Hyfte, Grace Van

Abstract The incidence of venous thromboembolism (VTE) in patients with acute lymphoblastic leukemia (ALL) receiving asparaginase-based induction is high despite primary thromboprophylaxis. Our aim was to derive and externally validate a VTE risk prediction model in patients with ALL receiving asparaginase-based induction. We conducted a multicenter retrospective cohort study of patients (aged ≥18 years) with newly diagnosed ALL receiving asparaginase-based induction. The derivation and external validation cohorts included 306 and 94 patients, respectively. Primary outcome was VTE at any site. A cause-specific Cox proportional hazards model stratified by thromboprophylaxis and center was performed to identify VTE risk factors in the derivation cohort. A risk prediction model for VTE at 30 days was derived using variables with P value < .05 in the multivariable model and was tested in the validation cohort. VTE risk factors on multivariable analysis in the derivation cohort included D-dimer ≥1 μg fibrinogen equivalent unit per mL (hazard ratio [HR], 2.64; 95% confidence interval [CI], 1.07-6.5) and hemoglobin (HR for each 1 g/dL increment, 1.19; 95% CI, 1.06-1.34) at ALL diagnosis. A VTE risk score based on these variables distinguished between a 4% (95% CI, 0.72-12) and 20% (95% CI, 14-27) 30-day cumulative incidence of VTE in the derivation cohort, with similar findings in the validation cohort (area under the curve, 0.56). The negative predictive value for VTE at 30 days was 96% and 93% in the derivation and validation cohorts, respectively, and the positive predictive value was 20% in both. We derived and validated a model using D-dimer and hemoglobin, which stratifies VTE risk in patients with ALL receiving asparaginase-based induction.

Background: Patients with acute lymphoblastic leukemia (ALL) receiving L-asparaginase (ASP) based induction therapy have a high risk of venous thromboembolism (VTE). Studies investigating universal thromboprophylaxis strategies have yielded sub-optimal results. Risk prediction models are needed to identify high-risk patients who would be candidates for intensification of thromboprophylaxis, and lower risk patients who are unlikely to benefit. Aims: Derive and externally validate a risk prediction model for VTE in patients with ALL receiving ASP therapy. Methods: We conducted a multicenter, international, cohort study of patients with newly-diagnosed ALL receiving ASP-based induction therapy (≥18yrs). The derivation and external validation cohorts included 306 and 97 patients without VTE prior to ASP therapy, respectively. Patients on therapeutic anticoagulation at index were excluded. Patients were followed from date of first ASP dose for 100 days post ALL diagnosis or until VTE, therapeutic anticoagulation or death. Candidate predictors were documented at ALL diagnosis and included demographics, disease and treatment characteristics, VTE history, comorbidities, thromboprophylaxis, complete blood count, fibrinogen and d-dimer. D-dimer was only analyzed within the 7 derivation cohort centers who routinely tested this at ALL diagnosis with <20% missing values. Primary outcome was VTE at any site (centrally confirmed). Secondary outcomes included major bleeding by ISTH criteria. Cause-specific Cox proportional hazard regression was performed to identify VTE risk factors in the derivation cohort while treating death as a competing event. Variables with p-value <0.1 on univariable analysis were included in a multivariable model and subsequently used in the risk model if p-value <0.05. Lastly, the derived VTE risk prediction model was tested in the external validation cohort for association with time to VTE using cause-specific Cox proportional hazard model. All analyses were stratified by center and use of low molecular weight heparin prophylaxis (at any dose on the date of first ASP dose). Results: The cumulative incidence of VTE in the derivation cohort was 17% at 30 days (95% confidence interval [CI] 13% - 21%) and 22% at 