Yousuf, Muhammad

6588 Background: TET2 is the most frequently mutated gene in chronic myelomonocytic leukemia (CMML) and has been associated with favorable outcome, with recent studies suggesting a more pronounced prognostic impact in the presence of ≥2 TET2 mutations ( Csizmar et al. leukemia 2025;39:2030; Kynning et al. Br J Haematol. 2025 Nov 25. doi: 10.1111/bjh.70264 ). We sought to determine if this particular observation extended to patients with ≥3 TET2 mutations ( TET2 MUT ) and also held true in the context of recently reiterated genetic risk factors. Methods: The current study was conducted under institutional review board approved minimum risk protocols allowing retrospective patient data collection and analysis. Diagnostic criteria were according to the International Consensus Classification (Arber et al. Blood 2022. 140:1200). All statistical analyses were conducted using JMP 18 software. Results: 536 CMML patients who underwent CLIA-approved NGS testing were stratified according to the number of TET2 MUT : 221 (41%) wild-type, 148 (28%) with one, 153 (29%) 2, and 14 (2%) ≥3 TET2 MUT . Co-mutation frequencies with significant differences between one vs. two TET2 MUT included ASXL1 (46% vs. 29%; p<0.01), KRAS (7% vs. 14%; p=0.02) and SETBP1 (5% vs. 0.6%; p=0.02). Phenotypic comparisons revealed preponderance of prognostically favorable traits associated with wild-type TET2 but no significant differences between one vs. two TET2 MUT . In age-adjusted univariate analysis, transplant-censored overall survival (TCOS), calculated from the time of mutation detection, was superior in patients with one (p<0.01; HR 0.7) or 2 (p<0.01; HR 0.4) but not ≥3 (p=0.4) TET2 MUT , vs. those with wild-type TET2 . TCOS was also superior with 2 vs. 1 (p=0.02; HR 0.7) but not with ≥3 vs. 1 (p=0.7) TET2 MUT ; the results were similar when TCOS was calculated from the time of diagnosis. Multivariable analysis confirmed the survival advantage of exactly two TET2 MUT vs. wild-type TET2 (p<0.01; HR 0.5), one TET2 MUT (p<0.01; HR 0.6), or ≥3 TET2 MUT (p<0.01; HR 0.4), after adjusting for age, sex, anemia, circulating blasts ≥2%, and leukocyte count ≥13×10⁹/L. The favorable impact of exactly two TET2 MUT persisted in CMML-MD and CMML-MP and remained independent of TET2 VAF, mutation type, karyotype, and other prognostically relevant mutations. This association was sustained in the BLAST (p<0.01; HR 0.5) and CPSS-mol (p=0.02; HR 0.7) models, but not in BLAST-mol model (p=0.12). Conclusions: Mechanistic explanation for this novel observation includes the possibility that TET2 mutational categories based on mutation count reflect distinct biological states rather than having a simple linear effect. The persistence of this signal despite adjustment for established molecular and genetic risk factors supports its consideration in the development of future CMML prognostic models.

