Effects of Donor-recipient Sex-matched Versus Sex-mismatched Red Blood Cell Transfusion on Outcomes in Critically Ill Adult Patients
Phase: PHASE4
Conditions: Red Blood Cell Transfusions, Sex Differences, Health Services, Critically Ill Intensive Care Unit Patients, Hematology, Cardiovascular
Sponsor: Michelle Zeller

 
 
 
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Effects of donor-recipient sex-matched versus 
sex-mismatched red blood cell transfusion on 
outcomes in critically ill adult patients 
 
Protocol for a randomized trial 
 
ClinicalTrials.gov Identifier: NCT06840756 
 
Principal Investigator: Dr. Michelle Zeller 
 
Version 1.4  
 
October 15, 2025 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
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1. 
BACKGROUND ................................................................................................................... 5 
1.1 WHAT IS THE PROBLEM? ............................................................................................... 5 
1.1.1 Background & Conflicting Data .................................................................................... 5 
1.1.2 Biological Hypotheses: ................................................................................................ 6 
1.1.2a Why male RBC transfusions might be harmful to female recipients: ......................... 6 
1.1.2b Why female RBC transfusions might be harmful to male recipients: ......................... 7 
1.2 WHAT THIS STUDY WILL ADD TO THE CURRENT KNOWLEDGE ................................ 7 
2. 
OVERALL GOAL ................................................................................................................. 8 
3. 
RESEARCH APPROACH AND METHODS ......................................................................... 8 
3.1 STUDY DESIGN ................................................................................................................ 8 
3.2 SETTING ........................................................................................................................... 8 
3.3 STUDY INTERVENTION AND DURATION ....................................................................... 9 
3.4 STUDY OUTCOMES ......................................................................................................... 9 
3.4.1 Primary outcome ......................................................................................................... 9 
3.4.2 Secondary outcomes .................................................................................................. 9 
3.4.3 STUDY DURATION .................................................................................................. 10 
3.4.4 RANDOMIZATION .................................................................................................... 10 
3.4.5 STUDY BLINDING .................................................................................................... 10 
4. SELECTION AND ENROLLMENT OF PARTICIPANTS........................................................ 11 
4.1 INCLUSION CRITERIA ................................................................................................... 11 
4.2 EXCLUSION CRITERIA .................................................................................................. 11 
4.3 RECRUITMENT SCHEDULE .......................................................................................... 12 
4.4 STUDY ENROLLMENT PROCEDURES ......................................................................... 12 
4.5 REQUEST FOR WAIVED CONSENT ............................................................................. 12 
4.6 THREATS TO RECRUITMENT ....................................................................................... 13 
4.7 LIKELY RATE OF LOSSES TO FOLLOW-UP ................................................................. 13 
5. DATA COLLECTION AND QUALITY CONTROL .................................................................. 13 
5.1 DATA COLLECTION ....................................................................................................... 13 
5.1.1 Data Collection from EMR: ........................................................................................ 14 
5.1.2 Data Collection from the LIS:..................................................................................... 14 
5.1.3 Data Linking and Confidentiality: ............................................................................... 14 
5.1.4 Data Collection from CBS: ........................................................................................ 14 

 
 
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5.1.5 Data Collection from Ontario Registered Person Database/ICES: ............................. 14 
5.2 TREATMENT DEVIATIONS ............................................................................................ 14 
5.2.2. Patient Withdrawal from the Study ........................................................................... 15 
6. 
STATISTICAL METHODS ................................................................................................. 15 
6.1 ESTIMATED SAMPLE SIZE ............................................................................................ 15 
6.2 OUTCOME MEASUREMENTS ....................................................................................... 16 
6.3 DATA ANALYSES ........................................................................................................... 16 
6.3.1 Principal analysis of primary outcome measure......................................................... 16 
6.3.2 Secondary Analyses ................................................................................................. 17 
6.3.3 Subgroup Analyses ................................................................................................... 17 
6.4 COST-EFFECTIVENESS ANALYSES ............................................................................ 17 
7. 
STUDY MANAGEMENT .................................................................................................... 18 
7.1 COMMITTEES................................................................................................................. 19 
7.2 ETHICS, CONSENT AND PRIVACY ............................................................................... 19 
7.3 KNOWLEDGE TRANSLATION PLAN ............................................................................. 20 
8. 
STRENGTHS AND POTENTIAL CHALLENGES ............................................................... 20 
9. 
ANTICIPATED RESULTS AND OUTPUTS ........................................................................ 20 
9.1 ANTICIPATED CONTRIBUTIONS ................................................................................... 21 
References: .............................................................................................................................. 22 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
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1. BACKGROUND  
 
1.1 WHAT IS THE PROBLEM?  
Blood transfusion is one of the most common procedures performed during 
hospitalization.1 Approximately 85 million red blood cell (RBC) units are transfused 
globally each year.2,3 RBC units are matched for blood groups, but matching for other 
donor characteristics such as sex is not considered.  By the current standard of care, 
male or female patients can receive RBCs from male or female donors. However, there 
is concern with regards to donor sex and transfusion risk.4–7 Plasma from female donors 
is associated with an increased risk of transfusion related acute lung injury and 
hypotension;8,9 sex-mismatched heart transplantation is associated with increased 
transplant-associated mortality;10–12 and stem cell transplants from female donors are 
associated with worse outcomes.10,13–16   
 
Anemia is common during critical illness;17 20 – 40% of critically ill patients require a mean 
of 2 – 5 RBC units during admission to the intensive care unit (ICU). Given blood bank 
inventories, sex-mismatched transfusions are inevitable once a patient receives more 
than 6 RBC units (>97% chance) (Figure 1). The population of transfused adult ICU 
patients is already at high risk of death, with a 90-day all-cause mortality of 35-37%.18 
Locally, based on historic data, we have found ~30% in-hospital mortality for this 
population (Table 1a), and based on our CIHR-funded multisite pilot data, 29% 30-day 
and 34% 90-day in-hospital mortality (Table 1b). Thus, optimizing supportive care 
strategies is needed to improve outcomes of this highly vulnerable patient group and a 
strategy as simple as matching RBC transfusions for donor sex may have significant 
impact.   
 
1.1.1 Background & Conflicting Data 
To support this study, we completed an exploratory analysis,19 meta-analysis20 and a 
CIHR-funded multisite feasibility pilot randomized controlled trial (RCT) (see Appendix 
B).21 The exploratory analysis included transfusion data linked to donor sex spanning a 
6-year period. We analyzed 25,219 transfusion recipients and found significant 
association between male to female RBC transfusions and death [hazard ratio (HR) 1.31, 
95% confidence interval (CI) 1.02-1.69].19 A trend towards higher mortality was also noted 
with female to male RBCs (HR 1.13: 95% CI 0.92-1.39), and with sex-mismatched versus 
sex-matched RBCs overall (HR 1.23: 95% CI 1.04-1.45). These findings suggest that sex-
mismatched RBC transfusions may contribute to a higher mortality among ICU patients.  
 
Several observational studies have explored the association between donor sex on 
recipient mortality after RBC transfusions4–6,19,22–27 We performed a systematic review 
and meta-analysis that showed an increased risk of death with sex-mismatched RBC 
transfusions compared with sex-matched transfusions (pooled HR 1.13: 95% CI 1.02-
1.24) (Figure 2).20 Data were derived from five retrospective observational studies (n= 
86,737). The certainty of evidence was low due to confounding, selection and reporting 

 
 
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bias of the primary studies; no country has implemented a sex-matched RBC transfusion 
strategy as a result of these observational data.   
 
Since the meta-analysis was published, additional observational studies have emerged. 
A large multi-database study explored associations of donor sex, prior pregnancy, and 
sex-mismatched transfusions on mortality.28 The authors concluded that transfusion of 
donor-recipient sex-mismatched RBCs was not associated with increased risk of death; 
however, subsequent analysis showed increased risk of death with sex-mismatched 
transfusions.28 Another study investigated associations between donor-recipient sex and 
post-transfusion mortality and morbidity in transfused critically ill patients from either 
male-only donors or from female-only donors.29 Transfusion of female RBCs to male 
patients was associated with an increase in ICU mortality compared with transfusion of 
female RBCs to female patients (OR 2.43; 95% CI, 1.02-5.77).   
 
Finally, a large RCT examined impact of donor sex on mortality after transfusion (iTADS). 
30–32 The iTADS trial addressed whether transfusion of male-only RBCs (male donor to 
male or female recipients) was associated with improved survival compared with female-
only RBCs (female donor to male or female recipients) in all hospitalized patients. Results 
showed no important difference in survival with either transfusion strategy. Subgroup 
analysis suggested a lower risk of death among male patients assigned to female-only 
RBCs than among those assigned to the male RBC group (hazard ratio [HR], 0.90; 95% 
CI, 0.81 to 0.99). In addition, among recipients from donors 20 to 30 years of age, there 
appeared to be a higher risk of death among patients assigned to the female donor group 
than those in the male donor group (HR 2.93; 95% CI, 1.30 - 6.64), and a post hoc 
analysis showed fewer deaths among recipients of sex-mismatched RBC transfusions 
compared with sex-matched (unadjusted HR, 0.89; 95% CI, 0.82 - 0.96). Further 
discussion on this trial is in Section 1.2 below.  
 