90 days (95% CI 17% - 27%). The multivariable model included hyperlipidemia (hazard ratio [HR] 1.35; 95% CI 0.56 - 3.27), body mass index ≥ 25 kg/m² (HR 1.49; 95% CI 0.75 - 2.95), platelet count ≥ 25 x 10⁹/L (HR 1.42; 95% CI 0.67 - 3.00), increasing hemoglobin per 1 g/dL (HR 1.17; 95% CI 1.03 - 1.32) and d-dimer ≥ 1 µg FEU/mL (HR 2.68; 95% CI 1.08 - 6.66). Hemoglobin as a continuous variable and d-dimer ≥ 1 µg FEU/mL were included in the final model and used to create a risk score which was weighted by estimated log-HR sum of these risk factors. The lowest risk quartile (risk score below 2.1) successfully differentiated between a lower 30-day cumulative VTE incidence (4%, 95% CI 0.72% - 12%) compared with the other quartiles (20%, 95% CI 14% - 27%; HR 4.40, 95% CI 1.54 - 12.5) which were defined as high risk (score ≥2.1). The risk score's negative predictive value (NPV) for VTE at 30 days was 96% and positive predictive value (PPV) was 20%. The validation cohort (n=97 from 3 external cohorts with routine d-dimer measurement) included 16 (16.5%) patients in the low-risk group and 81 (83.5%) in the high-risk group, with a 30-day VTE incidence of 6.3% (95% CI 0% - 17%) and 21% (95% CI 12% - 30%), respectively (HR 6.22; 95% CI 0.85 - 45.8; p = 0.073)). The NPV (93.7%) and PPV (21%) were comparable to the derivation cohort. The 30-day incidence of major bleeding was 0% (95% CI NR - NR) in low-risk patients (HR 1.36; 95% CI 0.27 - 6.77) and 2.0% (95% CI 0.55% - 5.4%) in the high-risk group. Conclusion: Patients with ALL receiving ASP induction therapy have a 17% VTE risk at 30 days. We derived and externally validated a VTE risk prediction model based upon d-dimer and hemoglobin. This model successfully identifies at least 16.5% of patients with ALL who have a low VTE risk after ASP treatment. Using this model, novel prevention strategies which may be associated with cost and risk could now be tailored to patients with higher risk. Prospective studies are needed to validate the model's performance and role in VTE risk management.

Introduction HMAs are the only approved therapy for CMML. In order to improve response rates and survival, the addition of VEN has been utilized in both CMML and after blast transformation (CMML-BT) with conflicting results in small single center studies. To clarify the role of upfront HMA+VEN in CMML and CMML-BT, we performed a large multicenter analysis. Methods We included patients with HMA-naïve CMML and CMML-BT treated with combination HMA and VEN from 4 academic centers. The primary outcome was overall survival (OS) with secondary outcomes including overall response rate (ORR) by 2015 MDS/MPN IWG criteria for CMML and 2022 ELN criteria for CMML-BT. To better appreciate the benefit of adding VEN to HMA treatment, we also performed 1:1 propensity score matched (PSM) analysis of patients treated with HMA alone as upfront therapy for CMML or CMML-BT. Matching was based on age, sex, ECOG performance status, CPSS-Mol, blood counts, peripheral and bone marrow blasts. Results Eighty-nine patients were treated with frontline HMA+VEN, 51 CMML and 38 CMML-BT (5 had received HMA alone for prior CMML). The median age was 71 years, 70% were male, and 83% had ECOG 0-1. Among CMML patients, 55% had MP-CMML subtype, 65% had CMML-2 and 12%, 30%, 58% were CPSS-mol intermediate-1, intermediate-2 and high risk, respectively. In the entire cohort, ASXL1 (63%), TET2 (47%), SRSF2 (39%) and RUNX1 (27%) were the most commonly mutated genes and 55% of patients harbored a RAS mutation ( KRAS [20%], NRAS [18%], CBL [13%], NF1 [11%], PTPN11 [9%]). For HMA combination partner, 46 (52%) were treated with azacitidine, 35 (39%) with decitabine, and 8 (9%) with both during their VEN course. During a median follow-up time of 15.8 months (mos) 52 patients (58%) died: 23 (45%) with CMML and 29 (76%) with CMML-BT. Among CMML patients, the ORR was 90%, including complete remission (CR) in 22%, complete cytogenic remission (CCR) in 4%, optimal marrow response (CMR) in 50%, and partial marrow