Abstract
Background:
Hypomethylating agents (HMAs) alone or in combination with venetoclax (VEN) offer a lower-intensity and tolerable treatment option for older and unfit acute myeloid leukemia (AML) patients. However, despite improved response rates, early mortality remains problematic. In the VIALE-A trial (DiNardo et al., 2020), nearly 20% of patients receiving either HMA alone or HMA-VEN died within 90 days, often due to treatment-related complications. High morbidity and mortality within the first three cycles underscore the need to identify patients at highest-risk for early treatment-related death to better tailor therapy.
Methods:
We performed a retrospective cohort study to identify clinical and molecular predictors of 90-day mortality in patients treated with HMA ± VEN between 2016 and 2025; 592 patients were identified by querying AML ICD-10-CM codes within the Northwell Health EMR. A cohort of 338 patients with AML (defined by the 2022 International Consensus Classification) received upfront HMA ± VEN induction (~76% received HMA+VEN). Univariate analysis (Mann-Whitney, chi-square, Fisher's exact tests) was conducted across 100 variables; p < 0.25 was used in the selection process for building the multivariable logistic regression model. Variables included age, ECOG performance status (PS), AML subtype, initial bone marrow findings, Day –7 to Day +1 pre-treatment transfusion dependence, laboratories, comorbidities, and various aspects of social support. In the multivariable analysis, adjusted odds ratios (aOR) and 95% confidence intervals (CI) were reported to quantify the strength and direction of associations. P<0.05 was considered statistically significant.
Results:
Among 338 patients (median age 79.9 years; range 42–102; 48% female; 50% White), the overall 90-day mortality rate was 34%. Univariate analysis identified 14 variables associated with early mortality: ECOG PS ≥ 2 (n=338; p < 0.001), adequate social support (n=260; p = 0.090), chronic kidney disease (Stage IIIb-V) (n=37; p = 0.085), dementia (n=14; p = 0.085), obstructive sleep apnea (n=15; p = 0.113), complex karyotype (n=127; p = 0.026), KMT2A rearrangement by FISH (n=19; p = 0.006), del(5q) by FISH (n=55; p = 0.112), blast % on marrow or peripheral flow cytometry (n=260; p = 0.034), increasing WBC count (n=334; p = 0.147) and LDH (n=313; p < 0.001) as continuous variables, RBC transfusion (n=135; p = 0.087) and platelet transfusion (n=134; p = 0.239) needs within 7 days of induction, and an active infection on day 1 of induction therapy (n=85; p<0.001). TP53 mutation status (n=78; p = 0.54) and age (n=338; p-value 0.219) were not significantly associated with 90-day mortality.
The final analysis identified five independent predictors of 90-day mortality: KMT2A rearrangement (aOR=3.4 [95% CI:1.1-10.1]), infection requiring hospitalization on day 1 of induction therapy (aOR=3.1 [95% CI:1.7-5.4]), ECOG PS (2 vs 0–1: aOR=2.9 [95%CI: 1.6-5.1]; 3–4 vs 0-1: aOR=4.9 [95% CI: 2.1-11.1]), complex karyotype (aOR=2.4 [95% CI:1.4-4.2]), and WBC (25-100 × 10⁹/L vs. 0-25: aOR=0.9 [95% CI: 0.5-1.9]; >100 × 10⁹/L vs. 0-25: aOR=5.9 [95% CI: 2.1-16.7]).
Discussion:
Previously these five predictors have been linked to poor HMA ± VEN outcomes. Elevated WBC ≥ 25 × 10⁹/L is linked to a high complication risk, VEN resistance, and early relapse and death (Maiti et al., 2021). KMT2A rearrangements are also associated with poor response rates and inferior survival (Montalban-Braco et al., 2020). Similarly, complex karyotype also confers a poor prognosis (Papaemmanuil et al., 2016).
Our data support the existing body of evidence that WBC count, KMT2A, and complex karyotype are associated with poor outcomes. We further add that these are associated with early death within 90-days. Interestingly, TP53 mutations, present in approximately 20% of our cohort, were not predictive of 90-day mortality in this population. As PS correlates with early mortality, most trials exclude patients ≥75 years old with an ECOG PS ≥3 (DiNardo et al., 2019; Wei et al., 2020). However, our data does not identify age as a predictor of outcome; therefore, future prospective clinical trials for older patients should prioritize PS over age for eligibility. Expansion of the dataset is ongoing to enhance model accuracy and evaluate additional predictors, with the aim of improving personalized care and minimizing harm in vulnerable AML populations.
