Background And Methods: The present review aims to evaluate the current state-of-the-art dosing regimens of high-dose (HD) and intrathecal methotrexate (MTX) using therapeutic drug monitoring (TDM) to optimize its therapeutic response and minimize associated toxicity, particularly in the central nervous system (CNS).
Results: MTX is administered systemically in a HD regimen (>1 g/m 2 ) for the treatment of various hematological neoplasms. HD-MTX treatment becomes complicated by marked interindividual drug elimination variability. TDM is specified to manage this high variability. Approximately 3%-7% of adults with acute lymphoblastic leukemia are diagnosed with CNS involvement, and the incidence of CNS relapse in patients, despite receiving prophylaxis, ranges from 5% to 10%. HD-MTX penetrates the blood-brain barrier and can be administered intrathecally, making this drug an important component of chemotherapy regimens for patients with hematologic malignancies involving the CNS or those at high risk of CNS relapse.
Conclusions: The major evidence found was that an MTX area under the curve target between 1000 and 1100 μmol hour -1 L is associated with better clinical outcomes. However, there seems to be a clinical gap in the prospective validation of HD and IT MTX management to optimize clinical outcomes and minimize toxicity, using the relationship between exposure level (area under the curve MTX) and optimal response to MTX, at systemic and CNS exposure.
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http://dx.doi.org/10.1097/FTD.0000000000001022 | DOI Listing |
Arterioscler Thromb Vasc Biol
January 2025
Institute of Experimental Hematology and Transfusion Medicine, University Hospital Bonn, Germany.
Background: Clinical expressivity of the thrombophilic factor V Leiden (FVL) mutation is highly variable. Recently, we demonstrated an increased APC (activated protein C) response in asymptomatic FVL carriers compared with FVL carriers with a history of venous thromboembolism (VTE) after in vivo coagulation activation. Here, we further explored this association using a recently developed ex vivo model based on patient-specific endothelial colony-forming cells (ECFCs).
View Article and Find Full Text PDFStat Methods Med Res
January 2025
CITMAga and Department of Statistics and Operations Research, Universidade de Vigo, Vigo, Galicia, Spain.
The study of the predictive ability of a marker is mainly based on the accuracy measures provided by the so-called confusion matrix. Besides, the area under the receiver operating characteristic curve has become a popular index for summarizing the overall accuracy of a marker. However, the nature of the relationship between the marker and the outcome, and the role that potential confounders play in this relationship could be fundamental in order to extrapolate the observed results.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, No. 88 West Taishan Road, Zhuzhou 412007, Hunan, China.
Aims: The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.
Methods And Results: We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF).
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, 401 East River Parkway, Minneapolis, MN, USA.
Aims: Many studies have utilized data sources such as clinical variables, polygenic risk scores, electrocardiogram (ECG), and plasma proteins to predict the risk of atrial fibrillation (AF). However, few studies have integrated all four sources from a single study to comprehensively assess AF prediction.
Methods And Results: We included 8374 (Visit 3, 1993-95) and 3730 (Visit 5, 2011-13) participants from the Atherosclerosis Risk in Communities Study to predict incident AF and prevalent (but covert) AF.
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