1.1.2 Biological Hypotheses:  
Important biochemical and biophysical differences between male and female RBCs 
include differences in hematocrit, cell volume, hemolytic propensity, deformability and 
mean cell hemoglobin content.33–36 In addition, donor sex, particularly during the 
reproductive years (16 – 35), result in significant differences in the age distribution and 
physical characteristics of the young and old RBCs in circulation in male and female blood 
donors.37–39 The impact of blood donor demographics, including sex, on patient outcomes 
is an emerging field of study with important potential for broad impact. To support our 
working hypotheses that sex-mismatched RBC transfusions may be harmful:   
1.1.2a Why male RBC transfusions might be harmful to female recipients:  
Transfusion of biologically older RBCs from male donors with a high hemolytic propensity, 
lower deformability and higher densities may critically affect oxygen delivery, coagulation 
and vasculopathy when transfused to female recipients.40 The mechanisms underlying 
the reduced sensitivity of female RBCs to storage-induced stress can be partially ascribed 
to the increased proportion of more robust, biologically “young” subpopulations of RBCs 
in female donors, and the differential effects of testosterone, progesterone, and estrogen 

 
 
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on the hemolytic propensity of RBCs.41 Larger amounts of hemoglobin, particularly free 
hemoglobin, found in male RBC products may overwhelm the haptoglobin scavenging 
capacity of the reticuloendothelial system in female recipients.42,43 This overwhelmed 
pathway for scavenging of the toxic free hemoglobin / iron in circulation results in a 
reduced nitric oxide (NO) bioavailability, which can in turn lead to endothelial dysfunction, 
platelet aggregation, and oxidative injury.42,44–46 RBC units from male donors have been 
shown to have elevated levels of immature RBCs47 which have been shown to be 
immunosuppressive48–52 and potentially enhance the erythrophagocytosis of mature 
RBCs and further release of hemoglobin into circulation.53  In addition, hemodynamic 
changes in blood flow due to the transfusion of male RBC (higher ratio of older, denser, 
less deformable cells) could affect the margination of red cells in circulation of a female 
recipient, thus affecting platelet and white blood cell endothelial interactions.54 It has been 
recently shown55 that single unit sex-mismatched compared to sex-matched RBC 
transfusion in ICU patients resulted in elevated iron levels, enhanced endothelial 
activation and increased mortality which supports this hypothesis.  
1.1.2b Why female RBC transfusions might be harmful to male recipients: 
Characteristics of the RBC sub-populations in male and female donors may have different 
immunological characteristics. Sub-populations of erythroid precursors in young females 
and pregnant women are enriched in immunosuppressive cells which have been shown 
to impair the defense against pathogens in neonates48–50,56 and cancer patients.51,52 In a 
rat model of transfusion, male animals who received female RBCs had elevated markers 
of vascular injury and more entrapment of RBCs in the lung, liver and spleen.57 
Observational data suggest that male recipients exposed to ever-pregnant female donors 
were at higher risk of mortality compared to female recipients.6  Further research is 
required to determine whether male recipients of female RBCs are at increased risk of 
morbidity and mortality from immunosuppressive side effects or from differences in the 
deformability/density/hemoglobin content of female RBCs.   
 
 
1.2 WHAT THIS STUDY WILL ADD TO THE CURRENT KNOWLEDGE  
Findings from observational studies are inconsistent. The iTADS RCT results are 
intriguing and require confirmation especially in ICU patients who have a much higher 
baseline risk of death than hospitalized patients, and for whom a procedural blood bank 
change could have a significant impact. iTADS did not have the power to detect potential 
differences in patients admitted to the ICU. The subgroup analyses of iTADS were found 
to have low credibility based on an external independent evaluation using the Instrument 
to assess the Credibility of Effect Modification Analyses (ICEMAN); this external analysis 
was performed by 2 independent methodology experts (unpublished).58 Our proposed 
trial is different from iTADS because we plan to examine ICU patients only with increased 
RBC exposures and higher event rate and we specifically address matched vs. 
mismatched (not male vs. female). We have worked closely with the iTADS study team 
who, given our different aims and study design, are supportive of the Sex Matters design 
and have contributed significantly to study design. Changing policy to a sex-matched 

 
 
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transfusion strategy would be a major change for hospitals and blood suppliers and 
requires strong evidence from RCTs to support such a change.  
 
Results of this RCT will inform policy on blood transfusion practices.  If mortality is shown 
to be reduced with sex-matched transfusions, then a policy change can be implemented 
at the blood bank level.  This will require re-evaluation of how blood bank inventories are 
managed and assigned. Results will be shared with the Canadian blood suppliers 
(Canadian Blood services [CBS] and Héma Québec) and disseminated with the help of 
the Canadian Transfusion Trials Group (CTTG) Patient Engagement Working Group and 
Knowledge Translation Working Group.   
  
2. OVERALL GOAL   
The goal of this study is to address the following question: In adults admitted to the ICU 
who require RBC transfusions, do donor-recipient sex-matched RBC transfusions result 
in improved 30-day mortality compared with sex-mismatched RBC transfusions? The 
outcomes of this prospective multi-center blinded pragmatic randomized trial are further 
described in Section 3.4 below. 
 
3. RESEARCH APPROACH AND METHODS   
3.1 STUDY DESIGN  
This study is designed as a superiority RCT with pragmatic features. We have selected 
ICU patients given the high mortality rate to maximize the chance of detecting a 
possible effect of sex-matching – moreover, a relatively high proportion (20-40%) of 
these patients will require RBC transfusions. Mortality in transfused ICU patients is 
~33.1% based on historic data (Table 1a); and 30-day in-hospital mortality was 29% in 
our pilot RCT (Table 1b).   
 
The pragmatic features of this RCT include enrolment of all ICU patients who require 
transfusion to maintain generalizability; waived consent (addressed in Section 4.5 and 
Table 3); electronic data collection based on existing electronic medical records 
(EMR)/sources to increase efficiency and decrease cost (Section 5.1.1); use of an 
objective outcome measure to minimize bias and maximize data completeness. Our 
research group (Michael G. DeGroote Centre for Transfusion Research, MCTR) has 
experience implementing large scale pragmatic RCTs, including the CHIR-funded 
INFORM trial (n=31,000),60 which used a similar pragmatic design. We have also 
collaborated with the iTADS team to leverage their infrastructure and experience.  
  
3.2 SETTING  
Eight sites in Ontario (see Table 5) will participate in this study. Management of the trial 
will be coordinated by MCTR, an experienced group in transfusion-related research with 

 
 
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an extensive track record in coordinating multi-centre RCTs. Infrastructure for trial 
coordination and data management are well established within this group. The study 
data will be captured electronically and transferred via a dedicated FTP server (or 
equivalent) located in the Computer Services Unit (CSU) at McMaster University, then 
stored on MCTR’s secure server also located in the CSU at McMaster University.  
  
3.3 STUDY INTERVENTION AND DURATION 
The interventions are donor-recipient sex-matched RBC transfusions (male donor to male 
recipient; female donor to female recipient) or sex-mismatched RBC transfusions (male 
donor to female recipient; female donor to male recipient). An ICU patient will be enrolled 
the first time a request is made to the blood bank for an RBC unit for them. All patients 
will receive ABO and Rh compatible RBCs as per routine blood bank practices, in addition 
to selecting for donor sex. All transfused RBCs will be obtained from Canadian Blood 
Services (CBS) and will be the standard RBC products provided in Canada.  
 
RBC units will be labelled with a unique sticker representing donor sex.  Only CBS will 
know the colour assignment for units. Inventory flow and study logistics were developed 
with input from iTADS investigators and successfully implemented in our pilot study (see 
Figure 4). The blood bank staff will be blinded to the treatment allocation, and the use of 
random block sizes will ensure that they cannot predict treatment allocation. The clinical 
staff, patient/family and research staff will be blinded to the intervention. Validation of 
sticker-labeling will be conducted at each site according to the protocol successfully 
implemented in the pilot (see Figure 5).  
 
Patients will receive RBCs according to their treatment allocation throughout the hospital 
stay until discharge from hospital or death (see Section 3.4.4 below). 
  
3.4 STUDY OUTCOMES  
3.4.1 Primary outcome  
The primary outcome is 30-day mortality, defined as death within 30 days of 
randomization. A review of mortality in clinical trials of critically ill patients showed that 
half of all deaths occurred in the first two weeks following randomization and three-
quarters by 28 to 30 days; with few additional deaths accruing over 6 months.61 Use of 
28-30 day mortality is consistent with other critical care trials.62–66  
  
3.4.2 Secondary outcomes  
Secondary outcomes for this study will include 30-day in-hospital mortality, 90-day 
mortality, time to 30-day in-hospital mortality, 90-day survival analysis, 90-day in-ICU 
mortality, time to 90-day in-ICU mortality, hemoglobin increment (per RBC transfusion),  
need for CRRT/HD, ICU/hospital lengths of stay, number/type/volume/dose of 
transfused product, transfusion reactions, and cost effectiveness measured using the 

 
 
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incremental cost per life year saved (ICER) between sex-matched and mismatched 
transfusions. Secondary outcomes were selected based on the quality and accuracy of 
data available in the source registries and to cover a clinically representative range of 
adverse short and long-term events after transfusion (renal, cardiovascular, oncology, 
mortality, infections).  
  
