R-FACT Study:Risk Factors for Alloimmunization After Red Blood Cell Transfusions (R-FACT)
|Study Design:||Time Perspective: Retrospective|
|Official Title:||R-FACT Study:Risk Factors for Alloimmunization After Red Blood Cell Transfusions|
- Red blood cell allo-antibody formation [ Time Frame: An average of 8 weeks after study initiation in a participating center ] [ Designated as safety issue: No ]Outcome measure would be assessed in a participating study center after an average 8 weeks from the initiation of the study in that particular study center
Biospecimen Retention: Samples With DNA
|Study Start Date:||January 2009|
|Estimated Study Completion Date:||August 2017|
|Estimated Primary Completion Date:||August 2017 (Final data collection date for primary outcome measure)|
Cases and Controls
Cases: alloantibody formers Controls: non-alloantibody formers
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Introduction: Individuals exposed to red blood cell (RBC) alloantigens through transfusion, pregnancy, or transplantation may produce antibodies against the alloantigens expressed by RBCs. Although the incidence of these events is debated and ranges between percentages of 1-6% in single transfused patients and up to 30 % in poly-transfused patients (e.g. sickle cell disease, thalassemia and myelodysplasia), they can pose serious clinical problems such as delayed haemolytic reactions as well as logistic problems e.g. to obtain timely and properly matched transfusion blood for patients in which new alloantibodies are detected.
Rationale: It is known that the risk of a recipient to develop antibodies depends on dose and route of administration and the immunogenicity of the antigen, as well as on genetic or acquired patient-related factors. The latter factors however are ill defined and therefore we hypothesize that the particular clinical conditions (e.g. used medication, concomitant infection, cellular immunity) during which transfusions are given may contribute to the risk of immunization.
Research objective: Examine the association between clinical, environmental and genetic characteristics of the recipient of erythrocyte transfusions and the risk against immunization against erythrocyte alloantigens exposed to during that transfusion episode.
METHODOLOGY Study Design and study population We will perform a retrospective matched case- cohort study at hospitals nationwide from a period January 2005 to December 2011. Large red blood cell using hospitals will be selected as study bases. The study cohort will comprise of consecutive red blood cell transfused patients at the study center.
Cases are defined as first time ever irregular red blood cell antibody formers, with no prior history of red blood cell transfusions and alloimmunization before the study period.
Controls will be all consecutive transfused patients who had received their first and subsequent red blood transfusions at the study center with no prior history of red blood cell transfusions and alloimmunization.
Observational studies, if well conducted, are equipped to examine interesting transfusion research questions. With that in mind, we chose a case- cohort study design for our study. With the help of such a design, we can compare the cases occurring in a red blood cell transfused cohort with a randomly selected sample of the cohort. Using such an approach, for any one given case, we will select 2 controls that have had at least the same number or more transfusions than the case itself. This approach has following advantages:
- This ensures that all the patients in the transfusion cohort with same or higher number of transfusions have an equal chance of being picked as controls. In essence, any member of the cohort who has been at a similar transfusion risk (of alloimmunization) at some point in their transfusion history can be selected as a control.
- Cases also have an equal chance of getting selected as controls for other cases.
This study design minimizes the selection bias, if any. Such a study design allows us to include a number of patients which is sufficient to detect smaller effects and to adjust for other risk factors, as well as document potential risk factors extensively.
Matching We will take in to account the number of transfusions a particular case received uptil the antibody forming episode, and match the 2 cases (selected per control) on the same number of transfusions.
To account for inter- hospital differences nationwide, we will also match the cases and controls on the site/ study center.
Implicated Period To examine the immunomodulating clinical risk factors surrounding the transfusions preceding the date of alloantibody formation, we will define a clinical risk period or an implicated period of alloimmunization during which the case would have formed an irregular red blood cell antibody. This period would be the time (in days) between the date of a first ever positive screen for alloantibody to a calendar date 30 days before that positive screen. We will also introduce a lag period of minimum 7 days between that first ever positive screen and the last ever transfusion (implicating transfusion) before that positive screen. (Figure 2) This is to ensure that a patient's immune system has adequate time to respond to the transfusion exposure.
We will define a similar implicated period in the matched controls as well, retrospectively from the "implicating transfusion" to 30 calendar days back.