Abstract Background: The recently published BLAST clinical and BLAST molecular (BLAST-Mol) risk models for chronic myelomonocytic leukemia (CMML) incorporate clinical and molecular variables, respectively, for prediction of overall survival (OS), while BLAST-BT (blast transformation) risk model was predictive of BT free survival (BTFS) [Blood 2025;doi:]. In the current study, the Mayo Clinic (USA) CMML cohort used in the aformentioned study was expanded from 457 to 600 patients and merged with an external cohort of 501 patients from the Humanitas Research Hospital (HRH; Italy) in order to validate the BLAST, BLAST-Mol and BLAST-BT risk models. Methods: CMML diagnosis and sub-categorization were according to the International Consensus Classification, which included CMML-1 (BM/PB blasts &lt;10%/&lt;5%) vs. CMML-2 (BM/PB blasts ≥10%/≥5%) and CMML-MP (leukocytes ≥13 x 10⁹/L) vs. CMML-MD (leukocytes &lt;13 x 10⁹/L) [Blood 2022;140:1200]. The BLAST score is based on clinical risk factors including PB blasts ≥2%, leukocytes ≥13 x 109/L, and severe or moderate anemia resulting in BLAST low, intermediate, and high risk groups. BLAST-Mol also accounts for mutations and karyotype and identifies PHF6 and TET2 as “favorable” and DNMT3A, U2AF1, BCOR, SETBP1, ASXL1, NRAS, PTPN11, RUNX1, TP53, as “unfavorable” mutations with adverse karyotype also considered unfavorable. BLAST-BT variables include absence of PHF6MUT, BM blasts ≥10%, presence of DNMT3AMUT, PB blasts ≥2%, leukocyte count ≥13 x 109 /L, and ASXL1MUT. OS analyses were censored for allogeneic stem cell transplant (ASCT). Conventional statistical methods were employed, using JMP Pro 18.0.0 software SAS Institute, Cary, NC, USA. Results: A total of 1,101 patients (median age 73 years; 67% males) with newly-diagnosed CMML were considered: Mayo cohort 600 and HRH cohort 501. Compared to the HRH cohort, the Mayo cohort was more likely to include CMML-MP (44% vs. 28%; p&lt;0.01), CMML-1 (87% vs. 81%; p&lt;0.01), ASXL1MUT (48% vs. 35%; p&lt;0.01), SRSF2MUT (43% vs. 33%; p&lt;0.01), NRASMUT (15% vs 9%; p&lt;0.01), PHF6MUT (7% vs. 4%; p&lt;0.01), and less likely to harbor TET2MUT (50% vs. 58%; p&lt;0.01), and SF3B1MUT (4% vs. 10%; p&lt;0.01) [Table 1]. At a median follow-up of 55 months (range 0-202), 686 (62%) deaths, 218 (20%) BTs, and 142 (13%) ASCTs were documented. CPSS-mol risk categories at diagnosis included low (N=190; median OS 67 months), intermediate-1(N=271; median 38 months), intermediate-2 (N=407; median 26 months), and high (N=231; median 16 months; p&lt;0.01) [AUC at 5 years 0.75]. BLAST clinical risk distribution was low (N=356; median OS 65 months), intermediate (N=393; median 30 months), and high (N= 352; median 16 months; p&lt;0.01) [Figure 1; AUC at 5 years 0.79]. The corresponding AUCs calculated for the Mayo and HRH cohorts, separately, were 0.81 and 0.76, respectively. BLAST-Mol risk assignments included low (N=357; median OS 66 months), intermediate (N=512; median 27 months) and high (N=232; median 15 months; p&lt;0.01) [Figure 2; AUC at 5 years 0.79]. The corresponding AUCs for the Mayo and HRH cohorts were 0.81 and 0.77, respectively. The 3-year cumulative incidence of BT (accounting for death from another cause) stratified by BLAST-BT risk model included 8% for very low risk (N=54), 11% for low risk (N=332), 20% for intermediate-risk (N=281), 27% for high risk (N=250), and 34% for very high risk (N=176) [p&lt;0.01; Figure 3]. Multivariable analysis of the combined Mayo-Humanitas cohort (N=1,101) identified age &gt;65 years (HR 1.7, 95% CI 1.3-2.1), CMML-2 subtype (HR 2.0; 95% CI 1.6-2.5), and a number of other mutations or co-mutations with BLAST-Mol-independent and CPSS-mol-independent prognostic contribution (to be elaborated further at the time of meeting presentation). Conclusions: The current study validates the predictive performance of the BLAST, BLAST-Mol and BLAST-BT risk models in a large two-center international cohort of patients with CMML. The possibility of further enhancement of the BLAST-Mol model is suggested by the demonstration of independent prognostic relevance from additional mutations and co-mutations.