3.4.3 STUDY DURATION  
With eight sites and sequential activations, it will take approximately 40 months 
following enrollment of the first randomized patient to achieve the required sample size 
of 11,082.  
 
For each patient, the treatment period begins when the patient is randomized just 
before receiving the first unit of RBCs in the ICU. Patients will receive their assigned 
RBC group throughout their hospital admission, even if moved to a different ward, until 
hospital discharge or death. Moreover, patients will continue to receive the assigned 
type of unit until 90 days post-randomization, even if they receive units in different 
admissions at the same hospital. Note: patients re-admitted to a different hospital would 
not follow this procedure. Instead, they would be considered “lost to follow-up”. 
  
3.4.4 RANDOMIZATION  
Patients will be allocated to a treatment group in a balanced (1:1) fashion using a 
secure, concealed, computer-generated, web-based (created on REDCap) 
randomization sequence.60 Treatment assignment will be established by way of an 
electronic marker placed in each patient’s blood bank electronic record within the 
Laboratory Information System (LIS) (further details in Figure 3). Randomization will be 
performed by blood bank technical staff immediately prior to RBC issue. Blocked 
randomization will be used with random variable block sizes (2, 4 or 6) stratified by sites 
to ensure allocation concealment.   
 
3.4.5 STUDY BLINDING  
RBC units will be labelled with a unique sticker representing donor sex. Only CBS will 
know the colour assignment for units. Inventory flow and study logistics were developed 
with input from iTADS investigators and successfully implemented in our pilot study (see 
Figure 4). The blood bank staff will be blinded to the treatment allocation, and the use 
of random block sizes will ensure that they cannot predict treatment allocation. The 
clinical staff, patient/family and research staff will be blinded to the intervention.  
 
The web-based randomization system with appropriate stratification will ensure that the 
randomization sequence remains concealed to blood bank technical staff and study 
investigators. Since RBC units are not labeled with donor sex and the stickers used to 
label the units are agnostic, caregivers and patients will not know whether they are 
receiving RBCs from a male or female donor. Health care professionals in the ICU will 

 
 
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be made aware of the study but will not know what sticker indicates which sex. Only 
hospitals with sufficiently large inventories will participate to decrease the chance of 
unblinding at the blood bank level given that there are more male blood donors (56%) 
(https://professionaleducation.blood.ca/en/transfusion/publications/surveillance-report).   
Other methods for protecting against sources of bias include: (a) computerized 
generation and concealment of the treatment allocation schedule by site; (b) use of an 
objective primary outcome measure that is not subject to ascertainment bias; and (c) 
anticipated near complete follow-up using hospital records and external databases for 
death data.  
4. SELECTION AND ENROLLMENT OF PARTICIPANTS  
4.1 INCLUSION CRITERIA  
Adults (age ≥18), admission to a participating ICU, and requiring RBC transfusion.   
  
4.2 EXCLUSION CRITERIA  
Requirement for a specialized RBC product or unit not readily available in inventory 
(e.g., rare blood type, washed RBCs, complex RBC antibodies, etc.), massively 
bleeding patient (i.e., ≥4 units of blood ordered at one time, or Massive Hemorrhage 
Protocol initiated, or an urgent blood request made), sex unknown or sex other than 
male or female (i.e. intersex), and do not have a valid Ontario Health Insurance Plan 
(OHIP) health card number.    
 
The eligibility criteria are meant to be pragmatic with few exclusions. In selecting only 
medical-surgical ICUs, our population is enriched for patients who have not had a 
recent transfusion prior to their ICU admission. Patients who were previously transfused 
prior to their ICU admission during the same hospital admission will be included; historic 
data show that 27% of those transfused in the ICU received a transfusion within 90 days 
prior to the ICU admission. In our pilot study, pre-randomization transfusion rate was 
~42% with ~one-third of those receiving their first RBC transfusion pre-randomization in 
the ICU. We chose not to exclude such patients to facilitate more pragmatic enrolment. 
Also, we believe the percentage of pre-transfused patient will decrease over time; the 
pilot trial was conducted during pandemic-related critical staffing shortages in the blood 
bank; improved staffing levels and familiarity with the trial should increase the number of 
patients randomized on first RBC unit transfused in the ICU. If a patient is allo-
immunized and is receiving a specially requested phenotype-matched product, it might 
be difficult to provide sex-matched units, depending on the specific frequency of the 
antigen. This represents approximately 2% of patients (these patients are not excluded 
unless antigen is rare, as above). We have focused on adult patients, as pediatric ICU 
patients have a much lower mortality rate compared to adults (though this is an 
important population for future study). We excluded patients where blood was urgently 
needed to ensure no delay in provision of blood due to randomization. Patients not 
identified as male or female cannot be stratified during randomization to receive either 
intervention and can therefore not be included in the study.  

 
 
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 4.3 RECRUITMENT SCHEDULE  
During the pilot RCT, sites were activated in a step-wise manner between January 7th 
and March 4th, 2022. During the period where all sites were enrolling, the blood bank 
technologists randomized ~120 patients/month. With eight sites and sequential 
activations, it will take approximately 40 months to achieve the required sample size. 
  
4.4 STUDY ENROLLMENT PROCEDURES  
We request waived consent authorization from the Research Ethics Board. Approval for 
the pilot study was obtained through Clinical Trials Ontario (CTO) with the Hamilton 
Integrated REB as the REB of record (see Appendix A). To meet the Tri-Council 
requirement to inform patients we will provide study information to all ICU patients at the 
time of discharge from the ICU/hospital. The information will be embedded in the patient 
electronic discharge summary or provided in paper form (as per site capabilities) and 
will state that they may have been included in this study and provides opt-out 
instructions. Similar brochures were used in the Sex Matters pilot, INFORM, and 
iTADS.   
  
4.5 REQUEST FOR WAIVED CONSENT  
We believe that it is appropriate to obtain waived consent for a number of reasons. First, 
for feasibility, transfusion requests are received on a 24/7 basis and our study 
population is comprised of ICU patients making it extremely difficult logistically to obtain 
consent in a timely manner from all patients in advance of their transfusion. Second, 
RBC units are currently released in random order without consideration of donor sex (ie. 
it is impossible to know what product is ordered). In our study, at time of study inclusion, 
the first transfusion will be administered at random (determined by our computerized 
system rather than “picking the next bag”) and this is no different than routine care. The 
only modification to the standard of care is that all subsequent transfusion (if any) will be 
of the same type as the first transfusion. We therefore follow the Tri Council 
recommendations that consent can be waive given that:  
1. “the research involves no more than minimal risk to the participants”;  
2. “the alteration to consent requirements is unlikely to adversely affect the welfare 
of participants”;  
3. “it is impossible or impracticable to carry out the research and to address the 
research question properly, given the research design, if the prior consent of 
participants is required”;  
4. “in the case of a proposed alteration, the precise nature and extent of any 
proposed alteration is defined”; and  
5. “the plan to provide a debriefing (if any) which may also offer participants the 
possibility of refusing consent and/or withdrawing data and/or human biological 
materials, shall be in accordance with Article 3.7B”.  
Patients at the participating centres routinely receive discharge papers at the time of 
discharge from the ICU informing them that they received a transfusion. To respect item 

 
 
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5, additional information will be provided about the nature of the study, stating that they 
may have been included in the study and providing opt-out instructions, while 
minimizing the potential for fear and apprehension for patients.  
 
Furthermore, it should be noted that deferred consent would not be feasible as this 
would potentially impact our ability to accurately obtain our primary outcome data; it 
may be more difficult to obtain consent from substitute decision makers in the event of a 
patient death during their ICU stay thus adding to potential bias. 
  
4.6 THREATS TO RECRUITMENT  
Recruitment compliance (number of recruitments that are compliant/total number of 
patient randomized ×100%) from the pilot study was 97% with protocol adherence 
(number of patients who receive all RBC transfusions as group assigned/total number of 
randomized and transfused patients x 100%) of 88%. At sites where compliance and/or 
adherence were lower than others, root cause analysis, corrective action and preventative 
action plans were developed during monthly Technical Resource Committee meetings 
(Section 7.1). In addition, based on our experience with one of the largest pragmatic 
RCTs ever performed in Transfusion Medicine (31,497 patients randomized)60 and our 
pilot experience, we expect excellent compliance for providing blood according to the 
patients’ group assignment. Blood bank staff at each centre will be responsible for issuing 
the blood and will receive formal training in randomization procedures. A marker will be 
placed in the patient’s electronic blood bank record within the LIS indicating treatment 
assignment. These electronic markers appear when the file is accessed and are checked 
prior to transfusion. The study budget includes resources to oversee blood bank 
adherence to study procedures. The Data Management Sub-Committee (Section 7.1) 
will monitor protocol violations monthly and implement corrective action if needed.  
 
