Seheult, Jansen

Abstract Hemorrhagic shock is a common emergency that accounts for more than 10% of global mortality and up to 40% of trauma-related mortality. The Advanced Trauma Life Support (ATLS) guidelines outline resuscitation strategies in patients with massive hemorrhage based on clinical trial and observational data. Early intervention with fluids and/or blood products is recommended during resuscitation as key to maintaining vascular patency, stabilizing a patient’s blood pressure, maintaining tissue oxygenation, and limiting shock. From a transfusion perspective, there is wide heterogeneity in the quality of blood products due to donor and manufacturing variation. The optimal transfusion strategy for massive hemorrhage remains unclear. In silico models of resuscitation may offer a means to evaluate the effectiveness and safety of various resuscitation protocols. Here we describe a stochastic multicompartment model of fluid balance and resuscitation that includes (i) cardiovascular hemodynamics, (ii) body fluid compartments and capillary solute exchange, (iii) the ability to alter hemorrhage, resuscitation, and hemostasis parameters, and (iv) tissue oxygenation and capillary-alveolar gas exchange. Building upon deterministic frameworks, this stochastic model more faithfully reflects the clinical heterogeneity of bleeding patients at a level I trauma center. This allows for a proof of concept in silico trial comparing crystalloids, conventional component therapy (CCT), i.e. red cell, platelet and plasma components, to cold-stored low titer group O whole blood (LTOWB). In an in silico cohort of ATLS class III (>30% and ≤40% blood volume lost) hemorrhage, LTOWB resuscitation reduced the time spent in the critical hemostatic window (platelet count <50x109/L, INR≥2, hemoglobin (Hgb) <8 g/dL and fibrinogen <150 mg/dL) compared with CCT (90.59 vs. 147.62 minutes; p=0.04), with no difference in predicted event-free survival (t = 240 minutes; cardiac < 1.5, fluid overload > 10% or SBP > 150% of the starting SBP, or Hgb < 3.0 g/dL). In a separate cohort of ATLS class IV (>40% blood volume lost) hemorrhage, LTOWB yielded a higher predicted event-free survival (74%) versus the CCT arm (69%, p < 0.01). We demonstrate how this in silico platform can function as a digital twin for hemorrhagic trauma enabling precision transfusion strategies, and in parallel, an operational twin to support blood banks to forecast blood product demand and inform massive transfusion protocols and clinical trial design.

Abstract Background The lupus anticoagulant (LAC) is an important laboratory criterion in the diagnosis of antiphospholipid antibody syndrome. LAC testing at the Mayo Clinic Special Coagulation Laboratory includes up to 13 tests, which are interpreted by trained physicians to identify the presence of LAC and rule out anticoagulant interferences. This feasibility study explored the use of two deep neural network (DNN) architectures for multilabel classification of LAC profiles as a first step toward automating interpretation. Methods Seven thousand two hundred and two retrospective cases were randomly split (64:16:20) for training, validation, and test, respectively. LAC positivity by dilute Russell's viper venom time (LAC-DRVVT) and activated partial thromboplastin time (LAC-APTT) and the presence of warfarin (WAR) and heparin (HEP) were adjudicated by one expert. DNN architectures included: single-output DNNs using domain-knowledge input feature selection and a single-column multioutput DNN using all 13 inputs. Results Domain-knowledge-naïve multioutput DNN achieved similar or improved performance for all 4 label prediction tasks compared with domain knowledge optimized DNNs: F1 scores of 0.977 vs 0.968 for LAC-DRVVT, 0.954 vs 0.945 for LAC-APTT, 0.961 vs 0.957 for HEP, and 0.995 vs 0.977 for WAR, respectively. Conclusions The comparable performance of the 4 domain knowledge optimized DNNs and the multioutput DNN using all 13 input features suggests that the DNN may learn feature importance or mapping to a task without explicit input selection. Given its relative simplicity and versatility, the multioutput DNN is the preferred choice for implementation in a clinical laboratory to standardize LAC diagnosis.

Abstract Background and Objectives Artificial intelligence (AI) has been gaining increasing interest in healthcare. During the 2024 International Society of Blood Transfusion (ISBT) Congress, the Clinical Transfusion Working Party (CTWP) conducted a session to explore the exciting intersection of AI in transfusion medicine (TM) practice, education and research. We report here the potential applications and the session outcome. Materials and Methods A pre‐workshop survey explored the participants' demographics and areas of use of AI and whether they have had any AI‐specific training or education. The workshop included presentations on the regulatory aspects of AI use and its application in TM practice, education and research. These were followed by round‐table discussions to explore participants' experience and concerns. Results The workshop had 72 attendees, with 38% falling in the 36–45‐year age group. A total of 70% indicated the use of AI, but only 12% reported having specific training or education. Participants expressed interest in different potential applications but also shared concerns on over‐reliance, potential loss of skills, the accuracy of provided information and content plagiarism. Conclusion The findings of the workshop highlight the need for training, educational resources, standards and regulatory frameworks to guide the use of AI tools in the field of TM.

Context.— Generative artificial intelligence (GAI) is a promising new technology with the potential to transform communication and workflows in health care and pathology. Although new technologies offer advantages, they also come with risks that users, particularly early adopters, must recognize. Given the fast pace of GAI developments, pathologists may find it challenging to stay current with the terminology, technical underpinnings, and latest advancements. Building this knowledge base will enable pathologists to grasp the potential risks and impacts that GAI may have on the future practice of pathology. Objective.— To present key elements of GAI development, evaluation, and implementation in a way that is accessible to pathologists and relevant to laboratory applications. Data Sources.— Information was gathered from recent studies and reviews from PubMed and arXiv. Conclusions.— GAI offers many potential benefits for practicing pathologists. However, the use of GAI in clinical practice requires rigorous oversight and continuous refinement to fully realize its potential and mitigate inherent risks. The performance of GAI is highly dependent on the quality and diversity of the training and fine-tuning data, which can also propagate biases if not carefully managed. Ethical concerns, particularly regarding patient privacy and autonomy, must be addressed to ensure responsible use. By harnessing these emergent technologies, pathologists will be well placed to continue forward as leaders in diagnostic medicine.