Abstract Background Myeloproliferative neoplasms (MPNs) can progress from chronic phase to accelerated or blast phase (MPN-AP/BP), a transition with poor prognosis and limited treatment options. While some abnormalities have been described, the genomic and clonal drivers of transformation remain poorly defined. We characterized mutational and structural alterations in MPN-AP/BP, leveraging paired samples to trace clonal evolution. Methods We profiled 186 samples from 160 individuals, including 159 MPN-AP/BP and 27 chronic-phase MPN samples (26 MF, 1 PV), using targeted sequencing of a 588 gene panel (mean depth: 709x). Paired pre- and post-transformation samples were available for 26 patients (n= 52). A consensus pipeline was used to detect somatic mutations, copy number variations (CNVs), and copy-neutral loss of heterozygosity (cnLOH). Clinical cytogenetic data were integrated when available. Single-cell validation is ongoing. Results Genomic complexity was widespread: 99% of patients harbored ≥1 oncogenic mutation (median 4; range 1–10). In total, 859 mutations were identified, most commonly in JAK2 (68%), ASXL1 (28%), TET2 (26%), SRSF2 (24%), TP53 (22%), and RUNX1 (22%). 684 structural alterations were identified in 86% of patients (median 3; range 1-20), including arm-level (22.5%), segmental (&gt;5 Mb; 60%), and focal (&lt;2 Mb; 9%) alterations. Recurrent events included 9p cnLOH (16%), del(5q) (13%), del(17p) (10%), and focal deletion of 21q22.12 involving RUNX1 (10%). Complex karyotypes (≥3 abnormalities) were observed in 55% of patients. Biallelic mutations accounted for 21% (183/859) of mutations, affecting 58% of patients across 21 genes, most frequently JAK2 V617F (28%), TP53 (17%) and TET2 (14%). Patients with biallelic mutations in these genes had significantly higher blast percentages compared to those with monoallelic mutations (p&lt;0.05), highlighting an association with aggressive disease. In 26 patients with longitudinal samples (chronic phase MPN and MPN-AP/BP), 87% (125/144) of mutations were already detectable during the chronic phase, on average more than 7 years prior to transformation. Only 11% (16/144) of mutations were newly acquired, including additional RUNX1 and TP53 hits, or structural changes that converted pre-existing JAK2 V617F and TP53 R248Q mutations from monoallelic to biallelic via 9p cnLOH or 17p loss, respectively. These findings suggest transformation is often driven by outgrowth of pre-existing high-risk clones, indicating that these clones likely have a competitive advantage and that the mutations within these clones have a cooperative biological interaction which underlies this competitive advantage. To investigate this hypothesis, we examined whether patterns of recurrent co-mutations were identifiable in this cohort. We identified recurrent co-occurring mutations in SRSF2-IDH2, ASXL1-SRSF2, and ASXL1-EZH2, which were enriched at transformation (p &lt; 0.05; Fisher exact test). Importantly, presence of these co-mutations was consistently associated with a higher transformation risk in two independent cohorts of MF patients from Mayo Clinic (n= 405) and University of Florence (n= 518). Clonal architecture analysis revealed that these co-mutations frequently arose within the same malignant clone: 100% for SRSF2-IDH2, 80% for ASXL1-SRSF2, and 85% for ASXL1-EZH2, and are often within the dominant clone. Prior studies have demonstrated mechanistic interplay between SRSF2 and IDH2 (Yoshimi et al.), as well as ASXL1 and SRSF2 (Sui et al.) mutations in myeloid malignancies. We assessed functional synergy of ASXL1-EZH2 using a murine model and identified that dual deletion caused a rapidly fatal myeloid neoplasm with shorter survival than single-gene loss. These results reinforce that functional synergy emerges within a shared cellular context, supporting clonal co-dependence as a driver of progression. Single-cell RNA sequencing of ASXL1-EZH2 mutant cases is ongoing and will be presented. Conclusions Transformation to MPN-AP/BP is driven by gradual clonal remodeling and expansion of pre-existing high-risk abnormalities. Biallelic and multi-hit alterations in TP53, JAK2, TET2, and RUNX1 promote clonal dominance and blast progression. Recurrent co-mutations such as IDH2-SRSF2 and ASXL1-EZH2 arise within the same clone, exhibit functional synergy, and are linked to poor outcomes. These findings support the clinical utility of early clonal and co-mutational profiling to guide risk-adapted intervention.

