ABO blood group influences plasma levels of von Willebrand factor (VWF) and factor VIII (FVIII), with non-O individuals having higher levels and increased thrombotic risk. The relationship between ABO group and global hemostasis has not been studied in Greece. This study aims to examine the association between ABO blood group and VWF/FVIII levels in healthy Greek adults, and to assess whether ABO-related differences affect global hemostasis by viscoelastic methods. It is a cross-sectional study of 179 healthy blood donors (86 group O, 93 non-O). VWF:Ag, VWF:GPIbM, and FVIII:C were measured. Platelet function was assessed by Platelet Function Analyzer-100 (Col/Epi), and global hemostasis by ROTEM EXTEM. Group O had significantly lower VWF:Ag, VWF:GPIbM, and FVIII than non-O (all p < 0.001). No differences were observed between groups in Platelet Function Analyzer-100 or any ROTEM parameter. Platelet count and fibrinogen, not VWF, drove ROTEM parameters. ABO blood group significantly affects VWF and FVIII levels, but global hemostasis remains uncompromised in healthy individuals. However, as group O has been reported to act as a disease modifier in patients with underlying hemostatic defects, these findings highlight that no single assay can safely capture clinical hemostatic risk, and ABO blood group should be interpreted in clinical context.
Global coagulation assays provide a comprehensive assessment of clotting and fibrinolytic dynamics and have shown potential in predicting bleeding and thrombotic risk. Successful clinical implementation, however, requires understanding of normal age-specific changes, which remains limited. This study aims to evaluate age- and sex-related differences in global coagulation assays from neonates to older adults. A total of 546 citrated plasma samples from 380 healthy pediatric participants (0 to < 18 years) and 166 adults (20-79 years, without known cardiovascular risk factors or medications affecting coagulation) were tested for thrombin generation (CAT-TG), modified overall hemostatic potential (mOHP), and plasmin generation (PG). This study demonstrated that hemostatic capacity was lowest at birth and increased rapidly throughout early childhood, with endogenous thrombin and plasmin potentials peaking in the fourth decade of life. Fibrin generation continued to rise, peaking in the sixth decade, accompanied by a decline in fibrinolytic capacity. Sex differences showed a hypercoagulable shift in females beginning at puberty, peaking during the reproductive years. These sex differences attenuated post-menopause, reflecting the convergence of cardiovascular risk in older age. These findings provide essential normative data to support age- and sex-adjusted interpretation of global coagulation assays. Overall, the most pronounced hemostatic changes occur in early life, consistent with developmental hemostasis. The midlife peak in procoagulant parameters and the decline in fibrinolytic capacity highlight progressive hemostatic changes associated with aging. While sex-related differences underscore the role of endogenous hormones in modulating coagulation, longitudinal studies in healthy and at-risk individuals are warranted to clarify trajectories and inform personalized interventions.
The hemostatic derangement of neonates with perinatal hypoxia is of multifactorial origin and may be attributed to ischemic tissue injury, acute blood loss, or disseminated intravascular coagulation. However, the pathophysiological mechanisms of thrombocytopenia, a common hematological finding in neonates with perinatal hypoxia, remain unclear. The aim of the present study was to evaluate the contribution of platelets to clot formation and clot strength in neonates with perinatal hypoxia using rotational thromboelastometry (ROTEM). This prospective cohort study included 139 neonates with perinatal hypoxia and 106 healthy neonates. Demographics and clinical findings were recorded, while hematological and biochemical tests, along with ROTEM, extrinsically activated test (EXTEM), and fibrin clot obtained by platelet inhibition with cytochalasin D (FIBTEM) assays, were performed in all study neonates. ROTEM parameters differed significantly between the two study groups; prolonged clotting time and clot formation time, lower clot amplitude at 10 minutes (A10) and maximum clot firmness (MCF) values, as well as lower PLTEM MCF (the difference between EXTEM MCF and FIBTEM MCF) and PLTEM MCE (the difference between EXTEM MCE and FIBTEM MCE) values were recorded in hypoxic neonates. Neonates with perinatal hypoxia demonstrate a hypocoagulable profile, with decelerated coagulation activation, reduced clot formation and stability, impaired fibrinolysis, and significantly reduced platelet contribution to clot dynamics.
Abstract Primary thromboprophylaxis for arterial and venous thromboembolism (VTE) still presents many challenges, as patient- and disease-specific risk factors complicate risk assessment and necessitate tailored strategies. To tackle this, multiple thromboembolism risk assessment models have been developed and validated, with machine learning (ML) models now emerging. Some ML models have been integrated into electronic health record (EHR)-based clinical decision support systems (CDSS) to improve guideline adherence and optimize appropriate anticoagulant use. We conducted a narrative review exploring the intersection of ML and clinical decision support (CDS) in primary thromboprophylaxis for VTE and stroke prevention in atrial fibrillation. Traditional risk assessment models integrated into EHR-based CDS have improved thromboprophylaxis rates and, in some studies, reduced thrombotic events. More comprehensive CDS strategies, such as the deployment of an EHR-agnostic CDS incorporating the IMPROVE-DD score for venous thromboprophylaxis, have demonstrated broader clinical impact, with reductions in both venous and arterial thromboembolism. With advances in artificial intelligence, ML-based risk prediction models for both venous and arterial thromboembolism have demonstrated superior discriminative performance compared with traditional models, predominantly in internal validation studies. Furthermore, their integration into active CDSS represents a promising strategy to improve thromboprophylaxis. However, most ML models lack external validation, remaining susceptible to overfitting and limiting generalizability. ML models integrated into active CDS hold much promise given their enhanced predictive performance, ability to integrate high-dimensional data and capture complex nonlinear interactions. Nevertheless, further work is needed to establish clinical utility through prospective outcome studies, ensure generalizability via external validation, and achieve integration into real clinical workflows.
Abstract:Clinical prediction scores have been central to thrombosis and hemostasis practice for decades, providing transparent, interpretable, and validated approaches to risk assessment. Increasingly, artificial intelligence (AI)-based predictive models are being developed to improve risk stratification by integrating large, complex datasets and identifying patterns beyond conventional statistical models. However, improved predictive performance alone does not determine clinical value, and AI introduces new ethical, regulatory, and governance challenges. This narrative review compares traditional thrombosis risk scores and AI-based predictive models, focusing on methodological differences, ethical considerations, regulatory requirements, and the competencies clinicians require for responsible implementation. Traditional scores benefit from simplicity, transparency, and ease of clinical interpretation but have important limitations, including modest predictive performance, limited adaptability, potential bias, and uncertain evidence that score-guided management improves outcomes compared with clinical judgment alone. Many of the concerns currently associated with AI-based risk prediction, including bias, generalizability, validation, and monitoring, also exist within traditional prediction models but have historically received less regulatory attention. AI-based models offer potential advantages through dynamic risk prediction, integration of multimodal data, and improved predictive accuracy; however, they introduce additional challenges related to explainability, data governance, privacy, cybersecurity, accountability, reproducibility, and postdevelopment surveillance. AI should be viewed as an evolution of clinical decision support rather than a replacement for established approaches or clinical expertise. Safe implementation requires robust validation, continuous monitoring, human oversight, transparent governance frameworks, and clinician AI literacy.
