Citations

Abstract Introduction: Hemophagocytic lymphohistiocytosis (HLH) is a rare and life-threatening hyperinflammatory syndrome resulting from immune dysregulation. While diagnostic and therapeutic guidelines (e.g., HLH-2004) are established for pediatric HLH, adult-onset HLH remains both a diagnostic and therapeutic challenge, with limited characterization and sparse outcome data. Here, we conduct a single-institution retrospective cohort study to evaluate clinical features, causes, and prognostic markers associated with outcomes. Methods: We performed a retrospective chart review of all adult patients (≥18 years) first diagnosed with HLH across all Mayo Clinic sites between January 1, 2006 and July 1, 2025. Diagnosis was based on hematologist adjudication at time of care. Patients with prior diagnoses of HLH or HLH related to T-cell therapy (CAR-T) were excluded. Clinical and laboratory variables were extracted from electronic health records. The primary outcome was overall survival (OS), defined as time from diagnosis of HLH to death or last follow-up. OS was estimated using the Kaplan-Meier method. Multivariable analysis to identify independent predictors of mortality was performed using a Cox proportional hazards regression model, adjusting for age at diagnosis, sex, HLH etiology, and relevant clinical and laboratory parameters. Model discrimination was assessed using Harrell's concordance index (c-index). Descriptive statistics were summarized using median and interquartile range (IQR) for continuous variables and frequency with percentages for categorical variables. Results: A total of 203 patients diagnosed with HLH were included. The median age at diagnosis was 55 years (range 18-86). The cohort was predominantly male (n=130, 64%) and White (n=163, 80%). The majority of diagnoses occurred in more recent years, with 75 patients (36.9%) diagnosed between 2021–2025, 61 (30.0%) between 2016–2020, and 43 (21.2%) between 2011–2015. Prior to HLH diagnosis, 86 patients (42.4%) had a history of malignancy, 55 (27.1%) had a history of autoimmune disease, and 28 (13.8%) had a history of transplant. The most common HLH etiologies were malignancy-associated (n=99, 49%), infectious (n=51, 25%), and autoimmune (n=24, 12%), in addition to idiopathic (n=6, 3%), post-transplant lymphoproliferative disease or graft-vs-host disease (n=3, 1%), drug-induced (n=3, 1%), or primary HLH (n=1, 0.5%). The most common malignancies included T-cell (n=35, 17.2%) and diffuse large B-cell lymphoma (n=24, 11.8%). Forty-four cases (21.7%) were associated with Epstein-Barr virus (EBV), either through overt viremia or malignancies with EBV positivity on pathology report. Five patients (2.5%) had COVID-19–associated HLH and 2 (1.0%) were attributed to immune checkpoint inhibitors. Overall, 164 patients (80.8%) met the HLH-2004 diagnostic threshold of ≥5 out of 8 criteria, while 196 (96.6%) met the HLH-2024 ≥4 of 7 modified criteria. Among patients with available information, elevated ferritin was present in 197 of 198 (99.5%), fever in 185 of 202 (91.6%), cytopenias involving ≥2 lineages in 184 of 199 (92.5%), hemophagocytosis in 156 of 182 (85.7%), elevated sCD25 in 117 of 121 (96.7%), hypertriglyceridemia in 117 of 186 (62.9%), hypofibrinogenemia in 118 of 196 (60.2%), splenomegaly in 125 of 188 (66.5%), and impaired NK cell activity in 16 of 25 (64.0%). In addition, hepatomegaly was noted in 57 of 195 patients (29.2%). After a median follow-up of 58.3 months, median OS was 62 days (95% CI 40-110) from day of diagnosis. In a multivariable Cox proportional hazards model with moderate discrimination (c-index=0.75), after adjusting for age at diagnosis and sex, T-cell lymphoma–driven HLH (HR 1.78, 95% CI 1.05–3.00, p = 0.03) and elevated blood urea nitrogen (median 51.0 mg/dL, IQR 27.9–99.1; HR 2.07, 95% CI 1.37–3.13, p < 0.005) were independently associated with shorter OS Conclusions: In this cohort of adult patients with HLH, malignancy was found to be the most common trigger for HLH. T-cell malignancies in particular were strongly correlated with poor survival. Elevated blood urea nitrogen at diagnosis also identified patients at significantly higher risk of death, consistent with other critical illnesses that can present with multi-organ failure. These findings emphasize the need for adult-specific prognostic tools incorporating disease etiology and organ dysfunction markers to better stratify risk and guide clinical management in adult-onset HLH.