Abstract Background: Paroxysmal nocturnal hemoglobinuria (PNH) is a clonal hematopoietic stem cell disorder caused by PIGA mutations, resulting in complement-mediated hemolysis. Myeloproliferative neoplasms (MPNs) are also clonal hematopoietic stem cell disorders driven by mutations in JAK2, CALR, or MPL, and include polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). Although the coexistence of PNH and and MPN is rare, both entities are associated with anemia and an increased risk of thrombosis. The main objectives of the current study were to characterize the clinical phenotype, and evaluate treatment outcomes, including response to complement inhibition in patients with treatment-requiring PNH and MPN. Methods A Mayo Clinic database search was conducted following Institutional Review Board approval to identify patients with PNH and MPN. Between January 2000 and June 2025, 11 patients had a pathologic diagnosis of MPN per the International Consensus Classification, and PNH requiring therapy. Clinical features, including hemolysis and/or thrombosis, and therapeutic interventions were recorded. Results The study cohort included 11 patients (median age 63 years; 73% male) with MPN (6 with PMF, 3 ET, 1 post-ET MF, 1 post-PV MF) in whom PNH was diagnosed concurrently (n=5), or at a median of 9 years (range: 2-15) after MPN diagnosis (n=6). In 9 of 11 patients (82%), PNH workup was initiated due to hemolytic anemia, while in 2 patients (18%), it was prompted by splanchnic vein thrombosis. Transfusion-dependent anemia was present in 6 patients (54%). PNH clone size [median (range)] was 0.58% (0–10.96%) in red blood cells, 18.82% (0–91.2%) in granulocytes, and 23.27% (0–98.8%) in monocytes. MPN driver mutations were evaluated in 8 cases and identified mutations in JAK2 (n=3), CALR (n=4), and MPL (n=1); two additional cases were negative for JAK2 mutations. Next-generation sequencing (NGS) was performed in 6 patients and identified ASXL1, ZRSR2, and SF3B1 mutations in one case each. Cytogenetic analysis (n =10) demonstrated an abnormal karyotype in 3 patients (30%), all of which were categorized as favorable risk. First-line treatment for PNH included complement inhibitors in 9 patients (82%), with eculizumab (n=8), or ravulizumab (n=1). Hemolysis was controlled in only 2 of 9 (22%) patients; one patient treated with eculizumab achieved durable anemia response for five years, while another patient achieved transfusion-independence within three months of eculizumab initiation. 3 patients underwent allogeneic stem cell transplant (ASCT); 2 achieved remission with follow up exceeding 5 years, while the third patient experienced PNH recurrence five months after transplant. The latter received donor lymphocyte infusions and eculizumab was re-initiated resulting in eradication of the PNH clone. Among patients treated with complement inhibitor and not transplanted, 4 of 6 (67%) were transfusion-dependent at last follow-up. Notably, one patient with PNH clone size of 91.19 % in granulocytes, had ongoing hemolysis despite dual complement blockade with ravulizumab and danicopan. Treatment for MPN included hydroxyurea (n=5), ruxolitinib (n=4), pacritinib (n=1), and erythropoiesis stimulating agents (n=4). Additionally, two patients each received antiplatelet therapy alone, anticoagulation alone, or a combination of antiplatelet and anticoagulation. During a median follow-up of 8.4 years (range, 0.6-19), 2 patients (18%) experienced venous thrombosis, and 1 patient (9%) had progression to myelodysplastic syndrome. 5 patients (45.5%) died, with sepsis as the cause of death in 3 cases. The incidence rate of thrombosis was 22 events per 1,000 person-years. Recurrent thrombosis was observed in a 68-year-old male with CALR-mutated MF and PNH clone size of 14.65% in granulocytes, with history of portal vein thrombosis. Despite treatment with eculizumab and low-molecular weight heparin, he developed thrombosis involving the inferior vena cava, common iliac, and hepatic veins. The second event occurred in a patient who developed deep venous thrombosis, while in remission and not on anticoagulation. Conclusion The current study represents the largest series of patients with treatment-requiring PNH and MPN. It reveals a predominance of MF patients, particularly among those harboring CALRmutations, and underlines suboptimal efficacy of complement inhibitor therapy in this patient population.

