Leader, Avi

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.

Abstract Introduction: Though originating from different cell origins, several reports have described patients with the coexistence of chronic myeloid leukemia (CML) and paraproteinemias, mostly in patients treated with tyrosine kinase inhibitors (TKIs). Objective : To determine whether the prevalence of paraproteinemia in a large cohort of chronic phase (CP-) CML patients is higher than expected in the general population. Methods: We conducted a cross-sectional study among out-patient CP-CML patients treated in a tertiary medical center. Between February 2019 and May 2021 consecutive CP-CML patients who consented to the study, performed the following tests: quantitative immunoglobulin (Ig) levels, serum-free light chains (sFLC), serum protein electrophoresis (SPEP), and if abnormal also serum protein immunofixation (IFIX). The primary outcome was the prevalence of paraproteinemia among this cohort of CML patients. We conducted further analyses to evaluate whether various patient, disease and treatment-related variables are predictors of paraproteinemia within our cohort. Results: One hundred patients, 50% males, with a median age of 63.5 (range 26-91.6) years were recruited for the study. The median time from CML diagnosis to enrollment was 6.3 (range 0.1-23.5) years. Cohort's characteristics are depicted in table 1. Monoclonal (M-) protein was detected on SPEP and IFIX in eight patients (8%), whose median age was 66.8 (range 46-74) years. Among them, two patients had an abnormal sFLC, elevated IgG or IgA levels, on a bone marrow biopsy over 10% monoclonal plasma cells were present, and in the absence of myeloma defining events they were diagnosed with IgG-kappa and IgA-kappa smoldering multiple myeloma (SMM), respectively. The other six patients had normal sFLC ratio and characteristics compatible with low-risk monoclonal gammopathy of undetermined significance (MGUS). Six patients with a detectable M-protein were treated with a TKI [imatinib (n=2), dasatinib (n=3), bosutinib (n=1)] and two were in treatment-free remission (TFR). All continue observation for their paraproteinemia. None of the patients in our cohort was diagnosed with active MM. The presence of anemia was predictive of paraproteinemia, albeit with borderline statistical significance in both univariate (p=0.053) and multivariate analysis (p=0.069). Apart from anemia, no predictors for paraproteinemia were found among various other patient and disease related variables. Conclusions: This is the largest study describing the prevalence of paraproteinemia among CML patients. We found the prevalence of MGUS in CP-CML patients (6%) to be higher than in the general population above the age of 50 (3.2%), and a non-negligible prevalence of SMM (2%). Based on these data, we recommend considering screening for paraproteinemia coexistence in CML patients. Further studies and extended follow-up are needed to elucidate the true clinical significance of our findings. Figure 1 Figure 1. Disclosures Leader: Bayer: Honoraria; Leo Pharma: Honoraria; Novartis: Honoraria; Pfizer: Consultancy, Honoraria; Sanofi: Honoraria.