Abstract Background: Social determinants of health (SDOH) are critical drivers of cancer care access and outcomes. In multiple myeloma (MM), access to cellular therapies including autologous stem cell transplantation and CAR-T therapy often depends on formal or informal caregiver support. Traditional SDOH measures (e.g., race, income, or education) are often used as proxies for access to care; however, these measures may overlook individual realities pertaining to cellular therapies where a caregiver is generally required. In response to feedback from a HealthTree Patient Advisory Board questioning the practice of inferring caregiver support, this study evaluated a simple survey question by a large cross-sectional survey. Methods: After development of a dedicated single-question (“What best describes your current caregiving support?”) through iterative feedback from the Patient Advisory Board and MM experts, four key answers were settled on: (1) cohabitation with dedicated caregiver(s) to support care (“family support”); (2) presence of non-cohabiting caregiver(s) to provide dedicated support (“external support”); (3) lack of dedicated caregiver, hence support is arranged when needed (“arrangeable support ”); or (4) lack of dedicated caregiver, hence finding any support is a struggle (“inadequate support”). We deployed this new demographic question from February through July 2025 through HealthTree. Responses were analyzed across SDOH variables using Fisher exact or chi square tests, and random forest classifiers were employed for predictive modeling. Results: Of 1231 respondents, 71% had dedicated caregivers (68%, n=839, with family support, and 3%, n=41, with external support). In contrast, 23% (n=287) of patients routinely arranged support while 5% (n=64) had inadequate support (struggle to find support). The inadequate-support group had significant demographic differences from the sample as a whole: a higher proportion were women (78% vs 56%, p = 0.04), Black (9% vs 4%, p < 0.01), lived >50 miles from their treatment center (17% vs 6%, p < 0.01), and had an annual income <$50,000 (17% vs 6%, p = 0.02). This group had a numerically higher proportion of patients whose employment status was disabled (12% vs 5%, p=0.08) as well. No proportion difference in age (67 ± 9 yr), military status, or severity of diagnosis - precursor disease (17%) v. multiple myeloma (81%). Rates of inadequate support were overall low in patients who lived with only their spouse (1%) or lived with their spouse & children (3%). In contrast, rates of inadequate support were higher (than 5%) in patients who lived alone (21%), divorced (20%), lived with adult children (14%), lived with friends (13%), separated (11%), or widowed (9%) (p < 0.001). However, the arrangeable-support group comprised 12% of patients who lived only with their spouse, 19% of patients who lived with their spouse and children, and 61% of patients who lived with their adult children (p < 0.001). Finally, a random forest classifier failed to identify any individual in the inadequate-support category, suggesting that unmeasured or more complex patterns may underlie this classification. Conclusions: To our knowledge, this is the largest analysis of caregiver support and its relationship with other SDOH in patients with MM to date. Despite the importance of caregiving to transplantation and CAR-T therapy, we found that 5% of patients with MM struggle to find caregiver support; this figure likely underestimates the true prevalence, given potential selection bias among patients who are digitally engaged through the HealthTree platform. Even among patients who lived with their spouses, an additional 10-20% of patients routinely had to arrange caregiving support on a visit-by-visit basis; for patients who lived with their adult children, this proportion was over 60%. The requirement to arrange caregiving support can create time toxicity and financial toxicity for patients, although these factors were not directly queried in our study. Finally, caregiver availability cannot be routinely inferred from traditional SDOH variables. Our data highlight the importance of including caregiver status as a standard demographic question in clinical and research settings. Further interventions are needed to improve caregiver support for vulnerable patients with MM as well as to reduce the duration of caregiver requirements for procedures such as CAR-T therapy.