4.7 LIKELY RATE OF LOSSES TO FOLLOW-UP  
We anticipate near complete follow-up since all patients will have in-hospital medical 
records available and patients’ discharge status is recorded with 100% accuracy. 
Mortality data from Ontario Registered Persons Database can identify the true number 
of deaths within 1%.70  
5. DATA COLLECTION AND QUALITY CONTROL  
5.1 DATA COLLECTION 
Data will be captured electronically at pre-specified intervals from the EMR and LIS.  All 
participating centres have proven track records for collecting the data required for this 
study using an electronic approach. Figure 6 illustrates the strategy for confidential data 
collection. Queries will be run to identify issues; sites will be asked to respond in a timely 
manner. The same level of scrutiny and follow-up will be applied to this electronic 
approach as would be to case report forms received during any RCT.   

 
 
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5.1.1 Data Collection from EMR:  
Each patient’s demographic and clinical information (primary and secondary diagnoses 
and interventions) is coded following discharge by trained medical chart abstractors and 
stored in the hospital’s EMR. Data variables will be collected as per pilot and influenced 
by iTADS. Data variables available to extract are summarized in Table 6.  
5.1.2 Data Collection from the LIS:  
RBC product and patient transfusion data will be extracted monthly from the LIS (Table 
6). All sites have the capability of extracting the relevant LIS data. The LIS is the gold 
standard source for accurate and complete transfusion data, validated to meet regulatory 
standards.69   
5.1.3 Data Linking and Confidentiality:  
Data from all sources will contain a specific patient identifier needed for linking. To protect 
patient identity and maintain confidentiality and compliance with privacy requirements, 
this unique patient identifier will be either protected, recoded, or given a new study ID as 
per individual site REB requirements. Once the identifiers have been managed at the site 
level, the data files will be exported to the Coordinating Centre. At the Coordinating 
Centre, the individual site files from medical records and the LIS will be linked using re-
coded patient identifiers; this approach for maintaining privacy and confidentiality is 
compliant with current Canadian legislation.  
5.1.4 Data Collection from CBS:  
CBS will provide a list with each inventory shipment of RBCs containing units coded 
according to colour which correspond to donor sex, though the blood bank staff will be 
blinded to this. We will also be collecting de-identified data on donor age, hemoglobin 
level at time of donation, volume of unit and manufacturing process from CBS. There are 
currently two different manufacturing process for RBCs through CBS and the type of 
process has been shown to impact cell quality.73,74  
5.1.5 Data Collection from Ontario Registered Person Database/ICES:  
30- and 90-day mortality data will be obtained following methodology from iTADS.32  
  
5.2 TREATMENT DEVIATIONS  
All protocol deviations will be documented and logged. All precautions will be taken to 
exclude potential deviations before randomization. The Data Management Sub-
Committee (Section 7.1) will monitor protocol violations monthly and implement 
corrective action if needed.  
 
In the event a patient is randomized to either treatment arm and experiences a massive 
bleeding event during the course of their hospitalization, the wellbeing and safety of the 
patient comes before compliance. If a randomized patient subsequently requires blood 
in quantities such that blood per allocation cannot be provided by the blood bank 
without jeopardizing patient safety, this would constitute a protocol deviation with the 

 
 
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reason recorded. This situation occurred 3 times during the pilot. Such cases are 
included in the intention to treat analysis but excluded from the per protocol analysis 
and should be balanced across treatment arms.  
 
5.2.1 Validation Errors  
 
For patients who are randomized, a list of all units transfused with a corresponding 
unique patient and allocation group identifier will be kept at the blood bank and sent 
back to CBS monthly to confirm that each patient received the appropriate unit.  On a 
regular basis, a member of the research team will conduct spot checks of the inventory 
list with colour allocation on units in inventory to ensure they are correctly labeled. If an 
incorrectly labeled unit is identified, the study team personnel will approach the blood 
bank staff to review, change the sticker and identify root cause of error. If more than 1% 
protocol deviation occurs, additional training, audits and study monitoring will occur at 
the sites to ensure appropriate randomization and study treatment allocation. While 
patient withdrawal is unlikely, it is possible that patients randomized may not require the 
RBCs by the time they reach the patient for transfusion due to a change in treatment or 
diagnosis. These patients will remain included in the study and analyzed using the 
intention-to-treat strategy.  
5.2.2. Patient Withdrawal from the Study  
For patients who indicate their desire to opt-out of the study after receiving notification 
of their enrollment, a note will be made in the blood bank management system. For 
these patients, future hospitalizations requiring RBC transfusions will have standard 
blood bank procedures applied - i.e. discontinuation with their enrolled study arm, and 
delivery of the most suitable RBC unit, selected at the discretion of the medical team 
and blood bank technician. The study team from sites will obtain REB approval to 
record the name, date of birth and contact information of the patient so that the research 
team can determine if the patient was included in the study. If the patient was included 
in the study, their data will be removed at sites and/or the coordinator center and a flag 
in the LIS will be added to indicate that the patient is not eligible for the study in case of 
future randomization.  
 
6. STATISTICAL METHODS  
  
6.1 ESTIMATED SAMPLE SIZE  
Thirty-day in-hospital mortality in the pilot trial was 29% and evidence from the literature 
suggests that 30-day all-cause mortality in transfused patients admitted to the ICU is 
~33%. In consultation with knowledge users and decision makers from CBS, 
intensivists, transfusion medicine experts, transfusion providers, methodologists, and 
patients, we have decided to target an absolute decrease in mortality of 3% (with 90% 
power based on a two-sided test with a type 1 error rate of 0.05) (Table 4), which will 

 
 
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require 5,037 patients/arm. Other transfusion studies in the ICU have used a 4.2%67 
and 5%18 reduction in in-hospital mortality, with baseline rates of 24-33%. We target a 
sample size 10% higher (n=5541/arm) to accommodate potential non-compliance to 
allocated intervention and the possibility of variable mortality rates in some hospital 
sites, as seen in the pilot trial. The total sample size is therefore 11,082.  
 
6.2 OUTCOME MEASUREMENTS  
Outcome data will be captured electronically from the hospital electronic health record 
(Discharge Abstract Data, laboratory and transfusion data). Linkage with Ontario 
Registered Person Database/ICES will yield mortality status (where unknown from 
hospital EMR) as was done in iTADs.30 Figure 6 shows data sources and linkages.  
 
6.3 DATA ANALYSES  
A statistical analysis plan will be written and posted publicly before the study database 
is locked. 
6.3.1 Principal analysis of primary outcome measure  
For the primary binary outcome (30-day mortality) and binary secondary outcomes (30-
day in-hospital mortality, 90-day mortality, 90-day survival analysis, 90-day in-ICU 
mortality, time to 90-day in-ICU mortality) logistic regression models will be fitted, 
controlling for centre and sex of recipient. Based on this, odds ratios (OR) will be 
computed for the treatment effect from the fitted models and accompanied by a 95% 
confidence interval (CI). Wald-based tests of significance will be carried out. Sensitivity 
analyses will adjust for factors such as whether they are surgical patients, and whether 
they had a malignancy or sepsis.   
 
 For hemoglobin increment at each RBC transfusion, a linear mixed model will be used 
with a patient level random effect to accommodate the association between increments 
in successive transfusions in the same recipient, centre and sex main effects, and an 
indicator of the treatment arm. The length of stay in the ICU will be analysed as ICU 
length of stay for all patients, hospital length of stay for all patients, ICU-free days (to 
day 30), and hospital free days (to day 90). The analysis will again be based on a linear 
regression model with main effects for centre, sex and treatment arm and a robust 
standard error will be computed.  
 
Total number of transfused RBC units per patient will be compared between the two 
arms based on a working Poisson regression model, stratifying on centre and sex, with 
a robust variance used for protection against extra-Poisson variation. Transfusion 
reaction numbers will be compared using a log-linear model controlling for centre and 
sex of donor and using a robust variance estimate.    
 
