Citations

Background: Splanchnic venous thrombosis (SVT) is a potentially severe and frequently underdiagnosed complication following splenectomy in patients with hematologic diseases. Despite its clinical importance, no standardized, evidence-based guidelines currently exist to inform perioperative prevention, diagnosis, or treatment in this population. Materials and Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines to estimate the incidence and risk factors for SVT after splenectomy in patients with hematologic diseases. Twenty-eight observational studies comprising 5025 patients were included. Risk factor data were analyzed using random-effects models. In parallel, an international Delphi consensus process was performed involving 35 experts (hematologists and surgeons) from 14 countries. Experts were assigned to five domain-specific groups and developed clinical statements on prophylaxis, diagnosis, and management of SVT. Consensus was defined as ≥80% agreement, and recommendations were graded using the GRADE framework. Results: The pooled incidence of SVT after splenectomy was 11.4%, with higher rates in studies using routine imaging (20.7%) and those conducted at single centers (15.1%). Splenic size and weight were significant risk factors, while age, BMI, and platelet count were not. Disease-specific risk was highest in patients with myeloproliferative neoplasms, lymphoma, and thalassemia. The Delphi process generated 25 consensus statements, 14 of which were classified as strong recommendations. Key guidance included perioperative vaccination, individualized thromboprophylaxis with BMI-adjusted low molecular weight heparin, selective Doppler-based screening in high-risk patients, and stepwise anticoagulation regimens based on thrombus burden and clinical evolution. Conclusion: This study presents the first international consensus-based guidelines for managing SVT after splenectomy in patients with hematologic disease. Developed through systematic evidence synthesis and expert consensus, these recommendations provide a practical framework rather than definitive standards of care and support individualized prevention, detection, and treatment strategies in this high-risk population.

Abstract Systems-based hematology (SBH) is an emerging discipline focused on optimizing care delivery for patients with hematologic disorders within complex health care systems. Despite its growing importance, the field lacks a formal definition, standardized curriculum, and clear career pathway, hindering widespread adoption and training of future SBH experts. We sought to define core principles, competencies, challenges, and future directions of SBH to identify foundational components for a potential educational curriculum. Semistructured interviews were conducted with 8 SBH experts. Expert participants were identified through their committee involvement with the American Society of Hematology. Five overarching themes emerged: (1) The accidental expert: SBH careers have developed organically and reactively in response to pressing institutional needs, not through formal training; (2) Defining the unseen: experts define the field pragmatically by its function and roles; (3) The SBH toolkit: success requires a hybrid skillset combining clinical hematology expertise with health systems science (HSS) principles; (4) The architect's dilemma: the field's growth is hindered by interdependent challenges of building a curriculum, awareness, and securing resources; and (5) Charting the future: the discipline is poised for growth, driven by increasing health care complexity and specialization. SBH represents the nexus of HSS principles and hematology care. Formalizing SBH requires a multipronged approach, including developing a standardized curriculum that equips learners with a hybrid skillset, creating clear value for health systems and patients, and fostering a community of practice through professional societies. These findings provide a foundational framework for developing educational resources and institutional support necessary to continue advancing SBH.