Abstract Background Blast transformation (BT) occurs in approximately 15-30% of patients with chronic myelomonocytic leukemia (CMML) and remains a leading cause of death. Allogeneic stem cell transplantation (ASCT) is currently the only treatment modality with the potential to cure the disease or prolong survival. Optimal timing of ASCT is critical for maximizing benefit while minimizing risks. To that end, contemporary risk models have focused on the prognostic relevance of individual, as opposed to concurrent mutations. In the current study, we looked into the possibility of prognostic prominence from concurrent mutations in predicting BT in CMML. Methods CMML diagnostic criteria were according to the International Consensus Classification (Arber et al. Blood 2022; 140:1200). A machine-learning hierarchical clustering algorithm was developed and tailored for patient stratification using survival outcomes and co-expression of genomic alterations. To reduce complexity and improve interpretability, we generalized each cluster using existing rule induction algorithms, including the in Trees framework and JRip. The final output was a set of mutation-based cluster definitions, each representing a distinct patient subgroup with similar survival trajectories. Competing risk analysis and cumulative incidence functions were used in downstream evaluation to validate the clinical distinctiveness of the clusters. For all survival analysis, patients were censored at the time of ASCT. Time-to-BT was calculated from the date of diagnosis to the date of BT or ASCT or last contact. BT-free-survival was calculated from the date of diagnosis to the date of BT, ASCT, death, or last contact. Results The core study cohort included 605 patients from the Mayo Clinic, USA, and the external validation cohort 501 patients from Humanitas Cancer Center, Milan, Italy. Using the patient cohort from the Mayo Clinic, machine-learning algorithms identified five molecular clusters with 3-year blast transformation (BT) rates ranging from 0% to 100% (AUC at 3 years 0.78): the order of molecular signature assignment (probability of BT/death from another cause) was i) PHF6MUT/ASXL1WT (0%/17% at 3 years; N=32), ii) NPM1MUT OR BCORMUT/ASXL1MUT OR SETBP1MUT/NRASMUT (48%/30% at 1 year; N=24), iii) RUNX1MUT/ASXL1MUT OR SRSF2MUT/NRASMUT OR EZH2MUT/ASXL1MUT OR SETBP1MUT OR BCORMUT (31%/55% at 3 years; N=132), iv) ASXL1MUT/TET2MUT OR DNMT3AMUT OR JAK2MUT (24%/28% at 3 years; N=123), and v) all other permutations (10%/40% at 3 years; N=294) [Figure 1]. Additional analysis confirmed significant differences in survival between RUNX1MUT/ASXL1MUT vs. RUNX1MUT/ASXL1WT (p&lt;0.01) OR RUNX1WT/ASXL1WT (p&lt;0.01) OR RUNX1WT/ASXL1MUT (p=0.038) [Figure 2]. Similar patterns of differences in survival were also documented for NRAS/SETBP1, ASXL1/EZH2, and NRAS/SRSF2 mutation combinations (Figure 2). A subsequent Cox regression analysis confirmed independent prognostic contributions from “PHF6MUT/ASXL1WT” (HR 5.43e-10; p&lt;0.01), NPM1MUT (HR 26.7; p&lt;0.01), “SETBP1MUT/NRASMUT” (HR 12.7; p&lt;0.01), BCORMUT(HR 5.8; p&lt;0.01), “RUNX1MUT/ASXL1MUT” (HR 2.3, p&lt;0.01), JAK2MUT (HR 2.1; p&lt;0.01), and “ASXL1MUT/TET2MUT” (HR 1.7; p=0.02). The prognostic relevance of “SETBP1MUT/NRASMUT”, “RUNX1MUT/ASXL1MUT”, NPM1MUT, and BCORMUT was validated in the external cohort from Italy (N=501). In the Mayo Clinic cohort, presence of any of the latter mutations was associated with 1-, 3-, and 5-year BT (death from other cause) rates of 27% (21%), 44% (49%), and 44% (51%), respectively (Figure 3). The corresponding values in the absence of high risk mutations were 7% (15%), 15% (37%), and 18% (52%) [Gray's p value &lt;0.01 for both BT and death from another