

outcome

measurements

between groups

Yes

No data were

collected on

medical co-

morbidities,

intraoperative

blood loss,

body mass

index, fluid

management or

reasons for

readmission

since these are

not coded in

our hospital

databases.

Confounding

and bias is

therefore

possible.

No

Data for all

primary THRs

and TKRs

performed

between

April and

December 2010

were collected

prospectively

and compared

against the

retrospective

controls on an

intention-to-

treat basis.

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 60

months

Yes

For pragmatic reasons,

strict compliance with

the protocol was relaxed

and patients with minor

derangements

in Hb or ferritin levels were

not always treated to avoid

postponement of surgery.


on practice

Gani, 2017,

USA

No

Data source:

prospectively

maintained

database

Similar

demographic

variables

between groups

No

No indication for

difference in

outcome

measurements

between groups

No

Multivariate

regression

analyses with

adjustment for

different

patient

characteristics

were

conducted

No

Increase

compliance

with transfusion

guidelines

Follow-up

period before

PBM: 24

months

Follow-up

period after

Yes


on practice

61

PBM: 24

months

Gross, 2015,

USA

No

Data source:

database of the

Cardiac Surgery

Department of

Eastern Maine

Medical Center

for all cardiac

operations

was used along

with a dedicated

database

developed for

the PBM program

Similar

demographic

variables

between groups

No

No indication for

difference in

outcome

measurements

between groups

Yes

No adjustment

for

confounding

variables in the

statistical

analysis

Unclear

Follow-up

period before

PBM: 9 months

Follow-up

period after

PBM: 66

months

Yes

The full implementation

of this pillar was hindered

by two primary factors: 1)

the often urgent need for

surgery, especially

coronary artery bypass,

that limited the time

available for preoperative

anemia management

and 2) restrictions placed

on the use of

erythropoietic-stimulating

agents (ESAs) in the

context of cardiac

and vascular surgery by

the current US Food and

Drug Administration (FDA)

labeling restriction against

the use of ESAs in cardiac

and vascular surgery.

Non-adherence to

transfusion guidelines may

influence

the results but were not

covered in the present

study.


on practice

Gross, 2016,

USA

Unclear

Data source:

locally developed

extraction

program from

the hospital and

the Cancer Care

electronic

medical record

into a Microsoft

Access database.

Demographic

data between

groups not

reported

No

No indication for

difference in

outcome

measurements

between groups

No

Multivariate

regression

analyses were

conducted

Unclear

Yes

Non-adherence to


influence



study.


on practice

Kansagra,

2017

No

No differences

between baseline

characteristics

between groups

No

No indication for

difference in

outcome

measurements

between groups

No

Fractional

logistic

regression

analyses and

separate

regression

models were

conducted

Unclear

Follow-up

period before

PBM: 24

months

Follow-up

period after

PBM: 24

months

Yes


on practice

62

Kopanidis,

2016,

Australia

No

No differences

between baseline

characteristics

between groups

No

No indication for

difference in

outcome

measurements

between groups

No

Unclear

Follow-up

period before

PBM: 24

months

Follow-up

period after

PBM: 24

months

Yes

Specific biases regarding

surgical decisions during

the operation or the need

for blood transfusion

cannot be excluded.

However, the surgical

technique, surgeon and Hb

transfusion triggers,

amongst the other

variables outlined above,

appeared to be similar in

both groups.

Furthermore, the external

validity or generalizability

of our results to other

hospitals is limited, since

we collected data only

from a single institution.

Non-adherence to


influence



study.


on practice

Leahy, 2014,

Australia

No

Data source: the

WA PBM data

system, a

repository linking

inpatient

admissions from

the patient

administration

system for FHHS

with blood

transfusion

events and

laboratory results

from the WA

laboratory

information

system

Similar age

between groups

No

No indication for

difference in

outcome

measurements

between groups

No

Poisson

regression

analysis was

used to

evaluate the

trend in rate of

blood units per

admission over

time and to

calculate the

rate ratios and

test their

significance.

Unclear

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 36

months

Yes

Non-adherence to


influence



study.


on practice

Leahy, 2017,

Australia (1)

No

Data source:

electronic

databases

Similar age

between groups

No

No indication for

difference in

outcome

measurements

between groups

No

Potential

confounders

such as patient

age and patient

comorbidities

Unclear

Follow-up

period before

PBM: 18

months

Follow-up

period after

Yes

Non-adherence to


influence



study.

63

were included

in the analysis

of in-hospital

mortality,

length of stay,

and serious

bleeding

Poisson

regression

analysis was

used to

evaluate the

rate of blood

units transfused

per admission

between years

and to calculate

the rate ratios

and test their

significance.

PBM: 54

months


on practice

Leahy, 2017,

Australia (2)

No

Data source:

Western

Australia PBM

data system

(validated

hospital data

systems, which

undergo regular

quality audits)

Demographic

variables

between groups:

unclear

No

No indication for

difference in

outcome

measurements

between groups

No

A multivariate

analysis was

performed and

adjusted for

the following

potential

confounders:

hospital,

patient age,

sex, admission

type (elective or

emergency),

Diagnosis-

Related Group

(DRG) category

(medical,

surgical, or

other),

indigenous

status, and

patient

comorbidities

Different

appropriate

regression

analyses and

adjustment

techniques

were used.