Context.— Generative artificial intelligence (GAI) technologies are likely to dramatically impact health care workflows in clinical pathology (CP). Applications in CP include education, data mining, decision support, result summaries, and patient trend assessments. Objective.— To review use cases of GAI in CP, with a particular focus on large language models. Specific examples are provided for the applications of GAI in the subspecialties of clinical chemistry, microbiology, hematopathology, and molecular diagnostics. Additionally, the review addresses potential pitfalls of GAI paradigms. Data Sources.— Current literature on GAI in health care was reviewed broadly. The use case scenarios for each CP subspecialty review common data sources generated in each subspecialty. The potential for utilization of CP data in the GAI context was subsequently assessed, focusing on issues such as future reporting paradigms, impact on quality metrics, and potential for translational research activities. Conclusions.— GAI is a powerful tool with the potential to revolutionize health care for patients and practitioners alike. However, GAI must be implemented with much caution considering various shortcomings of the technology such as biases, hallucinations, practical challenges of implementing GAI in existing CP workflows, and end-user acceptance. Human-in-the-loop models of GAI implementation have the potential to revolutionize CP by delivering deeper, meaningful insights into patient outcomes both at an individual and a population level.

AbstractBackgroundClot based assays used for lupus anticoagulant (LAC) detection are typically interpreted in a qualitative fashion and may not reflect LAC potency. In this cross‐sectional study, we describe a method for quantifying the LAC titer using serial (dependent) two‐fold dilutions in normal pooled plasma.MethodsSerial dilutions of 51 residual plasma samples from 50 patients were tested using the Russell's viper venom screening time (DRVVT) and activated partial thromboplastin screening time (APTT) methodologies. The measured clotting times and the corresponding dilution factors were then used to derive a four‐parameter logistic model. The LAC titer for each patient was interpolated as the sample dilution that corresponds to the upper reference interval limit of the corresponding assay.ResultsCalculated APTT and DRVVT LAC titers displayed a strong linear correlation (R2 = 0.84) between each other, but not with the degree of prolongation of the APTT/DRVVT screening time in the neat undiluted samples. Using data driven partitioning, patients could be grouped into low (<10) or high (≥10) DRVVT LAC titer. There were no significant differences in anticardiolipin (aCL) or anti‐beta 2 glycoprotein 1 (aB2GPI) antibody levels or prevalence of thromboembolic events between low and high LAC titer groups. In contrast, antiphosphatidylserine/prothrombin (aPS/PT) IgM antibody levels, but not IgG, were significantly higher in the high LAC titer group.ConclusionsThe degree of prolongation of the APTT/DRVVT screening time is not correlated with the LAC titer. Only aPS/PT IgM antibodies levels were strongly correlated with the LAC titers. Additional studies are warranted to determine clinical implications of high LAC titers.

AbstractVon Willebrand factor (VWF) is a multimeric glycoprotein with essential roles in primary hemostasis. Patients with von Willebrand disease (VWD), due to quantitative and/or qualitative defects of VWF usually experience mucocutaneous bleeding. Based on the laboratory results of VWF antigen, various VWF activities, factor VIII activity, and VWF multimer patterns, VWD can be categorized as type 1, 2, and 3 VWD. VWF multimer analysis by either manual or semi-automated electrophoresis and immunoblotting is a critical part of the laboratory testing to differentiate type 1, type 2 VWD, and subtypes of type 1 or 2 VWD. The multimer distribution patterns can also help to understand the underlying molecular mechanism of VWF synthesis, multimerization, and clearance defects in VWD. This review will cover VWF synthesis, multimerization, secretion, VWF multimer analysis, and VWF multimer interpretation of various types and subtypes of VWD.

Context.— Biomedical terminologies such as Logical Observation Identifiers, Names, and Codes (LOINC) were developed to enable interoperability of health care data between disparate health information systems to improve patient outcomes, public health, and research activities. Objective.— To ascertain the utilization rate and accuracy of LOINC terminology mapping to 10 commonly ordered tests by participants of the College of American Pathologists (CAP) Proficiency Testing program. Design.— Questionnaires were sent to 1916 US and Canadian laboratories participating in the 2018 CAP coagulation (CGL) and/or cardiac markers (CRT) surveys requesting information on practice setting, instrument(s) and test method(s), and LOINC code selection and usage in the laboratory and electronic health records. Results.— Ninety of 1916 CGL and/or CRT participants (4.7%) responded to the questionnaire. Of the 275 LOINC codes reported, 54 (19.6%) were incorrect: 2 codes (5934-2 and 12345-1) (0.7%) did not exist in the LOINC database and the highest error rates were observed in the property (27 of 275, 9.8%), system (27 of 275, 9.8%), and component (22 of 275, 8.0%) LOINC axes. Errors in LOINC code selection included selection of the incorrect component (eg, activated clotting time instead of activated partial thromboplastin time); selection of panels that can never be used to obtain an individual analyte (eg, prothrombin time panel instead of international normalized ratio); and selection of an incorrect specimen type. Conclusions.— These findings of real-world LOINC code implementation across a spectrum of laboratory settings should raise concern about the reliability and utility of using LOINC for clinical research or to aggregate data.