cause; Figure 3]. Similarly, the 1-, 3-, and 5-year BT (death from another cause) rates in the Italian cohort were 21% (19%), 37% (47%), and 42% (54%) in the presence and 8% (14%), 20% (35%), and 24% (45%) in the absence of high-risk mutations (Gray's p value &lt;0.01 for BT and 0.18 for death from another cause; Figure 4). Conclusions In the current study, machine-learning algorithms enabled the discovery of concurrent mutations in CMML that were shown to be prognostically more significant than their individual constituents. Such prognostic interaction might have contributed to some of the discrepancies noted in current literature regarding the prognostic relevance of certain mutations in CMML and should be accounted for in the development of future risk models.

Abstract Background: Sickle cell crisis (SCC) remains a leading cause of emergency hospitalization and acute morbidity in patients with sickle cell disease (SCD). With improved survival in SCD, long-term complications such as cardiovascular disease are emerging as critical determinants of morbidity and mortality. While right-sided heart failure has been adequately explored in SCD, the specific contribution of the other cardiovascular conditions to in-hospital mortality during sickle cell crisis remains unclear. This study evaluates the independent impact of cardiovascular comorbidities on in-hospital mortality among adults hospitalized for SCC using a large, nationally representative database. Methods: This is a retrospective analysis involving adult hospitalizations (≥ 18 years old) for SCC in the United States (U.S) using 2016-2020 National Inpatient Sample (NIS). The study was exempt from institutional review board approval as the NIS database contains deidentified patient information. ICD-10 codes used for SCC were: D570, D5721, D5741, D5743, D5745, D5781. Regarding cardiovascular co-morbidities, the study considered age, race (African American, Hispanic and Caucasian), dyslipidemia, hypertension, diabetes mellitus (both type 1 and type 2), systolic and diastolic heart failure, rheumatic valvular heart disease, atrial fibrillation/flutter, pulmonary hypertension, chronic coronary artery disease and ischemic stroke. Univariable Cox regression was performed to identify independent predictors associated with in-hospital mortality among patients admitted for SCC. All factors from the univariable analysis with p&lt;0.20 were included in a multivariable Cox regression model. All the outcomes were adjusted for age, sex, and Charlson Comorbidity Index. P-value of ≤0.05 was used to determine statistical significance. Results: The current study analyzed 424,540 SCD patients admitted for SCC (mean age 32.4 years, 55.4% females), from 2016 to 2020. Each additional year of age beyond 18 was associated with a linearly increasing in-hospital mortality among study patients hospitalized for SCC. This observation was confirmed in both univariate (HR: 1.06, 95% CI: 1.05–1.07, p&lt;0.001) and multivariate (HR: 1.05, 95% CI: 1.04–1.06, p&lt;0.001) analyses. On univariable analysis of cardiovascular co-morbidities in patients admitted for SCC, a higher in-hospital mortality was conferred by: hypertension (HR: 2.40; 1.95 – 2.94, p &lt;0.01) dyslipidemia (HR: 2.38; 1.54 – 3.65, p &lt;0.01), diabetes mellitus (HR: 2.15; 1.51 – 3.04, p &lt;0.01, systolic (HR: 5.20; 3.70 – 7.31, p &lt;0.01) and diastolic heart failure (HR: 2.11; 1.44 – 3.08, p &lt;0.01), atrial fibrillation/flutter (HR: 5.43; 4.04 – 7.32, p &lt;0.01), pulmonary hypertension (HR: 2.34; 1.75 – 3.13, p &lt;0.01) and ischemic stroke (HR: 5.75; 3.30 – 10.03, &lt;0.01); a lower in-hospital mortality was associated with the African American race (HR: 0.43; 0.20 – 0.91, p = 0.027). In