ArticleHaematologica2022
A risk score based on real-world data to predict early death in acute promyelocytic leukemia.
Article in Haematologica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 34 citations in OpenAlex.
- Article
- Deep Learning Identifies Abnormal Promyelocytes in Peripheral Blood Based on Morphological Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Establishment and validation of a 28-day clinical outcome prediction model for acute promyelocytic leukemia based on an early platelet-coagulation factor combined transfusion strategy.American journal of cancer research · 2026Article
- Multicenter validation of an explainable machine-learning model for APL, AMoL, and ALL.Frontiers in oncology · 2026Article
- Risk factors and management strategies in acute promyelocytic leukemia: a real-world single-center retrospective study.Frontiers in medicine · 2026Article
- Predictors of Early Death in Acute Promyelocytic Leukemia.Medical sciences (Basel, Switzerland) · 2025Review
- Development of a screening model for APL using cell population data and deep learning-extracted WBC scattergram features.BMC cancer · 2025Article
- Article
- Article
- RAPID-CRISPR: highly sensitive diagnostic assay for detection of PML::RARA isoforms in acute promyelocytic leukemia.Blood advances · 2025Article
- Study on clinical features of early death in older patients with acute promyelocytic leukemia.Annals of hematology · 2025Article
- Causes and risk factors for early death in adult patients with acute promyelocytic leukemia: a real-life experience.Hematology, transfusion and cell therapy · 2024Article
- Clinical characteristics and analysis of related factors associated with early death in newly diagnosed patients with acute promyelocytic leukemia.Discover oncology · 2024Article
- Hemorrhage in acute promyelocytic leukemia-fibrinolysis in focus.Research and practice in thrombosis and haemostasis · 2024Review
- Cellular hierarchy insights reveal leukemic stem-like cells and early death risk in acute promyelocytic leukemia.Nature communications · 2024Article
- Standard dose anthracycline plus all-trans retinoic acid and arsenic trioxide as induction chemotherapy significantly reduces early death and relapse for high-risk acute promyelocytic leukemia: a single-center real-world analysis.Therapeutic advances in hematology · 2024Article
- Risk stratification for early mortality in newly diagnosed acute promyelocytic leukemia: a multicenter, non-selected, retrospective cohort study.Frontiers in oncology · 2024Article
- How to avoid early mortality in acute promyelocytic leukemia.Hematology. American Society of Hematology. Education Program · 2023Article
- Effects of peripheral blood leukocyte count and tumor necrosis factor-alpha on early death in acute promyelocytic leukemia.BMC cancer · 2023Article
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Authors and funding
18 authors at 9 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
With increasingly effective treatments, early death (ED) has become the predominant reason for therapeutic failure in patients with acute promyelocytic leukemia (APL). To better prevent ED, patients with high-risk of ED must be identified. Our aim was to develop a score that predicts the risk of ED in a real-life setting. We used APL patients in the populationbased Swedish AML Registry (n=301) and a Portuguese hospital-based registry (n=129) as training and validation cohorts, respectively. The cohorts were comparable with respect to age (median, 54 and 53 years) and ED rate (19.6% and 18.6%). The score was developed by logistic regression analyses, risk-per-quantile assessment and scoring based on ridge regression coefficients from multivariable penalized logistic regression analysis. White blood cell count, platelet count and age were selected by this approach as the most significant variables for predicting ED. The score identified low-, high- and very high-risk patients with ED risks of 4.8%, 20.2% and 50.9% respectively in the training cohort and with 6.7%, 25.0% and 36.0% as corresponding values for the validation cohort. The score identified an increased risk of ED already at sub-normal and normal white blood cell counts and, consequently, it was better at predicting ED risk than the Sanz score (AUROC 0.77 vs. 0.64). In summary, we here present an externally validated and population-based risk score to predict ED risk in a real-world setting, identifying patients with the most urgent need of aggressive ED prevention. The results also suggest that increased vigilance for ED is already necessary at sub-normal/normal white blood cell counts.
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