Association of donor age, body mass index, hemoglobin, and smoking status with in‐hospital mortality and length of stay among red blood cell–transfused recipients

Roubinian, Nareg; Westlake, Matt; Lezin, Elizabeth St.; Edgren, Gustaf; Brambilla, Donald; Lee, Catherine; Bruhn, Roberta; Cable, Ritchard G.; Triulzi, Darrell J.; Glynn, Simone A.; Kleinman, Steve; Murphy, Edward L.; Epidemiology, Nhlbi Recipient; Study‐III, Donor Evaluation

doi:10.1111/trf.15541

Cited by 13 publications

(21 citation statements)

References 46 publications

(149 reference statements)

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“…Although substantial engineering and testing would be needed, in principle the presented strategy could be adapted to an inexpensive laser-free imaging flow cytometer for resource-poor situations. Improved techniques to monitor blood product quality would revolutionize efforts to personalize allocation of blood products based on factors thought to impact RBC quality, including donor characteristics (age, sex, ethnicity, frequency of donation) ( 44 , 46 – 50 ). Like many artificial-intelligence–driven analysis systems introduced in recent years, the goal need not be to entirely eliminate expert interaction but instead to screen samples or cell images to identify the most readily classifiable, so that the expert’s time is used on samples or cells that are more borderline.…”

Section: Discussionmentioning

confidence: 99%

Objective assessment of stored blood quality by deep learning

Doan

Sebastian

Caicedo

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans’ assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

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Section: Discussionmentioning

confidence: 99%

Objective assessment of stored blood quality by deep learning

Doan

Sebastian

Caicedo

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…While self‐reported smoking may underestimate its true prevalence, rates of smoking in donors (6.4%) paralleled those of transfused recipients in our study. However, this rate was lower than in prior studies, increasing the possibility of a chance result (Type I error) related to the study of infrequent events (the combination of donor smoking and gamma irradiation) on transfusion outcomes . The small number of gamma irradiated RBC units from smokers (approx.…”

Section: Discussionmentioning

confidence: 75%

Additive effects of blood donor smoking and gamma irradiation on outcome measures of red blood cell transfusion

et al. 2020

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BACKGROUND Recent publications have reported conflicting results regarding the role of blood donor tobacco use on hemoglobin (Hb) levels in patients after red blood cell (RBC) transfusion. We examined associations and interactions between donor, component, and recipient factors to better understand the impact of donor smoking on transfusion outcomes. STUDY DESIGN AND METHODS We linked blood donor and component manufacturing data, including self‐reported cigarette smoking, with a cohort of patients transfused RBCs between 2013 and 2016. Using multivariable regression, we examined Hb increments and subsequent transfusion requirements after single‐unit RBC transfusion episodes, adjusting for donor, component, and recipient factors. RESULTS We linked data on 4038 transfusion recipients who received one or more single‐unit RBC transfusions (n = 5086 units) to donor demographic and component manufacturing characteristics. Among RBC units from smokers (n = 326), Hb increments were reduced after transfusion of gamma‐irradiated units (0.76 g/dL; p = 0.033) but not unirradiated units (1.04 g/dL; p = 0.54) compared to those from nonsmokers (1.01 g/dL; n = 4760). In parallel with changes in Hb levels, donor smoking was associated with the receipt of additional RBC transfusions for irradiated (odds ratio [OR], 2.49; p = 0.01) but not unirradiated RBC units (OR, 1.10; p = 0.52). CONCLUSION Donor smoking was associated with reduced Hb increments and the need for additional transfusions in recipients of gamma‐irradiated RBC units. Additional research is needed to better understand interactions between donor, component, and recipient factors on efficacy measures of RBC transfusion.

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“…A retrospective cohort study of 93,726 patients in the REDS-III program showed that hospital length of stay was lower for the recipients of RBC units from young donors (<20 years old) [ 81 ]. Analysis of data separately for female and male RBCs gave a similar result.…”

Section: Red Blood Cellsmentioning

confidence: 99%

“…Analysis of data separately for female and male RBCs gave a similar result. The effect was deemed small and appeared when four or more units of RBCs were transfused [ 81 ]. Inflammatory markers change with age, and this can be investigated further in relation to the apparently favorable outcomes from the transfusion of young donor’s RBCs [ 81 ].…”

Section: Red Blood Cellsmentioning

confidence: 99%

“…The effect was deemed small and appeared when four or more units of RBCs were transfused [ 81 ]. Inflammatory markers change with age, and this can be investigated further in relation to the apparently favorable outcomes from the transfusion of young donor’s RBCs [ 81 ]. Interestingly, a cohort study of 30,503 RBC recipients in Canada had observed the opposite effect, namely that RBC transfusion from younger donors was associated with increased mortality [ 74 ].…”

Section: Red Blood Cellsmentioning

confidence: 99%

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Current Understanding of the Relationship between Blood Donor Variability and Blood Component Quality

Hadjesfandiari

Khorshidfar

Devine

2021

IJMS

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While differences among donors has long challenged meeting quality standards for the production of blood components for transfusion, only recently has the molecular basis for many of these differences become understood. This review article will examine our current understanding of the molecular differences that impact the quality of red blood cells (RBC), platelets, and plasma components. Factors affecting RBC quality include cytoskeletal elements and membrane proteins associated with the oxidative response as well as known enzyme polymorphisms and hemoglobin variants. Donor age and health status may also be important. Platelet quality is impacted by variables that are less well understood, but that include platelet storage sensitive metabolic parameters, responsiveness to agonists accumulating in storage containers and factors affecting the maintenance of pH. An increased understanding of these variables can be used to improve the quality of blood components for transfusion by using donor management algorithms based on a donors individual molecular and genetic profile.

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Association of donor age, body mass index, hemoglobin, and smoking status with in‐hospital mortality and length of stay among red blood cell–transfused recipients

Cited by 13 publications

References 46 publications

Objective assessment of stored blood quality by deep learning

Objective assessment of stored blood quality by deep learning

Additive effects of blood donor smoking and gamma irradiation on outcome measures of red blood cell transfusion

Current Understanding of the Relationship between Blood Donor Variability and Blood Component Quality

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