Publications by authors named "J Claire Maciejewski"
Iptacopan, a first-in-class, oral, selective complement factor B inhibitor, demonstrated efficacy and safety as monotherapy in C5 inhibitor (C5i)-experienced (APPLY-PNH [NCT04558918]) and C5i-naive (APPOINT-PNH [NCT04820530]) patients with paroxysmal nocturnal hemoglobinuria (PNH). In APPLY-PNH and APPOINT-PNH, changes in fatigue (FACIT-Fatigue) and health-related quality of life (HRQOL; EORTC QLQ-C30) from baseline to Day 168 were evaluated. The proportion of patients achieving meaningful within-patient change (MWPC) on the FACIT-Fatigue and 4 EORTC QLQ-C30 subscales (physical functioning, role functioning, fatigue, dyspnea) was evaluated using anchor-based thresholds.
View Article and Find Full Text PDFBackground: To compare the effectiveness of four surveillance strategies for detecting SARS-CoV-2 within the homeless shelter population in Hamilton, ON and assess participant adherence over time for each surveillance method.
Methods: This was an open-label, cluster-randomized controlled trial conducted in eleven homeless shelters in Hamilton, Ontario, from April 2020 to January 2021. All participants who consented to the study and participated in the surveillance were eligible for testing by self-swabbing.
Introduction: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, acquired, non-malignant hematologic disease characterized by complement-mediated hemolysis (with or without hemoglobinuria), fatigue, increased susceptibility to thrombosis, and bone marrow dysfunction. The development of complement inhibitors has transformed outcomes for patients with PNH, but patients may still experience pharmacodynamic breakthrough hemolysis (BTH), which can be caused by exposure to a complement amplifying condition (CAC), such as vaccination, infection, or surgery.
Materials And Methods: A 13-member expert panel used a validated methodology (a RAND/UCLA modified Delphi panel) to develop consensus on how to classify pharmacodynamic BTH in patients with complement-inhibitor treated PNH.
Article Synopsis
- Artificial intelligence, particularly machine learning, is transforming biomedical research, especially in hematologic malignancies and myeloid neoplasia, through improved diagnostics and predictions.
- Despite promising results from ML applications, none have yet been fully adopted in clinical practice due to challenges like limited familiarity among hematologists and patient concerns over privacy and reliability.
- The review discusses the mechanisms and applications of machine learning, focusing on hematologic conditions, and aims to highlight both the strengths and limitations for future clinical use.