The time to in-hospital death within 30 days of randomization will be modeled using a 
cause-specific Cox regression model treating discharge from hospital as a competing 

 
 
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risk.  Models will be stratified by centre and sex of patient. Follow-up will be censored at 
30 days. Tests of the adequacy of the proportional hazard assumption will be carried 
out based on Schoenfeld residuals as associated test statistics. Hazard ratios will be 
computed along with 95% CI and Wald-based tests of significance will be based on the 
regression coefficient. Sensitivity analyses will assess intervention effects adjusting for 
surgical status, and whether patients had malignancy or sepsis.  
6.3.2 Secondary Analyses  
In exploratory secondary analyses a time-dependent covariate will be derived which 
records the cumulative proportion of units received that were from a sex-mismatched 
donor as well as cumulative binary indicator of receiving sex mismatched RBCs. In a 
Cox regression model for in-hospital death a further stratification factor will be added as 
the cumulative number of RBC transfusions received. In this model then the effect of the 
cumulative number of mismatched RBC units will be assessed in individuals who have 
received the same total number of RBC units of any kind. This analysis will be done in 
two ways: the first is to count only the number of units received after randomization and 
counting only sex-mismatched RBC units after randomization. The second exploratory 
analysis will define the cumulative number of units received as including those that were 
received prior to randomization – the time-dependent cumulative number of sex-
mismatched units will likewise count those that were mismatched and transfused before 
randomization. Underlying diagnosis will be considered in the analysis. 
6.3.3 Subgroup Analyses  
Subgroup analyses will be carried out by broadening the primary regression models to 
include interactions between the treatment arm and the covariate identifying subgroups 
of interest. A centre by treatment interaction will be fitted to test for homogeneity of the 
effect of sex-matched blood across sites. With eight centres this will result in an 
7 degree of freedom test based on the logistic regression model for the primary 
outcome. Additional subgroups to be explored include sex of recipient in order to assess 
whether matching the sex of the donor matters more or less to female and male 
recipients, race/ethnicity when available, donor/recipient age, medical/surgical patients. 
Subgroup analyses will be directed at assessing the effect of the sex-matched RBC 
units in individuals who had or had not been transfused prior to randomization. Finally 
regression models incorporating interactions between treatment arm and patient 
characteristics will be fitted to assess variation in effects across patient groups with 
different underlying conditions including whether they are surgical or medical patients 
and whether they have a malignancy or sepsis.   
  
6.4 COST-EFFECTIVENESS ANALYSES  
An economic analysis alongside the trial will be conducted over a 90-day time horizon 
from the public payer’s perspective. We will estimate the cost to blood suppliers (e.g., 
the cost of producing recipient-donor sex-matched RBC units), the cost of 
hospitalization, and other health care costs during the 90 days using the ICES datasets. 
Ninety-day mortality will be used as the outcome measure for the economic analysis. 

 
 
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95% CI of the incremental cost per life year saved (ICER) will be assessed using the 
non-parametric bootstrapping method and cost effectiveness acceptability curve over a 
range of willingness-to-pay thresholds will be presented.      
7. STUDY MANAGEMENT 
This study is endorsed by the Canadian Transfusion Trial Group. Management of the 
trial will be coordinated by MCTR, an experienced group in transfusion-related research 
with an extensive track record in coordinating multi-centre RCTs. Infrastructure for trial 
coordination and data management are well established within this group. The study 
data will be captured electronically and transferred via a dedicated FTP server (or 
equivalent) located in the Computer Services Unit (CSU) at McMaster University, then 
stored on MCTR’s secure server also located in the CSU at McMaster University. These 
servers are regularly maintained, monitored daily for any problems, and all data are 
backed-up regularly. Only the study biostatistician and the study coordinator will have 
access to the study data. Blood bank inventory sticker labelling is described in Section 
3.3 and Figure 4.  
 
Randomization: Described in Section 3.4.4.  
Data Collection: Data will be captured electronically at pre-specified intervals from the 
EMR and LIS. All participating centres have proven track records for collecting the data 
required for this study using an electronic approach. Figure 6 illustrates the strategy for 
confidential data collection. Queries will be run to identify issues; sites will be asked to 
respond in a timely manner. The same level of scrutiny and follow-up will be applied to 
this electronic approach as would be to case report forms received during any RCT.   
Data Collection from EMR: Each patient’s demographic and clinical information (primary 
and secondary diagnoses and interventions) is coded following discharge by trained 
medical chart abstractors and stored in the hospital’s EMR. Data variables will be 
collected as per pilot and influenced by iTADS. Data variables available to extract are 
summarized in Table 6.  
Data Collection from the LIS: RBC product and patient transfusion data will be extracted 
monthly from the LIS (Table 6). All sites have the capability of extracting the relevant 
LIS data. The LIS is the gold standard source for accurate and complete transfusion 
data, validated to meet regulatory standards.69   
Data Linking and Confidentiality: Data from all sources will contain a specific patient 
identifier needed for linking. To protect patient identity and maintain confidentiality and 
compliance with privacy requirements, this unique patient identifier will be either 
protected, recoded, or given a new study ID as per individual site REB -requirements. 
Once the identifiers have been managed at the site level, the data files will be exported 
to the Coordinating Centre. At the Coordinating Centre, the individual site files from 
medical records and the LIS will be linked using re-coded patient identifiers; this 
approach for maintaining privacy and confidentiality is compliant with current Canadian 
legislation.  
Data Collection from CBS: CBS will provide a list with each inventory shipment of RBCs 
containing units coded according to colour which correspond to donor sex, though the 
blood bank staff will be blinded to this. We will also be collecting de-identified data on 

 
 
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donor age, hemoglobin level at time of donation, volume of unit and manufacturing 
process from CBS. There are currently two different manufacturing process for RBCs 
through CBS and the type of process has been shown to impact cell quality.73,74 
Data Collection from Ontario Registered Person Database/ICES: 30- and 90-day 
mortality data will be obtained following methodology from iTADS.32  
 
7.1 COMMITTEES 
Steering Committee (SC): The SC will be comprised of the Principal Investigators (PIs), 
Co-Investigators, Technical Specialist, patient representative, and the Study 
Coordinator. Responsibilities include study oversight and problem solving. The SC will 
meet by conference call at least every 3 months during the trial.  The Data Management 
Sub-Committee (DMSC), will be responsible for reviewing monthly reports and bringing 
issues to the SC.  
Technical Resource Committee (TRC): The TRC will be comprised of the PI, Site PI 
(ICU or TM MD) Study Coordinator, and a technical and medical representative from the 
Transfusion Services at all participating sites. This Committee will meet every two 
weeks during the first 3 months of the study to identify and resolve implementation 
issues and as needed throughout the trial.   
 
Data Safety Monitoring Committee (DSMC): Recruitment for this study will be 
approximately 40 months; an IDSMC will be formed (comprised of 3 members with 
expertise in Transfusion Medicine, Critical Care and Biostatistics) and will be 
responsible for reviewing aggregate outcome data every 6 month. They will make 
recommendations to the SC. They will provide an independent report related to any 
safety concerns including review of severe adverse events.  
 
7.2 ETHICS, CONSENT AND PRIVACY 
Individual consent waiver for patients randomized to receive sex-matched vs. sex-
mismatched RBCs is consistent with the five criteria specified in the Tri-Council Policy 
Statement.69 Our proposed study meets all five of these criteria (see Table 3). Approval 
for the pilot study was obtained through Clinical Trials Ontario (CTO) with the Hamilton 
Integrated REB as the REB of record (see Appendix A). To meet the Tri-Council 
requirement to inform patients we will provide all transfused ICU patients with 
information regarding the study at the time of discharge from the ICU/hospital. The 
information will be embedded in the patient electronic discharge summary or provided in 
paper form (as per site capability) and will state that they may have been included in 
this study and provides opt- out instructions. Similar brochures were used in the Sex 
Matters pilot, INFORM and, iTADS.   
 
We will ensure approval from Research Ethics board of all involved institutions, as well 
as from Canadian Blood Services (for donor sex information). All data collection and 
management will be performed in accordance with the Personal Health Information 
Protection Act of Ontario, Regulation 329/04. A unique de-identified number will identify 

 
 
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all patients and no patient identifiers will be kept with clinical data. The data will be 
encrypted and stored centrally at MCTR during the trial. No patients will be recruited 
before institutional approval is obtained. 
7.3 KNOWLEDGE TRANSLATION PLAN 
From the early development of this research project, we involved stakeholders and 
experts in a wide range of fields involved with the organization, research and the care of 
patients receiving transfusions (hematologists, intensivists, transfusion specialists, 
health-care researchers, epidemiologists, blood organization decision makers and 
senior scientists). This diversity of expertise will ensure that the research questions, 
objectives, methods and result analysis and interpretation answer pertinent questions 
for clinicians, but also for stakeholders, patients and the overall population. Our design 
will allow us to perform the required analyses to impact major blood users. 
Results of this RCT will inform policy on blood transfusion practices. If mortality is 
shown to be reduced with sex-matched transfusions, then a policy change can be 
implemented at the blood bank level. This will require re-evaluation of how blood bank 
inventories are managed and assigned. Results will be shared with the Canadian blood 
suppliers (CBS and Héma Québec) and disseminated with the help of the Canadian 
Transfusion Trials Group (CTTG) Patient Engagement Working Group and Knowledge 
Translation Working Group.   
 
8. STRENGTHS AND POTENTIAL CHALLENGES  
 
Knowledge users and decision makers from CBS are co-investigators on this study, 
providing their input and support to ensure they are aware of potential implications to 
the blood supplier. To support this study, we have previously completed an exploratory 
analysis,19 meta-analysis of observational data,20 and a CIHR-funded multisite feasibility 
pilot randomized controlled trial (RCT) (see Appendix B). The pilot met all feasibility 
outcomes and our experience has further informed construct and design of the 
proposed trial allowing us to address this research question in an efficient and cost-
effective manner (see Table 2 for feasibility outcomes and pilot study results).  
 