Unclear

Follow-up

period before

PBM: 18

months

Follow-up

period after

PBM: 54

months

Yes

Non-adherence to


influence



study.


on practice

64

Loftus, 2016,

USA

No

Data source:

Process measure,

patient/case

characteristics

and

outcomes were

pulled

electronically

from the

electronic

medical record

and two

administrative

databases and

MedSeries4

Similar age and

gender between

groups

No

No indication for

difference in

outcome

measurements

between groups

No

All patient

demographic,

clinical and

surgical factors

considered

were included

in the models

to adjust for

their impact on

outcomes

To adjust the

over-dispersion

issue in logistic

regression,

Pearson

adjustment was

performed.

Unclear

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 12

months

Yes

Non-adherence to


influence



study.


on practice

Mehra, 2015,

Switzerland

No

Similar age and

gender between

groups

No

No indication for

difference in

outcome

measurements

between groups

No

Multivariate

analysis was

performed with

binomial

logistic

regression

predicting

transfusion.

Unclear

Follow-up

period before

PBM: 20months

Follow-up

period after

PBM: 12

months

Yes

Non-adherence to


influence



study.


on practice

Meybohm,

2016,

Germany

No

Data source: All

data on

procedures,

diagnoses

code(s) and

blood product

utilization were

extracted from

the hospital

electronic

medical record

(Agfa ORBIS,

Bonn, Germany)

and anonymized

for further

analysis

using a computer

database (SAS)

Similar age and

gender between

groups

No

No indication for

difference in

outcome

measurements

between groups

No

Regression

analysis,

accounted for

type of surgery

and center

effect

Unclear

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 12-30

months

Yes

Non-adherence to


influence



study.


on practice

Rineau, 2016,

France

No

Data source:

unclear

No

No indication for

difference in

outcome

Yes

No adjustment

techniques

were applied

Unclear

Follow-up

period before

PBM: 4 months

Yes

Non-adherence to


influence

65

Similar age and

gender between

groups

measurements

between groups

Follow-up

period after

PBM: 6 months



study.


on practice

Ternström,

2014, Sweden

No

Data source: all

patient and

transfusion data

were collected

from patient

records and local

data registries

into a database

by a designated

research nurse.

Similar age and

gender between

groups

Yes

The data for the

period before the

start of the

project were

retrieved from

prospective

registries, the

data for the

period after the

start were

collected

prospectively

Yes

No adjustment

techniques

were applied

Unclear

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 12

months

Yes

Non-adherence to


influence



study.


on practice

Thakkar,

2016, USA

No

Data source:

electronic

medical records

at the Johns

Hopkins Bayview

Medical Center

Similar age and

gender between

groups

No

No indication for

difference in

outcome

measurements

between groups

Yes

No adjustment

techniques

were applied

Unclear

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 12

months

Yes


on practice

The presence of a PBM

program that was already

in place for almost 2 years

at Johns Hopkins Hospital,

which is the largest

hospital in our health

system

Theusinger,

2014,

Switzerland

No

Data source: Data

from database of

the Department

of Orthopaedics,

Balgrist Hospital

for all hip, knee,

and spine

operations

performed

between 2008

and 2011 were

matched with

those from the

laboratory and

transfusion

databases of the

Central

Laboratory of

Zurich (Zurich,

Switzerland)

Similar age and

gender between

groups

Yes

2008 was

analysed

retrospectively,

whereas 2009

through to 2011

prospectively

Yes

No adjustment

techniques

were applied

Yes

Peri-operative

data were

available

for all patients,

whereas

complete

postoperative

data were

available for

only 60% of hip

operations,

33% of knee

operations, and

50% of spinal

operations

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 36

months

Yes


on practice

66

Verdecchia,

2016, USA

No

Data source:

centralized

database of

patient

transfusion

records

Demographic

variables: unclear

No

Normalization

technique was

used to account

for differences in

the number of

patients treated

and the number

of transfusions

administered

at each hospital

Yes

No adjustment

techniques

were applied

Unclear

Follow-up

period before

PBM: 12

months

Follow-up

period after

PBM: 96

months

Yes

Non-adherence to


influence



study.


on practice

Xydas, 2012,

USA

Yes

Data source:

unclear

No

No indication for

difference in

outcome

measurements

between groups

No

multivariate

logistic

regression

models were

used

Unclear

Follow-up

period before

PBM: 6 months

Follow-up

period after

PBM: 6 months

Yes

Non-adherence to


influence



study.


on practice

Yaffee, 2014,

USA

No

Data source:

unclear

Baseline patient

characteristics

were similar

between groups

No

No indication for

difference in

outcome

measurements

between groups

Yes

No adjustment

techniques

were applied

Unclear

Follow-up

period before

PBM: 24

months

Follow-up

period after

PBM: 24

months

Yes

Non-adherence to


influence



study.


on practice

Certainty of the body of evidence: see GRADE evidence tables

Conclusion See Evidence-to-Decision template

Reference(s) See list ‘overview 20 included studies’

Evidence used for Guideline

Project PBM consensus meeting

Reviewer(s) Hans Van Remoortel

Documents

Evidence summary to support PICO question 15 on PBM ... › wp-content › uploads › 2019 › 04 › 20180401_evid… · Xydas, 2012, USA 6 Yaffee, 2014, USA 24 PBM component Intervention(s)