multivariable analysis of cardiovascular comorbidities that demonstrated nominal significance (p &lt; 0.20) in univariable analysis, only the following remained significantly associated with in-hospital mortality: systolic heart failure (HR: 2.65; 1.77 – 3.98, p &lt;0.01), atrial fibrillation/flutter (HR: 2.01; 1.41 – 2.8), p &lt;0.01) and ischemic stroke (HR: 3.25; 1.85 – 5.71, p &lt;0.01). Although not statistically significant in univariable analysis (p = 0.065), chronic coronary artery disease (CAD) emerged as a significant predictor of reduced in-hospital mortality in the multivariable analysis (HR: 0.52; 0.29–0.91, p = 0.021). Conclusions: Among adults hospitalized for sickle cell crises, select cardiovascular comorbidities, including systolic heart failure, atrial fibrillation/flutter, and ischemic stroke, were independently associated with significantly increased in-hospital mortality. Interestingly, CAD was linked to reduced mortality in adjusted models. This may be attributed to closer medical surveillance and management in patients with established CAD, along with the potential protective mechanism(s) in SCD that limit atherosclerotic progression — warranting further investigation

Abstract Background Red blood cell distribution width (RDW) is an easily accessible diagnostic test that is underutilized as a prognostic marker, despite substantial supporting evidence in cancer, cardiovascular diseases, and helathy individuals (Pilling et al. PLoS One 2028;13:e0203504). The association between increased RDW and incident acute myeloid leukemia (AML) has long been suggested in healthy volunteers (Abelson et al. Nature 2019;559:400). We looked into a similar phenomenon in patients with polycythemia vera (PV) or essential thrombocythemia (ET) in large discovery and validation cohorts. Methods A machine learning (ML) approach was used to identify baseline clinical and laboratory parameters that were predictive of leukemic transformation (LT) and develop a predictive model in a large discovery cohort of PV and ET, obtained from electronic medical records of the Maccabi Healthcare Services (MHS; Tel-Aviv, Israel). Receiver operating characteristic (ROC) curve analysis was utilized to determine the optimal cutoff points for RDW and other continuous variables. Mayo Clinic databases (Rochester, MN, USA) for patients with PV and ET were utilized as validation cohorts. Identification of study patients in the latter was according to the International Consensus Classification (Arber et al. Blood 2022; 140:1200) while in the discovery cohort was based on presence of ICD-9 codes associated with documentation of disease-specific driver mutations or treatment records that are consistent. Statistical analyses were performed using the R statistical software 4.4.2 (November, 2024) (Foundation for Statistical Computing, Vienna, Austria) or JMP Pro 18.0.0 software (SAS Institute, Cary, NC, USA). Results The discovery cohorts from Israel included 4,592 patients with PV (median age 63 yesrs; 53% males) and 5,968 with ET (median age 61 yesrs; males 40%). The validation cohorts from the Mayo Clinic included 633 PV (median 65 years; 51% males) and 634 ET (median 60 years; females 64%) patients that were informative for RDW. In the discovery cohorts, median follow-up (incident AML) was 6.1 years (N=186; 4.1%) in PV and 6.1 years (N=85; 1.4%) in ET. The corresponding values for the validation cohorts were 6.6 years (N=24; 3.8%) in PV and 7.0 years (N=22; 3.5%) in ET. Discovery cohort analyses Covariate-adjusted Cox regression analysis was utilized to estimate associations between LT and several clinical variables followed by machine learning pipeline to predict LT in a given timeframe. Conversion from continuous to