Data are needed on minimal factor activity (FA) levels required to prevent bleeding in hemophilia. We aimed to evaluate associations between hemophilia type and FA level and joint bleeding and orthopedic procedures using longitudinal data. Data were collected over an 11-year period on males with nonsevere hemophilia A or B without inhibitors who were receiving on-demand factor replacement therapy. Data on the number of joint bleeds in the previous 6 months and data on procedures from clinical records were analyzed using regression models. Data were collected on 4771 patients (hemophilia A, 3315; hemophilia B, 1456) from 19 979 clinic visits. Ages ranged from 2 to 91 years and baseline FA level ranged from 1% to 49% with a mean of 9.4%. Joint bleeding rates were heterogeneous across the FA range and were highest among men age 25 to 44 years. Adjusted for FA level, the mean number of joint bleeds per 6 months was 1.4 and 0.7 for patients with hemophilia A and B, respectively ( < .001). Regression models predicted 1.4 and 0.6 bleeds per year for hemophilia A and B patients, respectively, at an FA level of 15%. Patients with hemophilia B were 30% less likely than those with hemophilia A to have undergone an orthopedic procedure. We conclude that joint bleed rates for any given FA level were higher among hemophilia A than hemophilia B patients, and target FA levels of 15% are unlikely to prevent all joint bleeding in US males with hemophilia.
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http://dx.doi.org/10.1182/bloodadvances.2018020552 | DOI Listing |
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Department of Health Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom; Department of Geratology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
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January 2025
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital - via Manzoni 56, 20089 Rozzano, Milan, Italy. Electronic address:
Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficiencies. This review introduces AI, with a specific focus on machine learning (ML) methods, and examines its applications in the field of coagulation genetics over the past decade. We observed a significant increase in AI-related publications, with a focus on hemophilia A and B.
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Centre de Référence de l'Hémophilie, Hospices Civils de Lyon, UR4609 Universite Claude Bernard Lyon 1, Lyon, France. Electronic address:
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Haematology, Sydney Centres for Thrombosis and Haemostasis, Institute of Clinical Pathology and Medical Research (ICPMR), NSW Health Pathology, Westmead Hospital, Westmead, NSW 2145, Australia.
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Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan. Electronic address:
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