Abstract Introduction: TERT promoter (pTERT) variants are among the most frequent genetic alterations in human cancers, where they drive oncogenesis through telomerase reactivation. However, somatic, pathogenic pTERT variants have also been seen in non-cancerous conditions such as telomere biology disorders (TBD), where they attempt to rescue the bone marrow failure phenotype (adaptive compensation). We conducted this study to assess the phenotype and outcomes of pTERT mutations in TBD patients. Methods: After IRB approval, the Mayo Clinic institutional TBD database was queried to identify all patients with somatic pTERT mutations. We have designed a customized error corrected sequencing panel that covers somatic mutations (52 genes) associated with adaptive and maladaptive outcomes in TBD patients. This covers the promoter region for TERT and can detect variants down to 0.5% variant allele fraction (VAF) with 10,000 X sequencing depth. Clinical data was retrospectively abstracted. Results: We identified 7 pTERT variants (3 hotspots) in 6 (two females (33%), mean age 61 years, 4 alive). The three hotspots were: (1) c.-57A>C (2 individuals, mean VAF 17.1%; range 11.6%-22.6%), (2) c.-124C>T (4 individuals, mean VAF 12.9%; range 1.9%-41.3%), and (3) c.-146C>T (1 individual, VAF 1.2%). One individual had both the c.-124C>T (VAF 41.3%) and the c.-146C>T somatic pTERT variants, occurring in the context of a germline TERT variant. The median VAF for all 7 pTERT variants was 6.1% (range 1.2%-41.3%). The hotspot, c.-57A>C, occurred in the context of pathogenic germline TERT (VAF 49.9%) variants with pathogenic somatic co-variants in PPM1D (avg. VAF 1.5%). The second hotspot, c.-124C>T, occurred in the context of germline TERT (avg. VAF 49.6%) and RTEL1 (VAF 47.3%) variants with pathogenic somatic co-variants including ASXL1 (VAF 1%), DNMT3A (VAF 8%), and PPM1D (VAF 4%). Five of six assessable patients with pTERT variants had shortened telomere lengths (TL), with results pending for one individual. Among those with the c.-57A>C variant, granulocyte TLs were at or below the 10th percentile, while lymphocyte TLs were below the 10th percentile in one patient and below the 1st percentile in another. For those with the c.-124C>T variant, all granulocyte TLs were below the 1st percentile. Lymphocyte TLs in this group included one at the 10th percentile and two at or below the 1st percentile. The median follow up for all patients was 2 years. Hotspot 1 (c.-57A>C) variants were associated with normocellular bone marrow biopsies with one patient having normal blood counts and the other having chronic macrocytic anemia (grade 2) and thrombocytopenia (< grade 1). In patients with hotspot 2 and 3 somatic variants, bone marrow cellularity ranged from 15–50% (1 patient had grade 2-3 hypocellularity), with normal bone marrow morphology, except for polytypic plasma cells (10–15%) identified in a patient with IgG kappa multiple myeloma (currently MRD negative on maintenance therapy). This patient also had a history of head and neck cancer and follicular lymphoma. Of these four patients, only one had normal blood counts (co-occurring c.-146C>T variant); the rest had cytopenias (n=3, the worst being grade 2 thrombocytopenia), progressive macrocytosis (n=3), or eosinophilia (n=1). Comorbidities encountered in these patients included myasthenia gravis (1), hypogonadism (1), and sarcoidosis (1). Moreover, four patients (2 each for hotspots 1 and 2) presented with idiopathic pulmonary fibrosis, while liver fibrosis was seen in 4 patients (also 2 each for hotspots 1 and 2). At last follow up, none of these patients had documented progression to myelodysplastic syndrome or acute myeloid leukemia. Conclusions: We describe the spectrum and clinical features of somatic pTERT mutations in TBD patients. pTERT mutations involved the following hotspot regions, c.-124C>T, c.-146C>T, and c.-57A>C, and co-occurred with other somatic variants (i.e., PPM1D and DNMT3A). None of the 6 patients with pTERT mutations had a severe bone marrow failure phenotype or developed myeloid neoplasms at last follow up, exemplifying a unique mechanism of adaptive somatic rescue.