A main task is to ensure that every RBC unit stored in the participating sites blood 
banks are correctly classified as being of one of the two arms (see validation section 
above). This study will also require the training of blood bank personnel to perform an 
additional step (randomization of new patients or determining the study assigned group) 
prior to the release of an RBC unit. We have involved blood bank managers that will 
help with personnel training and study monitoring. Because of our track record of 
conducting large trials, all of which required extensive blood bank collaboration, we are 
confident in conducting this study in collaboration with our blood bank colleagues.  
9. ANTICIPATED RESULTS AND OUTPUTS  
Our study will provide robust evidence whether a sex-matched compared to a sex-

 
 
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mismatched RBC transfusion strategy improves survival of critically ill patients who 
receive a blood transfusion. We will also obtain important information regarding the 
recipient subgroups that may be less affected (or not at all) by such a practice and thus 
provide important information to the blood providers to help tailor transfusion practices 
to the patient. Our study results have potential to inform policy on blood transfusion 
practices in critically ill patients. If mortality is shown to be reduced with sex-matched 
transfusions, then a policy change can be implemented at the blood bank level. This will 
require re-evaluation of how blood bank inventories are managed and assigned as well 
as consideration of inventory, supply and cost from a systems level.  
 
9.1 ANTICIPATED CONTRIBUTIONS  
Knowing that anemia is common during critical illness, mismatched transfusions are 
inevitable once a patient received more than 6 RBC units (>97% chance) (Figure 1). 
Thus, optimizing supportive care strategies is needed to improve outcomes of this 
highly vulnerable patient group and a strategy as simple as matching RBC transfusions 
for donor sex may have significant impact.   
  
  
 
 

 
 
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Roubinian NH, Plimier C, Woo JP, et al. Effect of donor, component, and 

 
 
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recipient characteristics on hemoglobin increments following red blood cell transfusion. 
Blood. 2019;134(13):1003-1013. doi:10.1182/blood.2019000773  
44.  
Donadee C, Raat NJH, Kanias T, et al. Nitric Oxide Scavenging by Red Cell 
Microparticles and Cell Free Hemoglobin as a Mechanism for the Red Cell Storage 
Lesion. Cir. 2014;124(4):465-476. doi:10.1161/CIRCULATIONAHA.110.008698.Nitric  
45.  
Hod EA, Spitalnik SL. Stored red blood cell transfusions : Iron , inflammation , 
immunity , and infection. Transfus Clin Biol. 2012;19(3):84-89. 
doi:10.1016/j.tracli.2012.04.001  
46.  
Gladwin MT, Kanias T, Kim-shapiro DB. Hemolysis and cell-free hemoglobin 
drive an intrinsic mechanism for human disease. J Clin Invest. 2012;122(4):1205-1208. 
doi:10.1172/JCI62972.ischemic  
47.  
Li W, William N, Acker JP. Donor sex, pre-donation hemoglobin, and 
manufacturing affect CD71+ cells in red cell concentrates. Transfusion. 2023;n/a(n/a). 
doi:https://doi.org/10.1111/trf.17250  
48.  
Elahi S, Ertelt JM, Kinder JM, et al. Immunosuppressive CD71+ erythroid cells 
compromise neonatal host defence against infection. Nature. 2014;504(7478):158-162. 
doi:10.1038/nature12675.Immunosuppressive  
49.  
Namdar A, Koleva P, Shahbaz S, Strom S, Gerdts V. CD71 + erythroid 
suppressor cells impair adaptive immunity against Bordetella pertussis. Sci Rep. 
2017;(July):1-12. doi:10.1038/s41598-017-07938-7  
50.  
Miller D, Romero R, Unkel R, et al. CD71+ Erythroid Cells from Neonates Born to 
Women with Preterm Labor Regulate Cytokine and Cellular Responses. J Leukoc Biol. 
2018;103(4):761-775. doi:10.1002/JLB.5A0717-291RRR.CD71  
51.  
Liu Q, Wang M, Hu Y, et al. Significance of CD71 expression by flow cytometry in 
diagnosis of acute leukemia Signifi cance of CD71 expression by fl ow cytometry in 
diagnosis of acute leukemia. Leuk Lymphoma. 2014;55(4):892-898. 
doi:10.3109/10428194.2013.819100  
52.  
Chan KT, Choi MY, Lai KKY, et al. Overexpression of transferrin receptor CD71 
and its tumorigenic properties in esophageal squamous cell carcinoma. 2014;31:1296-
1304. doi:10.3892/or.2014.2981  
53.  
Li W, Acker J. CD71+ RBCs: A potential immune mediator in transfusion. 
Transfus Apher Sci. 2023;Accepted;  
54.  
Gutierrez M, Fish MB, Golinski AW, Eniola-adefeso O. Presence of Rigid Red 
Blood Cells in Blood Flow Interferes with the Vascular Wall Adhesion of Leukocytes. 
Langmuir. 2018;34:2363-2372. doi:10.1021/acs.langmuir.7b03890  
55.  
Alshalani, A., van Bruggenm R., Juffermans N. The impact of donor sex on 
endothelial cell activation following red blood cell transfusion. Vox Sang. 2020;TBA.  
56.  
Elahi S. New insight into an old concept : role of immature erythroid cells in 
immune pathogenesis of neonatal infection. Front Immunol. 2014;5(August):1-7. 
doi:10.3389/fimmu.2014.00376  
57.  
Alshalani A, de Wissel M, Tuip-de-Boer AM, et al. Transfusion of female blood in 
a rat model is associated with red blood cell entrapment in organs. PLoS One. 2022.  
58.  
Schandelmaier S, Briel M, Varadhan R, et al. Development of the instrument to 
assess the credibility of effect modification analyses (ICEMAN) in randomized controlled 
trials and meta-analyses. Cmaj. 2020;192(32):E901-E906. doi:10.1503/cmaj.200077  

 
 
Protocol V.1.4 October 15, 2025 
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59.  
Chassé M, Fergusson DA, Tinmouth A, et al. Effect of Donor Sex on Recipient 
Mortality in Transfusion; Supplementary Appendix. N Engl J Med. 2023;388(15):1386-
1395. doi:10.1056/nejmoa2211523  
60.  
Heddle NM, Cook RJ, Arnold DM, et al. Effect of Short-Term vs. Long-Term 
Blood Storage on Mortality after Transfusion. N Engl J Med. 2016;375(20):1937-1945. 
doi:10.1056/NEJMoa1609014  
61.  
Friedrich JO, Harhay MO, Angus DC, et al. Mortality As a Measure of Treatment 
Effect in Clinical Trials Recruiting Critically Ill Patients∗. Crit Care Med. 2023;51(2):222-
230. doi:10.1097/CCM.0000000000005721  
62.  
Rochwerg B, Millen T, Austin P, et al. Fluids in Sepsis and Septic Shock 
(FISSH): Protocol for a pilot randomised controlled trial. BMJ Open. 2017;7(7):1-5. 
doi:10.1136/bmjopen-2017-017602  
63.  
Barrot L, Asfar P, Mauny F, et al. Liberal or Conservative Oxygen Therapy for 
Acute Respiratory Distress Syndrome. N Engl J Med. 2020;382(11):999-1008. 
doi:10.1056/nejmoa1916431  
64.  
Cavalcanti AB, Suzumura ÉA, Laranjeira LN, et al. Effect of lung recruitment and 
titrated Positive End-Expiratory Pressure (PEEP) vs low PEEP on mortality in patients 
with acute respiratory distress syndrome - A randomized clinical trial. JAMA - J Am Med 
Assoc. 2017;318(14):1335-1345. doi:10.1001/jama.2017.14171  
65.  
Asfar P, Meziani F, Hamel J-F, et al. High versus Low Blood-Pressure Target in 
Patients with Septic Shock. N Engl J Med. 2014;370(17):1583-1593. 
doi:10.1056/nejmoa1312173  
66.  
Hernández G, Ospina-Tascón GA, Damiani LP, et al. Effect of a Resuscitation 
Strategy Targeting Peripheral Perfusion Status vs Serum Lactate Levels on 28-Day 
Mortality among Patients with Septic Shock: The ANDROMEDA-SHOCK Randomized 
Clinical Trial. JAMA - J Am Med Assoc. 2019;321(7):654-664. 
doi:10.1001/jama.2019.0071  
67.  
Cooper DJ, McQuilten ZK, Nichol A, et al. Age of red cells for transfusion and 
outcomes in critically ill adults. N Engl J Med. 2017;377(19):1858-1867. 
doi:10.1056/NEJMoa1707572  
68.  
Leach Bennett J, Blajchman MA, Delage G, Fearon M, Devine D. Proceedings of 
a Consensus Conference: Risk-Based Decision Making for Blood Safety. Transfus Med 
Rev. 2011;25(4):267-292. doi:10.1016/j.tmrv.2011.05.002  
69.  
Canadian Institutes of Health research. Tri-Council Policy Statement: Ethical 
conduct for research involving humans. 2005:1-93.  
70.  
Iron K, Zagorski B, Sykora K, Manuel D. Living and Dying in Ontario : An 
Opportunity for Improved Health Information; ICES Investigative Report.; 2008.  
71.  
Therneau T, Grambsch P. Modeling Survival Data: Extending the Cox Model. 
New York, NY: Springer-Verlag; 2000.  
72.  
Friede T, Kieser M. Blinded sample size re-estimation in superiority and 
noninferiority trials: bias versus variance in variance estimation. Pharm Stat. 
2013;12(3):141-146. doi:https://doi.org/10.1002/pst.1564  
73.  
Shih AW, Heddle NM, Barty R, Acker J, Investigators Q, Collaborative B. 
Processing methods and quality markers in the production of red blood cell units. 
International Society of Blood Transfusion Abstract 5A-534-02. Vox Sang. 

