Mechanical hemolysis is a major concern in the design of cardiovascular devices, such as prosthetic heart valves and ventricular assist devices. The primary cause of mechanical hemolysis is the impact of the device on the local blood flow, which exposes blood elements to non-physiologic conditions. The majority of existing hemolysis models correlate red blood cell (RBC) damage to the imposed fluid shear stress and exposure time. Only recently more realistic, strain-based models have been proposed, where the RBC's response to the imposed hydrodynamic loading is accounted for. In the present work we extend strain-based models by introducing a high-fidelity representation of RBCs, which is based on existing coarse-grained particle dynamics approach. We report a series of numerical experiments in simple shear flows of increasing complexity, to illuminate the basic differences between existing models and establish their accuracy in comparison to the high-fidelity RBC approach. We also consider a practical configuration, where the flow through an artificial heart valve is computed. Our results shed light on the strengths and weaknesses of each approach and identify the key gaps that should be addressed in the development of new models.

Our inter-regional model considered complex factors and can be a valuable tool to assist regulatory decision-making and strategic planning for emergency preparedness to avoid and mitigate associated adverse health consequences. (Disaster Med Public Health Preparedness. 2018;12:201-210).

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Background Transfusion‐related adverse events can be unrecognized and unreported. As part of the US Food and Drug Administration's Center for Biologics Evaluation and Research Biologics Effectiveness and Safety initiative, we explored whether machine learning methods, such as natural language processing (NLP), can identify and report transfusion allergic reactions (ARs) from electronic health records (EHRs). Study Design and Methods In a 4‐year period, all 146 reported transfusion ARs were pulled from a database of 86,764 transfusions in an academic health system, along with a random sample of 605 transfusions without reported ARs. Structured and unstructured EHR data were retrieved, including demographics, new symptoms, medications, and lab results. In unstructured data, evidence from clinicians' notes, test results, and prescriptions fields identified transfusion ARs, which were used to extract NLP features. Clinician reviews of selected validation cases assessed and confirmed model performance. Results Clinician reviews of selected validation cases yielded a sensitivity of 67.9% and a specificity of 97.5% at a threshold of 0.9, with a positive predictive value (PPV) of 84%, estimated to 4.5% when extrapolated to match transfusion AR incidence in the full transfusion dataset. A higher threshold achieved sensitivity of 43% with specificity/PPV of 100% in our validation set. Essential features predicting ARs were recognized transfusion reactions, administration of antihistamines or glucocorticoids, and skin symptoms (e.g., hives and itching). Removal of NLP features decreased model performance. Discussion NLP algorithms can identify transfusion reactions from the EHR with a reasonable level of precision for subsequent clinician review and confirmation.