Citations

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.

Purpose of review Cardiac amyloidosis is an infiltrative cardiomyopathy with characteristic echocardiographic findings. Echocardiography is the initial imaging test to identify restrictive cardiomyopathies, and timely diagnosis is important for treatment and prognosis. This review outlines key echocardiographic “red flags,” structural and functional findings, and emerging diagnostic tools that enhance early recognition and monitoring of cardiac amyloidosis. Recent findings Classic echocardiographic red flags for cardiac amyloidosis include increased ventricular wall thickness, biatrial enlargement, pericardial effusion, and a granular myocardial appearance. Tissue Doppler imaging shows reduced mitral annular velocities, while advanced disease presents with restrictive filling patterns. Global longitudinal strain with apical sparing is a hallmark finding that distinguishes cardiac amyloidosis from other cardiomyopathies. Left atrial strain has been proposed as a prognostic marker for thromboembolism in cardiac amyloidosis. Evidence shows variability in strain measurements across different vendors, which is an important consideration when comparing results between institutions in both research and clinical practice. Echocardiography can be used in conjunction with multimodal imaging techniques in order to monitor disease progression. Summary Echocardiography is the initial diagnostic test of choice and several findings can suggest cardiac amyloidosis. Advances in echocardiographic techniques have enabled earlier detection of cardiac involvement and now play a central role in both diagnosis and ongoing treatment monitoring.

Abstract INTRODUCTION Brain metastases (BM) are the most common intracranial tumor, with a poor prognosis of 10-16 months, and lung adenocarcinoma (LUAD) is the most common source. Currently, we cannot reliably predict which patients will develop BMs clinically, so most are detected after they develop and growing to cause deficits. Here, we use tumor DNA methylomes to predict which patients are likely to develop BM through the course of disease and we use both cerebrospinal fluid (CSF) and plasma methylomes for BM liquid biopsy, enabling their early detection and treatment. METHODS DNA methylomes were acquired from 419 tumor and plasma samples from 363 LUAD and BM patients. We built DNA methylation-based models that predict BM development and that also identify BMs non-invasively from CSF and plasma in discovery data subsets. A nomogram was built that predicts BM risk using methylation scores and clinical stage together. Models were evaluated in independent validation sets. RESULTS Our model predicting BM development was accurate (Univariable Cox: HR=5.7, 95% CI=1.9-17.2), p=0.0023), independent of clinical factors (Multivariable Cox: HR=8.9, 95% CI=2.0-40.5, p=0.0046), and more predictive than cancer staging used currently clinically (AUC=0.81 versus 0.65). Our combinatorial nomogram using methylation and staging data together showed enhanced BM prediction (Univariable Cox: HR=17.2, 95% CI=4.1-71.3, p<0.0001; AUROC=0.82). Our models identifying BM through biofluid methylomes accurately distinguished BM from differential diagnoses of gliomas and lymphomas in plasma (AUROC=0.80, 95% CI=0.68-0.93) and with greater accuracy in CSF (AUROC=0.93, 95% CI=0.71-1.0). CONCLUSION Overall, we show the first approaches for robust molecular prediction of BM development and for reliable liquid biopsy of BM. This work is expected to transform cancer management by identifying high-risk lung cancer patients after initial treatment for inclusion in screening protocols using neuroimaging and liquid biopsy, to identify BM development early with less advanced disease and more optimal therapeutic outcomes.

Migraine is one of the most disabling diseases worldwide, especially when it transforms into chronic migraine, which is often associated with medication overuse and can become resistant or even refractory to treatments. Molecular, neuroimaging and neurophysiological changes have been described in chronic migraine, some of which might not be fully reversible with preventive treatment. For these reasons, we should aim to prevent this transition, and initiate preventive treatment before disease becomes refractory and burden increases. Preventive migraine treatments are often delayed because of access to care, stigma leading to undertreatment and patients’ reluctance as a result of fear of side effects and, in some cases, fear of being labeled as chronically ill. With the availability of effective and well-tolerated preventive treatments, we must shift our mindset and take advantage of new opportunities to initiate preventive treatment earlier. In this International Headache Society position statement, we propose a migraine preventive strategy under the idea of shifting from reactive treatment once disability is established (prevention of attacks), to proactive, individualized prevention initiated early with safe, effective and tolerable therapies (prevention of disease progression). This approach is based on 1) promoting the early initiation of effective and tolerable preventive therapies, starting from two to four monthly migraine days in line with the majority of current guidelines and recommendations and 2) fostering longitudinal studies to gather more evidence on the potential benefit of early prevention, with the final goal of improving patient outcomes, promoting excellent migraine care, enhancing individual and social well-being, and, ultimately, preventing migraine progression and preserving brain health.

Intracardiac metastases from gastroenteropancreatic neuroendocrine tumors (NETs) are rare and challenging to diagnose. Lutetium (Lu)-177 DOTATATE therapy has demonstrated significant benefit in patients with metastatic NET; however, there are limited data regarding the direct impact on cardiac metastases. Patients with intracardiac metastasis from NET were evaluated. Patients were stratified by treatment with Lu-177 DOTATATE. Quantitative assessments of the progression of cardiac metastatic size (maximal diameter) and metabolic activity, as measured by maximal standardized uptake value (SUV max ), were performed on serial 68 Ga-DOTATATE PET imaging. In a cohort of 23 patients, 11 patients (47.8%) received Lu-177 DOTATATE therapy. Through a median of 2.2 years of follow-up, these patients showed some reduction in metastatic burden with a median reduction in maximal diameter of 1.0 mm (IQR: 0.0–3.0) and a median change in SUV max of −1.6 (IQR: −4.4 to 1.5). In contrast, patients who did not receive Lu-177 DOTATATE had some increase in maximal diameter of 0.5 mm (−2.0 to 2.0) and an increase in SUV max of 1.4 (−6.2 to 2.3). Overall, Lu-177 DOTATATE therapy may result in modest regression of intracardiac metastases as assessed by molecular imaging, whereas patients with cardiac metastases who did not receive therapy appeared to show progression.