Not all red cell concentrate units are equivalent: international survey of processing and in vitro quality data

Shih, Andrew W.; Apelseth, Torunn Oveland; Cardigan, Rebecca; Marks, Denese C.; Bégué, S.; Greinacher, Andreas; Korte, Dirk de; Seltsam, Axel; Shaz, Beth H.; Wikman, Agneta; Barty, Rebecca; Heddle, Nancy M.; Acker, Jason P.

doi:10.1111/vox.12836

Cited by 16 publications

(19 citation statements)

References 23 publications

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“…RBCs are degraded by continued storage, yielding oxidative damage, decreased oxygen release capability, and membrane deformation, which can affect the in vivo circulation of transfused RBCs ( 1 – 6 ). Technological progress in the preservation and storage of cells has enabled blood banks to store RBCs at 1 to 6 °C for up to 8 wk in some countries ( 7 – 10 ). During storage, however, the loss of membrane integrity causes the red cells to morph reversibly from regular biconcave discocytes (smooth/crenated discs) into echinocytes (crenated discoid and spheroid), characterized by membrane protrusions or spicula.…”

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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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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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“…10,11 Microvesicle formation is thought to be a main cause of posttransfusion complications due to their role in: (a) the pathogenesis of thrombosis, 12 inflammation, [13][14][15] and responses to pathogens; 16 (b) platelet activation; 17 (c) deformability changes that affect RBC rheology and blood flow; 18 and (d) decreased oxygen delivery due to microvesicle-entrapped Hb, 19 among others. The extent of this storage lesion is highly variable depending on factors including, but not limited to, blood processing methods, [20][21][22][23][24] storage additive solutions, [25][26][27] and donorrelated characteristics. 22,28 At the time of donation, RCCs consist of a population of RBCs with varying biologic ages, from recently matured (young) to senescent (old).…”

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confidence: 99%

Donor‐dependent aging of young and old red blood cell subpopulations: Metabolic and functional heterogeneity

et al. 2020

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Background: Characteristics of red blood cells (RBCs) are influenced by donor variability. This study assessed quality and metabolomic variables of RBC subpopulations of varied biologic age in red blood cell concentrates (RCCs) from male and female donors to evaluate their contribution to the storage lesion. Study Design and Methods: Red blood cell concentrates from healthy male (n = 6) and female (n = 4) donors were Percoll separated into less dense ("young", Y-RCCs) and dense ("old", O-RCCs) subpopulations, which were assessed weekly for 28 days for changes in hemolysis, mean cell volume (MCV), hemoglobin concentration (MCHC), hemoglobin autofluorescence (HGB), morphology index (MI), oxygen affinity (p50), rigidity, intracellular reactive oxygen species (ROS), calcium ([Ca 2+ ]), and mass spectrometry-based metabolomics. Results: Young RCCs having disc-to-discoid morphology showed higher MCV and MI, but lower MCHC, HGB, and rigidity than O-RCCs, having discoid-tospheroid shape. By Day 14, Y-RCCs retained lower hemolysis and rigidity and higher p50 compared to O-RCCs. Donor sex analyses indicated that females had higher MCV, HGB, ROS, and [Ca 2+ ] and lower hemolysis than male RBCs, in addition to having a decreased rate of change in hemolysis by Day 28. Metabolic profiling indicated a significant sex-related signature across all groups with increased markers of high membrane lipid remodeling and antioxidant capacity in Y-RCCs, whereas O-RCCs had increased markers of oxidative stress and decreased coping capability. Conclusion: The structural, functional, and metabolic dissimilarities of Y-RCCs and O-RCCs from female and male donors demonstrate RCC heterogeneity, where RBCs from females contribute less to the storage lesion and age slower than males.

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“…Male sex consistently features as a significant donor characteristic associated with higher hemolysis levels in stored RBCs. However, comparison between studies to assess the influence of RBC variables is hampered by differences in processing procedures, blood collection/storage systems, and statistical methods for unadjusted and adjusted analyses 13,41,42 . In the RBC‐Omics study, RBCs were produced by WB filter methods and stored in AS‐1 or AS‐3 solutions, different from the SAGM‐RBCs in this study 42–44 .…”

Section: Discussionmentioning

confidence: 99%

“…While CBS produces SAGM‐RBCs by the BCRC filter and WB filter methods, there are many differences in the specific protocols compared to those used in Australia. These include different manufacturers of the blood collection/storage systems and leukoreduction filters, WB holding conditions, and centrifugation settings 13,41 . Compared to CBS, the Australian protocol has higher g ‐force centrifugation, which possibly results in less residual plasma within the RBCs.…”

Section: Discussionmentioning

confidence: 99%

Higher donor body mass index is associated with increased hemolysis of red blood cells at 42‐days of storage: A retrospective analysis of routine quality control data

Payne

Adams

2020

Transfusion

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Background For reasons unclear, some stored red blood cells (RBCs) have low hemolysis, while others have high hemolysis, which impacts quality consistency. To identify variables that influence hemolysis, routine quality control (QC) data for 42‐days‐stored RBCs with corresponding donor information were analyzed. Study Design and Methods RBC QC and donor data were obtained from a national blood supplier. Regression models and analyses were performed on total cohort stratified by donor sex and by high hemolysis (≥90th percentile) vs control (<90th percentile) samples, including matching. Results Data included 1734 leukoreduced RBCs (822 female, 912 male), processed by buffy coat–poor or whole blood filtration methods. Male RBCs had larger volume, hemoglobin content, and higher hemolysis than female RBCs (median hemolysis, 0.24% vs 0.21%; all P < .0001). Multivariable regression identified increased body mass index (BMI) and RBC variables were associated with higher hemolysis (P < .0001), along with older female age and buffy coat–poor processing method (P < .002). Logistic regression models comparing the high and control hemolysis subsets, matched for RBC component variables and processing method, identified overweight‐obese BMI (>27 kg/m2) in males remained the single donor‐related variable associated with higher hemolysis (P < .0001); odds ratio, 3 (95% confidence interval [CI], 1.3‐6.7), increasing to 4 (95% CI, 1.8‐8.6) for obese males (BMI > 30 kg/m2). Female donor obesity and older age trended toward higher hemolysis. Conclusion Donor BMI, sex, and female age influence the level of hemolysis of 42‐days‐stored RBCs. Other factors, not identified in this study, also influence the level of hemolysis.

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Not all red cell concentrate units are equivalent: international survey of processing and in vitro quality data

Cited by 16 publications

References 23 publications

Objective assessment of stored blood quality by deep learning

Objective assessment of stored blood quality by deep learning

Donor‐dependent aging of young and old red blood cell subpopulations: Metabolic and functional heterogeneity

Higher donor body mass index is associated with increased hemolysis of red blood cells at 42‐days of storage: A retrospective analysis of routine quality control data

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