 
 
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2018;113(Supp. 1):65.  
74.  
Bakkour S, Acker JP, Chafets DM, et al. Manufacturing method affects 
mitochondrial DNA release and extracellular vesicle composition in stored red blood 
cells. Vox Sang. 2016;111(1):22-32. doi:10.1111/vox.12390  
75.  
Zeller MP, Barty R, Dunbar NM, et al. An international investigation into AB 
plasma administration in hospitals: How many AB plaSma units Were INfused? The 
HABSWIN study. Transfusion. 2018;58(1):151-157. doi:10.1111/trf.14368  
76.  
Zeller MP, Barty R, Aandahl A, et al. An international investigation into O red 
blood cell unit administration in hospitals: the GRoup O Utilization Patterns (GROUP) 
study. Transfusion. 2017;57(10):2329-2337. doi:10.1111/trf.14255  

 
 
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Figure 1. Plot of probability of receiving sex-mismatched RBCs by total number of 
RBCs transfused. The probability of receiving sex-mismatched RBCs was estimated 
assuming donor sex distribution of blood based on history data supplied from 
Transfusion Research Utilization Surveillance and Tracking (TRUST) (RBCs from 
female donors 43.7%; from male donors 56.3%). Probabilities of receiving sex-
mismatched RBC unit when transfused >10 RBC units are extremely close to 1 and not 
shown. The actual probabilities for each point on the graph are summarized in the Table 
below.   
  
 
 
Proportions 
Number of 
RBCs  
Female 
recipient  
Male recipient  
1  
0.56300  
0.43700  
2  
0.80903  
0.68303  
3  
0.91655  
0.82155  
4  
0.96353  
0.89953  
5  
0.98406  
0.94344  
6  
0.99304  
0.96815  
7  
0.99696  
0.98207  
8  
0.99867  
0.98991  

 
 
Protocol V.1.4 October 15, 2025 
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Figure 2. Random effects meta-analysis of observational data comparing recipient 
mortality outcomes in patients transfused sex-matched compared to sex-mismatched 
red blood cell transfusions.  
  
  
A note on observational studies and their limitations:  
 Observational studies are always limited by confounding; hence, causation or lack of 
evidence for causation can only be demonstrated by randomized controlled trials. This 
concept has been clearly illustrated by research related to harm when stored red blood 
cells (RBCs) are transfused. Over 30 observational studies were conducted in this area 
of research with discrepant results. Subsequently, well conducted  randomized 
controlled trials provided clarity demonstrating that fresh RBCs were not superior to 
stored RBC.1,2 This area of research took over 25 years of laboratory, observational and 
experimental research to finally reach the conclusion that the storage lesion did not 
affect patient outcome.   
  
1.  
Chai-Adisaksopha C, Alexander PE, Guyatt G, et al. Mortality outcomes in 
patients transfused with fresher versus older red blood cells: a meta-analysis. Vox 
Sang. 2017:1-11. doi:10.1111/vox.12495  
2.  
Heddle NM, Cook RJ, Eikelboom JW. Short-Term versus Long-Term Blood 
Storage. N Engl J Med. 2017;376(11):1091-1094. doi:10.1056/NEJMc1700464  
9  
0.99942  
0.99432  
10  
0.99975  
0.99680  

 
 
Protocol V.1.4 October 15, 2025 
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Figure 3. Flow diagram of effects of donor-recipient sex-matched blood transfusion on 
patient outcomes randomized controlled trial.  
 
  
  
 
 
 
 
 
 
 
 
 
 
 
 

 
 
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Figure 4. High level red blood cell inventory flow diagram.  
  
  
    
  
   
  
 
  
 
 
 
 
 

 
 
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Figure 5. Sticker validation quality control.  
  
  
  
Note: During the pilot RCT, 6,576 units were labelled over the course of the study; 
1,320 units were checked (20%), with a total of 6 units discrepant (0.5%) across all 
sites.  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
Protocol V.1.4 October 15, 2025 
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Figure 6. Data flow diagram illustrating the unique electronic data capture process to be 
used in the study, the integration of data from Canadian Blood Services (CBS) to 
identify the donor sex of each transfused RBC, the integration of data from TTISS-ON to 
identify transfusion reactions, and the strategy and the process to ensure privacy of the 
patient data.  
  
 
 
 
 
 
RedCap Randomization 
dataset 
• PaƟent IdenƟﬁers 
• AllocaƟon assignment 
Medical Record DAD 
• PaƟent demographics 
• PaƟent clinical data 
• IdenƟﬁer for linking 
LIS 
• Product characterisƟcs 
• Transfusion data 
• IdenƟﬁer for linking 
Medical Record Number to be scrambled by the 
person responsible for extracting data from each 
source (ensuring patient confidentiality) 
Anonymous data to be transferred 
to the MCTR secure server for 
linking and analysis 
SexMatters datasets for 
analysis 
CBS donor dataset 
• ProducƟon data 
• Donor/donaƟon data 
TTISS 
• Transfusion reacƟon 
External death registry data 
• 30 and 90-day mortality 

 
 
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Table 1a. Mortality rates for transfused patients in select Hamilton Intensive Care Units 
with projected reductions in mortality rate per year estimated using three-year history 
data (2015-2017).    
Yearly Data  
HHS – 
General  
ICU-EAST  
HHS- 
General  
ICU-
SOUTH  
St Joes  
JI-ICU  Total  
# patients admitted to ICU  
714  
500  
774  
1988  
# (%) patients transfused during 
ICU stay  
184 (25.8)  123 (24.6)  
195 
(25.2)  
502 (25.3)  
# (%) deaths in ICU patients 
transfused  
58 (31.8)  
39 (31.4)  69(35.4)  166 (33.1)  
Deaths reduced by 0.5% lower 
mortality  
1  
1  
1  
3  
Deaths reduced by 1% lower 
mortality  
2  
1  
2  
5  
Deaths reduced by 2% lower 
mortality  
4  
2  
4  
10  
   
Table 1b. Overall 30- and 90-day in-hospital mortality rates in patients from the pilot 
RCT.  
Pilot Outcomes  
Modified Intention-to-
Treat Analysis  
Per Protocol 
Analysis  
# patients  
379  
335  
# (%) patients 30 day in-hospital 
mortality  
110 (29.0)  
98 (29.3)  
# (%) patients 90 day in-hospital 
mortality  
130 (34.3)  
113 (33.7)  
  
Table 2. Pilot feasibility outcome results. 
Proportion we 
expected to achieve 
during the pilot 
study  
Proportion that we set 
to define success for 
the feasibility 
outcomes  
Actual 
proportion 
achieved 
during the 
pilot study1  
Randomizing eligible 
patients (patient 
level)  
90%  
80%  
79%2  
Recruitment 
compliance (patient 
level)  
95%  
90%  
97%  
Protocol adherence 
(transfusion level)   
97%  
90%  
88%3  
1Results from patients recruited during the feasibility period of recruitment only.   

 
 
Protocol V.1.4 October 15, 2025 
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2We identified that staffing shortages in the Blood Bank was a significant barrier to 
randomizing eligible patients. Sites that were not experiencing staffing shortages during 
the pilot were able to recruit up to 100% of eligible patients. Moving forward, we will 
assess Blood Bank capacity and resources before initiated the study.    
  
3We identified that inventory inadequacy and the inability to override automated blood 
choice mechanisms (ex. specialized fridges that automatically dispense RBCs based on 
ABO/Rh compatibility only) had a small impact on protocol adherence. Moving forward, 
we will ensure that sites are receiving and labeling a robust RBC inventory and have 
identified instances where choosing units based on sex compatibility stickers is not 
possible.   
  
  
Table 3. Criteria specified in the Tri-Council Policy Statement for waiver of informed 
consent.  
  