Abstract Background Despite advances in the diagnosis and treatment of immune thrombotic thrombocytopenic purpura (iTTP), recent mortality estimates remain around 5–10%. Predicting iTTP mortality risk has been of interest, as it may help individualize treatment or potentially guide the use of more intensive or novel therapies based on disease severity. However, prior attempts at mortality prediction have yielded inconsistent results, and no individual variable has demonstrated independent or reliable predictive ability. In a previous analysis utilizing the USTMA iTTP registry, which included 419 patients from 15 TTP referral centers, we reported that mortality due to immune TTP was unpredictable (Blood. 2021;138[Suppl 1]:1047). Neither the French Thrombotic Microangiopathy (TMA) Reference Score (previously validated in the French TMA Registry), nor a previous logistic regression model attempt (developed by USTMA investigators in 2021) demonstrated sufficient accuracy in predicting iTTP mortality in the USTMA dataset (AUC = 0.63 and 0.69, respectively). We subsequently utilized gradient boosted machine, a machine learning approach capable of handling multiple continuous variables and the complex interactions between them in large datasets, and developed a novel prediction tool: the USTMA Mortality Index (Res Pract Thromb Haemost. 2024; 8(3):102388). It demonstrated good performance in predicting iTTP mortality (AUC = 0.77) in repeated 10-fold cross-validation. However, it has not yet undergone external validation. Objectives To externally validate the USTMA Mortality Index to predict mortality due to acute iTTP in a multicenter cohort. Methods We performed an external validation using an independent cohort of iTTP patients from 5 institutions: University of Utah, Case Western Reserve University, Rochester Regional Health, University of Illinois, and Mayo Clinic. Patients in the validation cohort were not part of the development cohort (the USTMA registry). We included patients with confirmed iTTP and defined acute iTTP mortality as death within 30 days of admission. USTMA Mortality Index variables were collected at initial presentation, which included (in order of highest to lowest relative influence): lactate dehydrogenase, serum creatinine, age, hemoglobin, platelet count, presence of stupor/coma, fever. The model's prediction accuracy was assessed using area under the receiver operating characteristic curve (AUC). We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) based on the previously defined cut-offs of 4.27% and 9.08% mortality risk in the original model, which categorized patients into standard risk, high risk, and very high risk of predicted mortality. Results The external validation cohort included 103 patients with confirmed iTTP and 11 deaths (10.7% mortality rate). The USTMA Mortality Index demonstrated poor performance with AUC of 0.69 (95% CI, 0.55–0.83) in the validation cohort. The wide confidence interval reflects substantial uncertainty in model performance, ranging from poor to excellent, and is likely due to the small number of deaths. At a risk threshold ≥4.27%:Sensitivity = 64%Specificity = 70%PPV = 20%NPV = 94% At a risk threshold ≥9.08%:Sensitivity = 18%Specificity = 87%PPV = 14%NPV = 90% Conclusion The USTMA Mortality Index demonstrated poor performance in the external validation cohort (AUC = 0.69, 95% CI, 0.55–0.83), contrasting with its previously favorable performance in repeated 10-fold cross-validation. This discrepancy likely reflects model overfitting, a known challenge in AI-based clinical modeling. Our study is limited by the small number of deaths, resulting in a wide confidence interval ranging from poor to excellent performance. Given that neither the French TMA Reference Score, nor our group's prior logistic regression model (2021), nor the USTMA Mortality Index (2024) could reliably predict mortality upon external validation in our cohorts, these findings collectively suggest that mortality in acute iTTP remains difficult to predict at this time, regardless of statistical methodology or current machine learning capabilities. Clinical decision-making, including the use of therapies such as caplacizumab, should not be guided solely by mortality risk predictions. Instead, efforts to reduce mortality and improve outcomes should prioritize individualized clinical assessment over model-based stratification at this time.

