Feasibility of photoacoustic imaging for the non-invasive quality management of stored blood bags

The oxygen saturation of red blood cell concentrates: The basis for a novel index of red cell oxidative stress

BACKGROUND

Oxidative stress is a major driving force in the development of storage lesions in red cell concentrates (RCCs). Unlike manufactured pharmaceuticals, differences in component preparation methods and genetic/physiological status of donors result in nonuniform biochemical characteristics of RCCs. Various characteristics of donated blood on oxygen saturation (SO₂ ) distribution were investigated, and a model to estimate potential oxidative stress burden of stored RCC at transfusion is proposed.

STUDY DESIGN AND METHODS

The oxygen content of freshly prepared RCCs (770) was quantified noninvasively as fractional hemoglobin saturation (SO₂ ) with visible reflectance spectrometry. Using separate RCCs and mimicking typical handling of RCCs during routine storage, evolution of SO₂ was followed for construction of an empirical model. Based on this model, the oxygen exposure index (OEI) was formulated to estimate the accumulated oxygen exposure burden of RCC at the time of transfusion.

RESULTS

The SO₂ of RCCs varied widely at donation (mean 43% ± 1.3%; range 20%-93%). Multivariate regression model showed that sex and processing method had small effects on SO₂ (R²  = 0.12), indicating that variability was mainly attributed to other individual donor characteristics. Storage simulation model indicated that median SO₂ increased gradually over 6 weeks (approx. 1.3 fold), while OEI increased at a faster rate (approx. eight-fold).

CONCLUSION

In addition to storage age, the OEI provides a potential new metric to assess the quality of RCCs at the time of transfusion in terms of their oxidative stress. In future studies, a single noninvasive measurement during storage could link OEI to clinical outcomes in transfusion recipients.

In vivo photoacoustic assessment of the oxygen saturation changes in the human radial artery: a preliminary study associated with age

SIGNIFICANCE

We demonstrate the potential of probing the sO2 change under blood flow in vivo using photoacoustic (PA) imaging and sheds light on the complex relationship between RBC aggregation and oxygen delivery.

AIM

To conduct in vivo assessments of the sO2 in the radial artery of healthy volunteers and simultaneously probe the relation between the sO2 and hemodynamic behavior such as red blood cell (RBC) aggregation.

APPROACH

The effects of PA-based measurements of blood hemodynamics were studied as a function of the subjects' age (20s, 30s, and 40s). The pulsatile blood flow in the human radial artery of 12 healthy subjects was imaged in the 700 to 900 nm optical wavelength range using a linear array-based PA system.

RESULTS

The PA power when blood velocity is minimum (Pamax) was larger than the one attained at maximum blood velocity (Pamin), consistent with predictions based on the cyclical variation of RBC aggregation during pulsatile flow. The difference between Pamin and Pamax at 800 nm (ΔPa800) increased with age (1.7, 2.2, and 2.6 dB for age group of 20s, 30s, and 40s, respectively). The sO2 computed from Pamax was larger than the one from Pamin.

CONCLUSIONS

The ΔPa800 increased with participant age. The ΔPa800 metric could be a surrogate of noninvasively monitoring the age-induced changes in RBC aggregation. The sO2 change during a cycle of pulsatile blood flow also increased with age, demonstrating that RBC aggregation can affect the sO2 change.

Emerging use of machine learning and advanced technologies to assess red cell quality

Improving blood product quality and patient outcomes is an accepted goal in transfusion medicine research. Thus, there is an urgent need to understand the potential adverse effects on red blood cells (RBCs) during pre-transfusion storage. Current assessment techniques of these degradation events, termed "storage lesions", are subjective, labor-intensive, and complex. Here we describe emerging technologies that assess the biochemical, biophysical, and morphological characteristics of RBC storage lesions. Of these emerging techniques, machine learning (ML) has shown potential to overcome the limitations of conventional RBC assessment methods. Our previous work has shown that neural networks can extract chronological progressions of morphological changes in RBCs during storage without human input. We hypothesize that, with broader training and testing of multivariate data (e.g., varying donor factors and manufacturing methods), ML can further our understanding of clinical transfusion outcomes in multiple patient groups.