Abstract Background Accurate reconstruction of electronic health record (EHR) treatment sequences is essential for leveraging real-world data (RWD) in clinical research and trial-eligibility platforms in multiple myeloma (MM). However, inconsistencies in RWD treatment documentation and the complexity of line-of-treatment (LOT) assignments in MM hinder reproducibility and limit comparisons against published trials. To address these limitations, we developed HEAL-MM, an automated rule-based algorithm that systematically parses structured medication data to calculate LOTs and segment treatment into standardized, clinically meaningful phases. The algorithm is designed to be interpretable, adaptable to evolving therapeutic frameworks, and compatible with widely adopted EHR data standards. Here, we report its real-world validation against expert-adjudicated treatment sequences to assess concordance, identify edge-case failure points, and support RWD integration. Methods A stratified cohort of 100 MM patients (9,136 structured treatment events) was curated from 114 unique healthcare systems compiled from the multi-institutional HealthTree Foundation harmonized registry. Cohort selection was randomized to ensure coverage across diagnostic eras (pre-2010, 2015–2020, 2022–2025) and treatment exposures, including CAR T-cell therapies, bispecific antibodies, and anti-CD38-based regimens. HEAL-MM sequenced MM regimens chronologically and applied predefined logic rules to identify LOT transitions, the initiation of a new therapeutic course based on drug changes, treatment gaps, and procedural anchors such as stem cell transplant or CAR T. Within each LOT, the algorithm segments and labels therapy into phases, clinically distinct intervals (e.g., induction and maintenance) using rule-based clinical logic. Outputs were benchmarked against expert-labeled adjudications. Concordance was evaluated using concordance index (C-index), stratified by patient, LOT depth and diagnostic era. Results: HEAL-MM demonstrated high concordance with expert-labeled treatment sequences: 0.94 for LOT identification, 0.98 for treatment phase segmentation, and 0.96 for phase label assignment. Achieving complete agreement with all expert annotations in 90% of patients for LOT assignment, 94% for the number of treatment phases identified, 77% for the specific labels applied to each phase, and 75% across all three dimensions simultaneously. Among discordant patients, the absolute difference in final LOT count was 0.5 ± 0.9. Performance was highest in early lines (LOTs 1–3; C-index: 0.97), with seven patients showing discrepancies at this stage. Concordance declined in later lines (LOT ≥4; C-index: 0.87), where 50% of the discordant cases (n=4) were attributable to misclassifications in earlier LOTs, such as medication restarts following >3-month gaps, demonstrating the compounding effect of early errors on longitudinal accuracy. Temporal stratification revealed improved accuracy in post-2020 cases (C-index: 0.94) compared to 2015–2020 cohorts (0.91), reflecting the increasing adoption of structured treatment standards and EHR data quality improvements. To assess HEAL-MM performance at clinically significant junctures, we analyzed treatment transitions that initiated a new LOT or marked a change in phase, achieving C-indices of 0.97 for LOT transitions, 0.97 for treatment phase shifts, and 0.94 for phase label assignment. Conclusions: HEAL-MM provides a validated, scalable solution for standardizing MM treatment structures in RWD environments, as it accurately detects the exact treatment entry of a new LOT 97% of the time. This rule-based framework enables reproducible LOT and phase assignment across heterogeneous EHR systems, supporting real-time cohort identification across institutions and EHR platforms. The algorithm's modular design allows for continuous integration of novel therapeutic categories and evolving treatment logic. While the algorithm maintained high fidelity overall, early-stage misclassifications consistent with increased heterogeneity and documentation ambiguity occasionally propagated downstream, underscoring the compounding effect of upstream errors in longitudinal reconstruction. With its generalizable structure and compatibility with industry-standard data models, HEAL-MM lays the foundation for structured treatment reconstruction in MM across RWD infrastructure and serves as a transferable blueprint for other hematologic malignancies.

Abstract Introduction: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, life-threatening hematological disorder affecting ≈24 per 1,000,000 people in the US. Iptacopan, the first orally administered monotherapy for PNH, received US Food and Drug Administration approval in December 2023. APPRISE-PNH is a data platform designed to capture comprehensive real-world information on iptacopan-treated patients with PNH. The aim of this study was to describe patient profiles and assess treatment patterns and clinical outcomes among iptacopan-treated patients (age ≥18 years) with PNH in the US real-world setting. Methods: The present interim analysis of APPRISE-PNH includes data from patients enrolled between July 2, 2024, and June 13, 2025, with additional data collection expected through 2025. Duration of therapy was measured from the date of iptacopan initiation (index) until discontinuation or data cutoff, whichever occurred first. Treatment adherence was measured using the medication possession ratio (MPR; ratio of the total days' supply to days covered) and the proportion of days covered (PDC; ratio of days with supply to days covered). Clinical outcomes were assessed during follow-up including hemoglobin (Hb; change from pre-index to highest level post index, proportion of patients with Hb increase ≥2 g/dL, and proportion of patients with