Abstract Background Due to the high mortality of immune thrombotic thrombocytopenic purpura (iTTP), rapid clinical evaluation and early intervention are crucial. The PLASMIC score remains a valuable and widely used tool for predicting severe ADAMTS13 deficiency. However, recent studies have shown variable performance across different populations. In addition, its variables may often be missing during initial presentation. Refining the accuracy of early iTTP evaluation could improve timely recognition and treatment, while reducing unnecessary interventions in those eventually ruled out for iTTP. Machine learning advancements offer several advantages for enhancing clinical prediction models: the ability to assess continuous variables, capture complex interactions between them, and handle missing data. We previously utilized XGBoost (extreme gradient boosting) to develop a novel machine learning tool, TTP-14, which improved upon the PLASMIC score's predictive performance in repeat 10-fold cross-validation achieving an AUC of 0.91 (Blood. 2024;144[Suppl 1]:2617). TTP-14 generates a predicted probability for iTTP, expressed as a continuous percentage estimate of iTTP diagnostic likelihood, and is also able to do so with missing variables. However, it has not previously been externally validated. Objectives To externally validate the ability of TTP-14 to predict iTTP diagnosis and improve upon the PLASMIC score. Methods We performed an external validation of TTP-14 using an independent cohort of patients with suspected iTTP at the Mayo Clinic. These patients were not part of the original multi-center development cohort of 106 iTTP patients from the University of Utah, Case Western Reserve University, Rochester Regional Health, and University of Illinois. For the validation cohort, we identified patients who underwent ADAMTS13 activity testing between 2010 and 2025 for evaluation of thrombotic microangiopathy at the Mayo Clinic. iTTP was defined as ADAMTS13 activity <10%, and controls as ADAMTS13 activity >20%. TTP-14 variables were collected at initial presentation: platelet count, lactate dehydrogenase, creatinine, international normalized ratio, reticulocyte percentage, active cancer, indirect bilirubin, mean corpuscular volume, total bilirubin, haptoglobin, neurologic deficit, hemoglobin, age, and history of transplant. Receiver operating characteristic curve (AUC) was reported with 95% confidence intervals (CI). While the model provides a continuous estimate and is not categorical, we evaluated clinically meaningful probability thresholds to illustrate performance in decision-relevant zones. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were reported at these select thresholds and analyzed for patients with and without missing variables. Result We included 50 iTTP patients and 56 controls in the external validation cohort. 13 patients had missing PLASMIC variables. TTP-14 achieved an AUC of 0.96 (95% CI: 0.93–0.99), outperforming the PLASMIC score's AUC of 0.90 (95% CI: 0.83–0.96) in the same dataset, and improving upon TTP-14's previously reported cross-validated AUC of 0.91 (95% CI: 0.90–0.92) in the development cohort. Among patients with complete data (n = 93), applying select thresholds for clinical relevance demonstrated: Low risk cut-off (<5% predicted iTTP risk): Sensitivity = 98% Specificity = 71% PPV = 76% NPV = 97% Accuracy = 84% High risk cut-off (≥50% predicted iTTP risk): Sensitivity = 71% Specificity = 98% PPV = 97% NPV = 78% Accuracy = 85% In the full cohort (n = 106) that included those with missing PLASMIC variables, TTP-14 maintained excellent predictive performance with AUC = 0.95 (95% CI: 0.92–0.99). Performance metrics analyzed using the above thresholds also yielded similar results. Conclusion External validation of TTP-14 demonstrated high predictive accuracy for iTTP diagnosis (AUC = 0.96). TTP-14 outperformed the PLASMIC score in the validation cohort (AUC = 0.96 vs 0.90, respectively). TTP-14 offers more flexibility than the PLASMIC score by generating predictions even with incomplete data, and maintained excellent performance in the cohort that included those with missing variables (AUC = 0.95) Its refined accuracy and ability to predict iTTP despite missing data provide a key advantage, offering an alternative tool to improve rapid iTTP diagnosis. TTP-14 may help enhance early recognition and treatment of iTTP while reducing unnecessary interventions in non-TTP patients.