First time formed alloantibody Our endpoint for cases, or first time formed irregular red blood cell antibodies is defined as clinically significant antibodies as screened by a three cell serology panel at 37 degree Celsius. All patients were routinely screened for alloantibodies, which is repeated at least every 72 hours, if further transfusions as required. The antibodies are screened for by a three cell panel including an indirect antiglobulin test (LISS Diamed ID gel system) and subsequently identifies by a standard 11 cell panels in the same gel system.
Data acquirement, measurements and handling Transfusion cohort data will be acquired from the hospital blood transfusion services and on site patient records. Second we will use data from a patient questionnaire. Thirdly, we will determine the patients' racial background from blood of the included and consenting patients.
4.3.1 Patient Medical history and records Potential clinical risk factors include haematological, oncological, surgical and medicinal data as well as auto- immune diseases and related conditions at the time of the implicated (likely causal) transfusion. Factors and conditions that will be actively scored are, infections (including the causal microorganisms) and active / chronic allergies (including the if known antigens), fever, cytopenia(s), systemic inflammatory response (a clinical response to a (non)-specific insult of either infectious or noninfectious origin), peripheral blood progenitor cells transplantation (autologous or allogenous), multi trauma, splenectomy, solid malignancies, autoimmune disorders (rheumatoid arthritis, diabetes mellitus type 1 etc.), chemotherapy, immunosuppressive drugs, cytostatics and antibiotics will be studied.
Questionnaire Participants will be asked to fill out a printed questionnaire. The participants have also the option to fill in a web-based questionnaire, which will be accessible via a link provided in the information letter. After identification of control patients a similar mailing will be sent to these controls Environmental and life style factors like vaccination status, previous pregnancies in case of females, level of education and current professions (as a proxy for socio- economic status) will be obtained via the patient information questionnaire. The questionnaire will add to the knowledge to these possible confounders in cases and controls.
In general, many questions will involve "life-time" risk factors and information and are not particularly targeted at the time of implicated episode.
Racial confounder Based on the knowledge that different ethnicities have varying frequencies of erythrocyte antigens, a so- called mismatch between a donor from one particular ethnicity and the recipient of another ethnicity does play a role in developing immune response to donor erythrocytes. Therefore, we will also attempt to document racial mismatch leading to red blood cell alloimmunization. This is attempted by one question in the questionnaire but will foremost rely on the blood group typing which usually determines the ethnicity.
Blood research and sampling To investigate the effect of genetic factors on the risk of the development of alloantibodies, we will collect blood samples from all participants for extensively typing the blood to get an antigen profile and to look at genetic markers which influence immune system and vaccination efficiency. SNP's in candidate genes (e.g. coding for HLA types) modulating specific and innate immune responses will be assessed. Biomarkers typical for the activity of the immune response: cytokines and titres of antibodies against common (vaccinated) antigens can later be determined in the plasma and serum that are stored as well.
Statistical analysis We expect to include a total of 500 case patients and 1000 controls. Logistic regression models will be used to assess the association between the risk to develop antibodies and potential risk factors, adjusted for other risk factors and for the number of exposures to the antigen.
We will examine the association between the risk factor and alloimmunization using logistic regression.
We will also make a selection of all cases and controls on the most frequently found antibodies and if the relative impact of risk factors and immune modulators on the risk of all the antibody types( in separate analysis) is in the same direction, we will make a generalized observation.
With 1500 patients, and the conventional 80% power and a p-value of 0.05, we will be able to detect effects (odds ratio) of dichotomized risk factors of 1.35 or higher.
An additional analysis will be performed along the lines of a "case-crossover" design within the case patients. The "Hazard Period" (time period right before the detection of a positive antibody) will be compared to a "Control Component" (a specified time period other than the Hazard Period) in the case patient's medical history and the risk ratio for the transient effect risk factors will be calculated.
Please refer to this study by its ClinicalTrials.gov identifier: NCT01616329
|Contact: Jaap Jan Zwaginga, MD PhDfirstname.lastname@example.org|
|Leiden University Medical Center||Recruiting|
|Leiden, Netherlands, 2300RC|
|Contact: Jaap Jan Zwaginga, MD Phd email@example.com|
|Principal Investigator:||Johanna van der Bom, MD Phd||Sanquin- LUMC, Leiden|
|Principal Investigator:||Jaap Jan Zwaginga, MD Phd||Sanquin-Lumc, Leiden|
|Principal Investigator:||Dorothea Evers, MD MSc||LUMC Leiden|