binary variables was accompliched by ROC analysis. Shapley Additive exPlanations (SHAP) application enabled selection of top-ranked features that included RDW &gt;15.5 (HR 3.2, 1.1-9.5; p=0.03), age &gt;60 years (HR 2.9, 1.4-5.9; p&lt;0.01), and LDH &gt;1.5 x UNL (HR 4.0 (1.6-10.0; p&lt;0.01). A similar analysis in ET also identified RDW &gt;15.5 (p&lt;0.01), age &gt;60 years (p=0.01) as well as male sex (p=0.03) as independent risk factors for LT. Subseqent risk models based on these risk factors were externally validated and enabled robust risk stratification with 10-20 year AUCs rangening from 0.75 to 0.81. Validation cohorts analyses The indpendent predictive value of increased RDW observed in the discovery cohort was confirmed in the validation cohorts for both PV and ET and further gender-specified: female/male RDW ≥16.4/15.8 in PV (HR 7.9, 1.9-33.8) and ≥16.1/14.4 in ET (HR 5.1, 2.2-11.9). Also confirmed were the additional prognostic contributions of age ≥60 years (HR 3.8, 1.3-10.9) and LDH &gt;1.5 x UNL (HR 3.9, 1.6-9.4), in PV, while no other clinical risk factor showed RDW-independent prognostic relevance in ET. The prognostic impact of RDW in ET was most apparent in females (p&lt;0.01), as opposed to males (p=0.32) while the effect was equally significant in PV. In multivariable analysis that included previously recognized high risk mutations in PV (SRSF2) and ET (SF3B1), both RDW and mutations sustained significance (p&lt;0.05). Conclusion The current study identifies higher RDW as a key risk factor for LT in both PV and ET, independent of other clinical or genetic risk factors. Possible underlying mechanisms include increased RDW as a marker of long-term pre-leukemic clones with sub-clinical ineffective erythropoiesis or an associated pro-leukemic inflammatory state.

ABSTRACTBackgroundTricuspid regurgitation (TR) is a common occurrence in patients with heart failure (HF), and its role in disease progression has gained attention in recent years. Although TR can worsen clinical outcomes in HF patients, the impact of gender, racial, and socioeconomic factors remains largely unexplored. With growing evidence supporting the role of percutaneous interventions for the treatment of significant TR, understanding these disparities is more crucial than ever.MethodsData were extracted from the National Inpatient and National Readmission 2016–2020 Databases. We used ICD‐10 code I50 to identify the patients primarily admitted for HF and subdivided the cohort into two groups based on the presence or absence of TR. We performed multivariable logistic regression analysis to determine odds of the in‐hospital mortality and multivariable Cox regression analysis to assess the 30‐ and 90‐day hospital readmission in HF patients with and without TR. All the analyses were adjusted for age, gender, insurance status, Charlson comorbidity index, and hospital characteristics. STATA 16 software was used for analysis.ResultsThere was no difference in the in‐hospital mortality among HF patients with and without TR (OR: 1.04, 95% CI 0.94–1.16, p = 0.442) except in certain subgroups of HF patients. HF patients with TR were 6% more likely to have HF‐specific readmission in 30 days (HR: 1.06, 95% CI 1.00–1.13, p = 0.044) and 9% more likely to have HF‐specific readmission in 90 days (HR: 1.09, 95% CI 1.03–1.15, p = 0.002). Subgroup analysis revealed significant gender, racial, and socioeconomic disparities in the in‐hospital mortality and the readmission outcomes of HF patients with TR compared to those without TR.ConclusionIn our population‐based survey analysis, we observed significant gender, racial, and socioeconomic disparities in the clinical outcomes of HF patients with TR compared to those without TR.