Introduction: Systems-based hematology (SBH) is an emerging field focused on improving hematology care delivery through quality improvement (QI) and health systems science (HSS). However, a universal definition and standardized training curriculum are lacking. This study aimed to explore experts' perspectives on defining SBH, identify core knowledge and skills, and discuss challenges and opportunities in developing a curriculum. Methods: Semi-structured interviews were conducted with eight hematologists who have expertise in SBH through practical experience, scientific publications, and serve on related committees for the American Society of Hematology. These interviews were automatically transcribed through Microsoft Teams. Thematic analysis, using both inductive and deductive coding, was employed to identify themes and subthemes from the verbatim interview transcripts. This was accomplished with a large language model (LLM; Google Gemini Advanced, June 2024). Results: In combination, these semi-structured interviews lasted approximately 400 minutes and generated 231 pages of transcript text with 63117 words for analysis. Five main themes and associated subthemes were identified. (1) Defining SBH: experts highlighted the lack of a universal definition for SBH, emphasizing the reason it is often defined by its functions, such as improving healthcare delivery, ensuring appropriate use of resources, and addressing disparities in care. They often compared SBH to other fields like antimicrobial stewardship or implementation science to illustrate its expansive role and scope. (2) The Role and Scope of SBH: experts described SBH as encompassing a broad range of activities, including improving healthcare delivery, implementing QI initiatives, conducting pragmatic research, and advocating for policy changes at various levels. They emphasized the importance of data-driven approaches and collaboration with other healthcare professionals. (3) Training and Education in SBH: the lack of formal training programs and curricula for SBH was a recurring theme. Experts suggested that a comprehensive curriculum should include QI methodologies, implementation science, clinical informatics, health economics, leadership skills, and hematology-specific examples and case studies. They proposed various delivery methods, including asynchronous online modules, virtual workshops, and in-person sessions, targeting hematology fellows, junior faculty, and other healthcare professionals involved in hematology care. They unanimously agreed that a SBH curriculum could borrow heavily from an established HSS curriculum but should incorporate hematology-specific examples. (4) Challenges and Barriers to Implementing SBH: experts identified several challenges, including the lack of awareness and recognition of SBH among healthcare professionals and administrators, resource constraints, and the need for institutional buy-in. They also highlighted the technical difficulty of measuring and demonstrating the value of SBH interventions, particularly in terms of cost savings and improved patient outcomes. (5) The Future of SBH: despite the challenges, experts expressed optimism about the future of SBH, predicting its growing importance and expanding scope, including malignant hematology, community practice, and pediatrics. They envisioned SBH playing a crucial role in addressing rising healthcare costs, quality concerns, and disparities in care. They also anticipated the integration of new technologies like artificial intelligence, “virtual” care delivery, and molecular genetics into SBH practice. Conclusions: This study provides valuable insights into the current state of SBH and the need for a standardized curriculum. The findings can inform the development of educational resources and training programs to equip future practitioners with the necessary knowledge and skills to effectively practice SBH and improve hematology care delivery. This study also sheds light on the utility of qualitative research within SBH and the emerging analytical tools that will require further exploration and standardization (e.g., LLM prompt engineering).

Abstract Immune thrombotic thrombocytopenic purpura (iTTP) is a chronically relapsing disorder caused by autoantibody-mediated deficiency of ADAMTS13. Rituximab is frequently administered to prevent relapses, but whether the durability of rituximab effect is maintained with subsequent treatment courses has not been studied. Using the United States Thrombotic Microangiopathy Consortium (USTMA) retrospective iTTP registry, we evaluated clinical relapse-free survival (RFS) with subsequent courses of rituximab treatment in multiply relapsing patients. Separately, we evaluated overall RFS (composite of time to clinical relapse, ADAMTS13 relapse, or preemptive rituximab) in a prospective iTTP cohort from the Johns Hopkins University and the University of Minnesota. In the USTMA registry, median clinical RFS was shorter after the second or subsequent rituximab-treated episode than the first (2.1 vs 6.0 years; P = .04). White patients’ clinical relapse risk after the second and subsequent rituximab courses was not significantly different compared with the first (hazard ratio [HR], 1.86; 95% confidence interval [CI], 0.22-15.80; P = .57), whereas for Black patients, clinical relapse risk was significantly higher after the second or subsequent courses (HR, 2.82; 95% CI, 1.52-5.24; P = .001). In the prospective cohort, overall RFS progressively shortened after each episode of rituximab treatment with the first episode having the longest RFS (2.8 years; interquartile range, 2.0-6.0) and this loss of response durability was most pronounced in Black patients. The durability of rituximab’s effect declines with subsequent treatments, which is more pronounced in Black patients, who may benefit from closer monitoring and alternative immunomodulatory approaches such as maintenance rituximab and consideration of other agents.

The NCCN Guidelines for Cancer-Associated Venous Thromboembolic Disease provide strategies for the prevention, diagnosis, and treatment of venous thromboembolism (VTE) in adult patients with cancer. VTE is a common and life-threatening condition in patients with cancer, and its management often requires multidisciplinary efforts. The NCCN panel is comprised of specialists spanning various fields, including cardiology, hematology, medical oncology, internal medicine, interventional radiology, and pharmacology. The content featured in this issue specifically addresses the evaluation and recommended treatment options outlined in the NCCN Guidelines for the diverse subtypes of cancer-associated VTE.