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Parsimonious versus extensive bleeding score: can we simplify risk stratification after percutaneous coronary intervention and reduce bleeding events by de-escalation of the antiplatelet strategy?
Open Heart
January 2025
Cardiology, Radboudumc, Nijmegen, The Netherlands
Background And Aims: Due to the multitude of risk factors outlined in the guidelines, personalised dual antiplatelet therapy (DAPT) guidance after percutaneous coronary intervention (PCI) is complex. A simplified method was created to facilitate the use of risk stratification. We aimed to compare the predictive and prognostic value of the 'Zuidoost Nederland Hart Registratie' (ZON-HR) classification for bleeding risk with the PREdicting bleeding Complications In patients undergoing Stent implantation and subsEquent DAPT (PRECISE-DAPT) score and to determine the effect of ticagrelor monotherapy versus DAPT in patients with or without high bleeding risk (HBR).
View Article and Find Full Text PDFProspective Validation of CAR-HEMATOTOX and a simplified version predict Survival in Patients with Large B-Cell Lymphoma treated with anti-CD19 CAR T-cells: data from CART-SIE study.
Transplant Cell Ther
January 2025
Chair of Hematology, University of Milan; Division of Hematology and Stem Cell Transplantation, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano.
Background: Anti-CD19 CAR T-cells have revolutionized outcomes in relapsed/refractory large B-cell lymphomas. Long-term follow-up underscored the role of hematological toxicity in non-relapse mortality, largely driven by infections, leading to the development of the CAR-HEMATOTOX (HT) score for predicting neutropenia. The European scientific community (EHA/EBMT) later reached a consensus, defining a new entity: immune effector cell-associated hematotoxicity (ICAHT).
View Article and Find Full Text PDFKI-67 as a predictive indicator of papillary thyroid cancer in Iraqi patients.
Probl Endokrinol (Mosk)
March 2024
Background: KI-67 (MKI-67 in humans) is a protein able to bind to DNA which contributes to cell growth and cell proliferation. KI-67 is currently considered as a biomarker that is widely utilized as prognostic indicator for evaluating cell proliferation, diagnosing diseases, and conducting research. Several different kinds of cancer have high Ki-67 expression, which simplifying the choice of treatment for individuals with various cancer types.
View Article and Find Full Text PDFEfficient Discovery of Robust Prognostic Biomarkers and Signatures in Solid Tumors.
Cancer Lett
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State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 102206, Beijing, China; International Academy of Phronesis Medicine (Guangdong), 510320, Guangdong, China. Electronic address:
Recent advancements in multi-omics and big-data technologies have facilitated the discovery of numerous cancer prognostic biomarkers and gene signatures. However, their clinical application remains limited due to poor reproducibility and insufficient independent validation. Despite the availability of high-quality datasets, achieving reliable biomarker identification across multiple cohorts continues to be a significant challenge.
View Article and Find Full Text PDFPrioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data.
J Clin Med
January 2025
Clinical Trial and Biostatistics, Research and Innovation Unit, University Hospital of Ferrara, 44124 Ferrara, Italy.
A machine learning prognostic mortality scoring system was developed to address challenges in patient selection for clinical trials within the Intensive Care Unit (ICU) environment. The algorithm incorporates Red blood cell Distribution Width (RDW) data and other demographic characteristics to predict ICU mortality alongside existing ICU mortality scoring systems like Simplified Acute Physiology Score (SAPS). The developed algorithm, defined as a Mixed-effects logistic Random Forest for binary data (MixRFb), integrates a Random Forest (RF) classification with a mixed-effects model for binary outcomes, accounting for repeated measurement data.
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