Population-based cohort of 500 patients with Gaucher disease in Israel.
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2019-01
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Abstract
Objective
To characterise a population-based cohort of patients with Gaucher disease (GD) in Israel relative to the general population and describe sociodemographic and clinical differences by disease severity (ie, enzyme replacement therapy [ERT] use).Design
A cross-sectional study was conducted.Setting
Data from the Clalit Health Services electronic health record (EHR) database were used.Participants
The study population included all patients in the Clalit EHR database identified as having GD as of 30 June 2014.Results
A total of 500 patients with GD were identified and assessed. The majority were ≥18 years of age (90.6%), female (54.0%), Jewish (93.6%) and 34.8% had high socioeconomic status, compared with 19.0% in the general Clalit population. Over half of patients with GD with available data (51.0%) were overweight/obese and 63.5% had a Charlson Comorbidity Index ≥1, compared with 46.6% and 30.4%, respectively, in the general Clalit population. The majority of patients with GD had a history of anaemia (69.6%) or thrombocytopaenia (62.0%), 40.4% had a history of bone events and 22.2% had a history of cancer. Overall, 41.2% had received ERT.Conclusions
Establishing a population-based cohort of patients with GD is essential to understanding disease progression and management. In this study, we highlight the need for physicians to monitor patients with GD regardless of their ERT status.Type
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Jaffe, Dena H, Natalie Flaks-Manov, Arriel Benis, Hagit Gabay, Marco DiBonaventura, Hanna Rosenbaum, Alain Joseph, Asaf Bachrach, et al. (2019). Population-based cohort of 500 patients with Gaucher disease in Israel. BMJ open, 9(1). p. e024251. 10.1136/bmjopen-2018-024251 Retrieved from https://hdl.handle.net/10161/33081.
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Arriel Benis
Dr. Arriel Benis is a researcher and educator working at the intersection of medical informatics, digital health, and artificial intelligence, advancing health systems and biomedical engineering innovation. His work leverages AI, data science, and knowledge management to improve health-related decision-making at the individual, population, and public health levels.
His research focuses on developing data-driven healthcare solutions that enhance patient care, optimize clinical processes, and promote sustainable systems. Dr. Benis has engineered (a) clinical decision support systems with direct patient and healthcare partitioners impact such as ADHD, PTSD, and diabetes patient management and health communication, (b) MIMO -the Medical Informatics and Digital Health Multilingual Ontology- integrating more than 3500 terms and concepts across 30+ languages, actively deployed in healthcare organizations for AI-powered training and international projects support, (c) smart home and smart city health monitoring approach from a One Health viewpoint. Dr. Benis is a pioneer of the One Digital Health framework, which strategically links digital health innovation with environmental monitoring.
His past academic positions include serving as a department head and track director in biomedical and health informatics. He holds various leadership roles in the international medical informatics community, is a fellow of the International Academy for Health Sciences Informatics, and is the Editor-in-Chief of JMIR Medical Informatics. Dr. Benis is committed to training the next generation of innovators in digital health and medical informatics.
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