Venous thromboembolism is a common complication in patients with cancer, but only limited data are available in acute myeloid leukemia (AML). In a prospective study in a cohort of 272 adult patients (aged 18-65) and an independent validation cohort of 132 elderly adults (aged >60) with newly diagnosed AML, we assessed markers of disseminated intravascular coagulation (DIC) (fibrinogen, D-dimer, α-2-antiplasmin, antitrombin, prothrombin time, and platelet count) and the DIC score according the International Society of Thrombosis and Haemostasis and their associations with the occurrence of venous and arterial thrombosis during follow-up. The prevalence of thrombosis was 8.7% (4.7% venous, 4.0% arterial) in the younger adults over a median follow-up of 478 days and 10.4% (4.4% venous, 5.9% arterial) in elderly patients. Most thrombotic events (66%) occurred before the start of the second course of chemotherapy. The calculated DIC score significantly predicted venous and arterial thrombosis with a hazard ratio (HR) for a high DIC score (≥5) of 4.79 (1.71-13.45). These results were confirmed in the validation cohort of elderly patients with AML (HR 11.08 [3.23-38.06]). Among all DIC parameters, D-dimer levels are most predictive for thrombosis with an HR of 12.3 (3.39-42.64) in the first cohort and an HR of 7.82 (1.95-31.38) in validation cohort for a D-dimer >4 mg/L vs ≤4 mg/L. It is concluded that venous and arterial thrombosis may develop in ∼10% of AML patients treated with intensive chemotherapy, which to a large extent can be predicted by the presence of DIC at time of AML diagnosis.

Download full-text PDF

Source
http://dx.doi.org/10.1182/blood-2016-02-701094DOI Listing

Publication Analysis

Top Keywords

validation cohort
12
dic score
12
venous arterial
12
arterial thrombosis
12
disseminated intravascular
8
intravascular coagulation
8
acute myeloid
8
myeloid leukemia
8
elderly patients
8
thrombosis
7

Similar Publications

Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.

Eur J Nucl Med Mol Imaging

January 2025

Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.

Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.

Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.

View Article and Find Full Text PDF

Atherosclerosis is a major cause of morbidity and mortality worldwide; in Israel, ischemic heart disease is the second leading cause of death for both genders aged 45 and above. Atherosclerosis involves stiffening of the arteries due to the accumulation of lipids and oxidized lipids on the blood vessel walls, triggering the development of artery plaque. Coronary artery disease (CAD) is the most common manifestation of atherosclerosis.

View Article and Find Full Text PDF

Study Design: Retrospective cohort study using prospective database.

Objective: This study aimed to establish a risk-scoring system for predicting severe complications after pyogenic spondylodiscitis surgery.

Summary Of Background Data: Pyogenic spondylodiscitis surgery can cause severe complications.

View Article and Find Full Text PDF

Background: Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. We aimed to develop a model with multi-modal features (MMF) using artificial intelligence (AI) approaches to enhance the performance of HCC detection.

Materials And Methods: A total of 1,092 participants were enrolled from 16 centers.

View Article and Find Full Text PDF

Background: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!