Hb normalization to ≥12 g/dL), lactate dehydrogenase (LDH) (change from pre-index to lowest level post index and proportion of patients with LDH reduction <1.5× upper limit of normal [ULN]), and number of red blood cell (RBC) transfusions. Results: Among 52 patients enrolled as of June 13, 2025, the median age was 53.7 years, 38% were male, and 60% were covered by commercial insurance. Overall, 25% of patients (n=13) were complement inhibitor (CI) naive at the time of iptacopan initiation, while 75% (n=39) were treatment experienced and had received ≥1 prior CI therapy for PNH (77% ravulizumab, 46% eculizumab, 26% pegcetacoplan). All patients received iptacopan 200 mg twice daily, per the product label; the median (IQR) duration of iptacopan therapy was 9.8 (5.0-12.8) months. At the time of data cutoff, 2 patients discontinued therapy (1 due to provider decision, 1 due to death). The median (IQR) MPR and PDC were 105.2% (100.6%-110.7%) and 97.3% (92.7%-99.5%), respectively. Among the 52 patients enrolled in APPRISE-PNH, 42 patients had medical records available at the time of data cutoff. Of the 23 of 42 patients (55%) with Hb values available pre- and post index at the time of data cutoff, 4 were CI treatment naive (median [IQR] pre-index Hb, 9.9 [9.1-10.2] g/dL) and 19 were CI treatment experienced (median [IQR] pre-index Hg, 10.7 [8.9-12.2] g/dL). The median (IQR) change from pre-index to highest value post-index was similar for both groups (1.6 [0.8-2.3] g/dL and 1.6 [0.6-3.4] g/dL, respectively). However, among the CI treatment–experienced patients, those with pre-index Hb <12 g/dL (n=11) experienced a greater improvement than those with pre-index Hb ≥12 g/dL (n=8), with median (IQR) of 3.3 (2.2-4.5) g/dL vs 0.4 (0.1-1.3) g/dL, respectively. Overall, 12 of 23 patients (52%) achieved a Hb increase ≥2 g/dL, and 17 of 23 (74%) achieved Hb normalization to ≥12 g/dL. Among the 18 of 42 patients (43%) with LDH values available pre- and post index, the median (IQR) change from pre-index to lowest post-index value was −41.5 (−155.8 to 2.5) U/L. Among those with clinical data, most patients (38 of 42; 90%) did not require RBC transfusions during follow-up and 4 had evidence of an RBC transfusion over the follow-up period; 2 of 4 patients requiring transfusions had aplastic anemia and/or myelodysplastic syndrome. At the time of data cutoff, no patients with clinical data available had evidence of a major adverse vascular event (including thrombosis, transient ischemic attack, stroke, and major bleeding) over the follow-up period. Conclusions: These data represent early insights into the real-world use of iptacopan and related Hb response in US patients with PNH. Adherence to iptacopan was high, and iptacopan treatment improved Hb and LDH parameters, providing comprehensive hemolysis control. Enrollment is currently ongoing; data on additional patients with longer follow-up are forthcoming.

ABSTRACT Pharmacists play a critical role in optimizing medication use, especially in vulnerable populations such as those with chronic kidney disease (CKD). Despite strong evidence and recent guideline endorsements, many pharmacists continue to rely on the Cockcroft–Gault (C‐G) equation for medication dosing. This opinion paper issues a call to action for the widespread adoption of a race‐free, body surface area–adjusted estimated glomerular filtration rate (eGFR BSAadj ) for medication‐related decision‐making, as endorsed by the 2024 Kidney Disease: Improving Global Outcomes CKD guidelines and the National Kidney Foundation and the American Society of Nephrology Taskforce. Persistent variability in pharmacy practice, institutional protocols, and pharmacy education underscores the need for coordinated change. We urge health systems to integrate eGFR BSAadj into electronic health records and clinical decision support to enhance medication decision‐making; institutions to align protocols with contemporary eGFR‐based prescribing standards; pharmacy educators to contemporize and standardize education on kidney function assessment and medication dosing; and regulatory bodies to update licensure and board examinations. The pharmacy profession must act decisively in order to align with evidence‐based, equitable kidney‐related dosing practices and ensure optimal medication management for all patients.

ABSTRACT Central venous catheters (CVCs) are a cornerstone of hematopoietic stem cell transplantation (HSCT), facilitating the delivery of chemotherapy, parenteral nutrition, medications, and blood products. Despite CVCs’ essential role, prolonged use can lead to complications such as increased risk of infection, thrombosis, mechanical issues, and psychosocial burden. However, the timing of CVC removal remains highly variable across institutions. Additionally, there are no standardized guidelines to balance the benefits of extended access against the risks of prolonged use. This narrative review examines the existing literature on CVC management post‐HSCT, highlighting key factors influencing the removal timing, such as patient characteristics, disease‐specific considerations, and institutional practices. The review emphasizes the urgent need for multi‐institutional research and standardized protocols to improve outcomes, reduce complications, and enhance the quality of care for HSCT patients.

Abstract Background Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. Objective To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse. Fitness for Purpose Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application. Fitness for Reuse Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable—that is, it identifies the same intended population with similar performance characteristics when applied to a new setting. Conclusions The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.