Criteria Specified in the Tri- 
Council Statement for Waiver 
of Informed Consent  
Justification for Waiving Informed Consent  
1) The research involves no 
more than minimal risk to the 
participants  
The interventions in this study are two different 
strategies to distribute blood inventory for the 
provision of a RBC transfusion. Both interventions fall 
within the current practice for the provision of a RBC 
transfusion. The study arms include provision of sex-
matched RBCs compared to sex-mismatched RBCs. 
Hence, the study presents no more than minimal risk 
to participants as standard of care does not take 
donor sex into consideration.    
2) The alteration to consent 
requirements is unlikely to 
adversely affect the welfare of 
participants  
  
There is no reason to suspect that the waiver will 
adversely affect the welfare of patients. The current 
standard of care does not consider nor match donor-
recipient sex. In this study, some patients will receive 
blood from donors of the same sex and others will be 
sex-mismatched. The intent of the study is to see if 
there is any evidence that giving sex-matched blood 
is beneficial in reducing recipient mortality risk.    
3) It is impossible or 
impracticable to carry out the 
research and to address the 
research question properly, given 
the research design, if the prior 
consent of participants is 
required  
  
Transfusion requests are received on a 24/7 basis 
and our study population is comprised of Intensive 
Care Unit (ICU) patients, making it extremely difficult 
logistically to obtain consent in a timely manner from 
all patients in advance of their transfusion. Attempting 
to obtain consent would ultimately compromise the 
objective of the study to efficiently recruit a large 
representative population in a timely manner without 

 
 
Protocol V.1.4 October 15, 2025 
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delaying their blood transfusion.   
4) In the case of a proposed 
alteration, the precise nature and 
extent of any proposed alteration 
is defined  
The specific alteration that we are proposing for this 
study is waived consent to participate in the RCT. As 
mentioned, it would be impracticable to obtain 
consent from all patients in a timely manner and 
without delaying the transfusion.  Furthermore, the 
study is evaluating the effect of sex-matched RBC 
transfusions on clinical outcomes. Outside of the 
study, consent for the provision of transfusion as a 
medical treatment has been obtained and the choice 
to transfuse a patient is at the discretion of the 
treating physician.    
5) Participants will be provided a 
debriefing, which may also offer 
participants the possibility of 
refusing consent and/or 
withdrawing data and/or human 
biological materials  
We will provide all transfused patients in participating 
ICUs with an information pamphlet about the study. 
This pamphlet will inform patients about the nature of 
the study while minimizing the potential for fear and 
apprehension for patients. We used this approach 
successfully in the INFORM study and the pilot trial 
for this RCT, and the approach and pamphlet was 
approved by the REB in both cases. See patient 
information entitled “Blood Transfusion- Did YOU 
know?”  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
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Table 4. Thirty-day in-hospital mortality in the pilot trial was 29% and evidence from the 
literature suggests that 30-day all cause mortality in transfused patients admitted to the 
ICU is ~33%. In consultation with knowledge users and decision makers from CBS, 
intensivists, transfusion medicine experts, transfusion providers, methodologists, and 
patients, we will target an absolute decrease in mortality of 3% (90% power, Type 1 
error 0.05), which will require 5,037 patients/arm. Other transfusion studies in the ICU 
have used 4.2%64 and 5%18 reduction in in-hospital mortality, with baseline rates of 24-
33%. We target a sample size 10% higher (n=5541/arm) to accommodate potential non-
compliance to allocation arm and possibility of variable mortality rates in some hospital 
sites, as observed in the pilot trial. The total sample size is therefore 11,082.  
  
Type I error rate = 5% (Zα/2 = 1.96); Power = 90% (Z1−β = -1.28); Balanced design  
   
*A 3% absolute risk reduction was selected as clinically important in consultation with 
knowledge users and decision makers from CBS, methodologists, intensivists, 
transfusion medicine experts, transfusion providers and patients. It is similar to the 4.2% 
and 5% absolute risk reduction that was used to calculate the sample size for other 
studies of ICU patients that assessed the impact of blood storage duration (Cooper 
NEJM 2017; Lacroix NEJM 2015). 
 
  
 
 
 

 
 
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Table 5. Participating sites   
Hospital   
Site Co-investigators or 
leads 
Participated in pilot 
RCT  
Hamilton General Hospital   
Dr. Andrew Shih 
Dr. Paul Engels 
Pilot participant  
Sunnybrook Health Science   
Dr. Yulia Lin  
Dr. Neill Adhikari 
Pilot participant  
London Health Sciences Centre  
Dr. Ziad Solh  
Dr. Marat Slessarev 
Pilot participant  
Kingston Hospital Sciences Centre  Dr. Jeannie Callum  
Dr. Stephanie Sibley 
Pilot participant  
Lakeridge Hospital 
Dr. Karim Soliman 
Dr. Nadia Gabarin 
Mount Sinai Hospital  
Dr. Nadine Shehata  
Ottawa Hospital – General and 
Civic Campuses 
Dr. Shane English  
Dr. Alan Tinmouth 
University Health Network – 
General  
Dr. Nadia Gabarin   
Dr. Jacob Pendergrast  
Dr. Eddy Fan 
 
Table 6. Relevant data that can be pulled from the Laboratory Information System (LIS) 
and/or medical records, and may be pulled for this study.   
  
Data Extracted from LIS   
Blood product inventory  
Transfused patients (inpatient and 
outpatient)  
Transfused blood product  
• 
Hospital site*  
• 
Product Unit Number*  
• 
Product names (Red cells, 
Plasma, Platelets, 
Cryoprecipitate)*  
• 
CBS product code*  
• 
Product marker (CMVNEG, 
irradiated and etc.)*  
• 
Product collection date and expiry 
date*  
• 
Issue date/time*  
• 
Product final disposition 
(transfused, presumed transfused 
or returned) * 
• 
Ward at product issue*  
• 
Product source (CBS collection 
center codes)  
Transfused patients  
 
• 
Hospital ID (MRN)*  
• 
Encounter number * 
• 
Patient age at transfusion*  
• 
Patient gender and/or sex*  
• 
Patient marker (e.g. phenotype marker 
or study SEMA markers etc.)*  
• 
Patient ABO group and RH type  
• 
Admission date/time and discharge 
date/time  
• 
Admission status 
(inpatient/outpatient/ER admission)  
 
Transfused patient laboratory testing  
• 
Specimen collection date/time  
• 
Result verify date/time  
• 
Test name (Hemoglobin, Platelet CT, 

 
 
Protocol V.1.4 October 15, 2025 
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• 
Product ABO group and RH type  
• 
Product receipt date/time  
• 
Product antigen name and result  
• 
Volume issued  
 
Product Orders   
• 
Hospital ID (MRN)  
• 
Order date/time  
• 
Ward at order   
• 
Product ordered  
• 
Specialty of order physician 
PT in INR, APTT, Clauss Fibrinogen, 
Creatinine) and result within 24 hours 
prior to randomization 
 
 
Group and Screen Results    
• 
Hospital ID (MRN)  
• 
Specimen collection date/time  
• 
Result verify date/time  
• 
ABO/Rh result  
• 
Screen result (positive, negative, 
NP,ND)  
• 
Positive antibody name 
  
Data Extracted from Medical Records   
Inpatient admissions  
Patient demographics  
• 
Institution*  
• 
Hospital ID (MRN)*   
• 
Ontario Health Insurance Plan (OHIP) 
number 
• 
Patient age and gender and/or sex*  
 
Admission information   
• 
Encounter number*  
• 
Admission date/time and discharge 
date/time*  
• 
Acute length of stay  
• 
Admission category   
• 
Discharge disposition  
• 
 Most responsible physician service code  
ICU   
• 
ICU admission date/time and discharge 
date/time*  
• 
ICU units *  
Diagnosis   
• 
Diagnosis type (primary, secondary and etc.)  
• 
Diagnosis code (ICD 10)  
 
Interventions  
• 
Intervention Code (CCI codes)  
• 
Intervention start date/time and end 
date/time  

 
 
Protocol V.1.4 October 15, 2025 
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• 
Location (OR rooms, or departments, units)  
• 
Intervention doctor service  
• 
Unplanned return to OR   
  
  
Notes:   
1. All variables listed in the tables above will be collected for all randomized 
patients. Only variables with * will be collected from all patients in participating 
intensive care unit(s) (regardless of randomization status) between date of last 
report and date of current report as part of regular denominator data pulls.  
2. All variables listed in the tables above will be collected from all patients in 
participating intensive care unit(s) (regardless of randomization status) within the 
previous month as part of start-up data pulls. These data are required to confirm 
that sites are able to pull all required variables. This may be completed several 
times, as required, until confirmation of satisfactory data and subsequent site 
activation. These data will not be analyzed and will be destroyed upon 
confirmatory check.   
3. In addition to the data above provided by sites, we will also collect data from the 
following sources:  
a. Data on unit and donor characteristics from Canadian Blood Services  
b. Data on transfusion reactions from the Ontario Transfusion Transmitted Injuries 
Surveillance System  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  

 
 
Protocol V.1.4 October 15, 2025 
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 Appendix A: Pilot Research Ethics Approval  
 

 
 
Protocol V.1.4 October 15, 2025 
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Appendix B: Pilot Canadian Society for Transfusion Medicine Annual Conference 2023 
Abstract 
 
 
 

 
 
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