Deepa Jayakody Arachchillage

Summary: Inflammation contributes to primary antiphospholipid syndrome (PAPS) pathogenesis, but cytokine profiles and associated thrombotic phenotypes remain incompletely defined. A panel of 20 cytokines, chemokines and endothelial activation markers was analysed in 37 PAPS patients on stable anticoagulation and 12 healthy controls (HC). We compared concentrations between groups and performed unsupervised hierarchical clustering and principal component analysis (PCA) to identify inflammatory subgroups. Compared with HC, patients with PAPS exhibited significantly elevated levels of 14/20 analytes: granulocyte‐macrophage colony‐stimulating factor (GM‐CSF), interferon (IFN)‐γ, interleukin (IL)‐1α, IL‐1β, IL‐6, IL‐10, IL‐12p70, IL‐13, IL‐17a, tumour necrosis factor‐α (TNF‐α), intercellular adhesion molecule‐1 (ICAM‐1), P‐selectin and macrophage inflammatory protein‐1β (MIP1β) (all p < 0.05). Unsupervised clustering identified three inflammatory antiphospholipid syndrome (APS) subgroups distinct from HC. A core cytokine signature comprising GM‐CSF, IL‐6, IL‐10, IL‐12p70, IL‐13 and TNF‐α was elevated across all APS clusters. One cluster, enriched for arterial thrombosis, demonstrated a more pronounced inflammatory phenotype, with increased IFN‐α. Comparison of phenotypes showed significantly higher concentrations of GM‐CSF, IFN‐α, IL‐1α, IL‐4, IL‐6, IL‐10, IL‐17a and TNF‐α in arterial versus venous thrombosis. Our findings demonstrate widespread cytokine dysregulation and distinct inflammatory subgroups in PAPS, with arterial thrombosis showing stronger inflammation than venous thrombosis, supporting divergent thrombo‐inflammatory mechanisms and potential precision immunomodulatory therapeutic targets.

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.

Antiphospholipid syndrome (APS) is an acquired autoimmune disease characterized by the presence of thrombosis and/or pregnancy morbidity with persistently positive antiphospholipid antibodies (aPL). Due to the heterogeneity and relative rarity of this disorder, clinical practice in the management of APS remains varied. In this retrospective UK-wide multicenter study, we aimed to delineate clinical practice and outcomes in thrombotic APS to improve the areas of limited knowledge, particularly anticoagulant practice, thrombotic recurrence, and bleeding. Anticoagulation in Antiphospholipid Syndrome is the largest multicenter observational study to date, spanning 20 National Health Service trusts in the UK, and including 500 adult patients (aged ≥18 years) with thrombotic APS, on or off anticoagulation during 2012-2021. Thrombotic APS is primarily treated with vitamin K antagonists; however, the rates of recurrent thrombosis remain high. In the last decade, recurrent thrombosis occurred in 43 of 125 (34.4%), 31 of 95 (32.6%), and 37 of 93 (39.8%) patients with single-, dual-, and triple-positive aPL, respectively, with a recurrence rate of 46%. There was no difference in the probability of recurrent thrombosis based on the number of positive aPL tests (P = .82), especially in the first 3 years. However, the probability of recurrent thrombosis was significantly higher in patients with lupus anticoagulant (P< .01) compared with the presence of other antibodies. There was a higher probability of recurrence in patients with arterial than venous thrombosis (P = .03). Overall, the 10-year bleeding rate was 22.0%, with 6.7% of patients having major bleeding. Identifying patients with APS at higher risk of recurrent thrombosis remains a challenge, and current risk stratification is not adequate.

Direct oral anticoagulants (DOACs), including direct thrombin inhibitors (dabigatran) and direct factor Xa inhibitors (apixaban, rivaroxaban, edoxaban), have transformed anticoagulant management in recent years due to their predictable pharmacodynamics, rapid onset of action, and fixed dosing without the need for routine laboratory monitoring. Unfractionated heparin (UFH) remains the anticoagulant of choice for patients who are acutely unwell and treated in intensive care units due to its short half-life, reversibility, ease of dose titration, and nonrenal dependent excretion. It is therefore not uncommon for an individual's anticoagulation management to require rapid changing from DOAC to UFH. Due to UFH's complex pharmacokinetics, including nonspecific binding to acute phase proteins and dose-dependent clearance, careful laboratory monitoring, generally with activated partial thromboplastin time (APTT) or anti-factor Xa (anti-Xa) activity, is necessary. When transitioning from a DOAC to UFH, overlapping pharmacologic effects can significantly interfere with coagulation assays, particularly if residual DOAC levels persist at the time UFH is initiated. DOACs can prolong the APTT and elevate anti-Xa activity, leading to overestimation of UFH activity, inappropriate dose adjustments, and increased risk of bleeding or thromboembolic events. Here, we examine the laboratory implications of transitioning from DOAC therapy to UFH, with a focus on the performance and interpretation of APTT and anti-Xa assays in the presence of residual DOAC levels and how to overcome the interference of DOAC in UFH monitoring. We suggest an algorithm to follow during this transition.