response (PMR) in 6%. The OS of CMML patients was 25.1 mo (95% CI 15.7 - NR). A PSM population of HMA patients matched to HMA+VEN treated patients showing similar baseline characteristics. ORR was significantly higher with HMA+VEN (95%) as compared those treated with HMA alone (46%), p < 0.001. However, there was no significant OS difference between HMA+VEN vs HMA alone (19.1 mos vs 19.1 mos, p = 0.85, Figure 1). For CMML-BT patients, the ORR of HMA+VEN was 81% including CR in 17%, CR with partial hematologic recover (CRh) in 3%, CR with incomplete hematologic recovery (CRi) in 28%, morphologic leukemia-free state (MLFS) in 22% and partial response (PR) in 11%. The OS of CMML-BT patients was 8 mos (95% CI 4.6 - 26.2). A PSM population of CMML-BT patients treated with HMA alone showed a significantly increased ORR with HMA+VEN (82% vs 40%, p = 0.039). However, there was no difference in OS (7.49 mos vs 9.26 mos, p = 0.83, Figure 2). Among HMA-VEN treated patients, achievement of a CR was associated with an improved OS in the CMML (HR 0.31, 95% CI 0.13 - 0.75) and CMML-BT (HR 0.37, 95% CI 0.19 - 0.74). No predictors of response were identified, including the presence of a RAS mutation. In terms of safety, 7 patients (7.9%) had a grade 3/4 (G3/4) neutropenic infection and 2 patients (2.2%) had a G3/4 hemorrhagic event while being treated with HMA+VEN. A total of 29 patients went on to receive allogeneic transplant (alloSCT): 22 (43%) with CMML and 7 (18%) with CMML-BT. CMML patients who underwent alloSCT after HMA+VEN were younger as compared to non-transplanted patients (64 vs 74 years, p < 0.0001), but had otherwise similar baseline characteristics including stage, CPSS-mol category and prevalence of RAS mutations. Similarly, CMML-BT patients bridged to alloSCT were younger (66 vs 74 years, p < 0.002) and had a better ECOG performance status (p = 0.047). Of patients who received alloSCT, 10 patients (34%) died, 5 with CMML and 5 with CMML-BT. The median OS after alloSCT was 26.7 mo. Conclusions Although the use of VEN with HMA in the upfront treatment of CMML and AML secondary to CMML results in higher response rates compared to HMA alone, this combination does not improve survival based on this multi-center PSM analysis. The current sample size of CMML-BT may limit detection of OS differences between treatment groups. The role of VEN to reduce blasts and serve as a bridge to alloSCT, particularly after HMA failure, requires further investigation in larger studies in both the CMML and CMML-BT populations.

Abstract We used a rigorous PRoBE (prospective-specimen collection, retrospective-blinded-evaluation) study design to compare the ability of biomarkers of systemic inflammation and biomarkers of gastrointestinal (GI) tissue damage to predict response to corticosteroid treatment, the incidence of clinically severe disease, 6-month nonrelapse mortality (NRM), and overall survival in patients with acute graft-versus-host disease (GVHD). We prospectively collected serum samples of newly diagnosed GVHD patients (n = 730) from 19 centers, divided them into training (n = 352) and validation (n = 378) cohorts, and measured TNFR1, TIM3, IL6, ST2, and REG3α via enzyme-linked immunosorbent assay. Performances of the 4 strongest algorithms from the training cohort (TNFR1 + TIM3, TNFR1 + ST2, TNFR1 + REG3α, and ST2 + REG3α) were evaluated in the validation cohort. The algorithm that included only biomarkers of systemic inflammation (TNFR1 + TIM3) had a significantly smaller area under the curve (AUC; 0.57) than the AUCs of algorithms that contained ≥1 GI damage biomarker (TNFR1 + ST2, 0.70; TNFR1 + REG3α, 0.73; ST2 + REG3α, 0.79; all P < .001). All 4 algorithms were able to predict short-term outcomes such as response to systemic corticosteroids and severe GVHD, but the inclusion of a GI damage biomarker was needed to predict long-term outcomes such as 6-month NRM and survival. The algorithm that included 2 GI damage biomarkers was the most accurate of the 4 algorithms for all endpoints.