-----Mensagem original-----
De: SBIS-News@yahoogrupos.com.br [mailto:SBIS-News@yahoogrupos.com.br] Em nome de Renato M.E. Sabbatini, PhD
Enviada em: quarta-feira, 27 de abril de 2011 06:56
Para: SBIS-News@yahoogrupos.com.br
Assunto: [SBIS-News] Mineração de dados do PEP identifica pistas genéticas para doenças
Resumo: O recrutamento de milhares de pacientes para coletar dados de
saúde para indícios genéticos de uma doença é caro e demorado, mas pode
ser mais rápido e mais barato quando se usa a técnica de mineração de
dados existentes nos registros médicos eletrônicos, de acordo com nova
pesquisa da Northwestern University nos EUA. Neste estudo, usando as
informações de pacientes em PEPs com consultas médicas de rotina em
cinco hospitais permitiram aos pesquisadores identificar corretamente
pacientes com cinco tipos de doenças: diabetes tipo 2, demência, doença
arterial periférica, cataratas e arritmias cardíacas. Foi obtida
precisão de 73 a 98%. As instituições que participaram do estudo fazem
parte de um consórcio chamado Electronic Medical Records and Genomics
Network.
Fonte: http://www.sciencedaily.com/releases/2011/04/110420143654.htm
Resumo por Renato M.E. Sabbatini
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Mining Data from Electronic Records: Faster Way to Get Genetic Clues to
Disease
ScienceDaily (Apr. 20, 2011) - Recruiting thousands of patients to
collect health data for genetic clues to disease is expensive and time
consuming. But that arduous process of collecting data for genetic
studies could be faster and cheaper by instead mining patient data that
already exists in electronic medical records, according to new
Northwestern Medicine research.
In the study, researchers were able to cull patient information in
electronic medical records from routine doctors' visits at five national
sites that all used different brands of medical record software. The
information allowed researchers to accurately identify patients with
five kinds of diseases or health conditions -- type 2 diabetes,
dementia, peripheral arterial disease, cataracts and cardiac conduction.
"The hard part of doing genetic studies has been identifying enough
people to get meaningful results," said lead investigator Abel Kho,
M.D., an assistant professor of medicine at Northwestern University
Feinberg School of Medicine and a physician at Northwestern Memorial
Hospital. "Now we've shown you can do it using data that's already been
collected in electronic medical records and can rapidly generate large
groups of patients."
The paper is published April 20 in Science Translational Medicine.
To identify the diseases, Kho and colleagues searched the records using
a series of criteria such as medications, diagnoses and laboratory
tests. They then tested their results against the gold standard --
review by physicians. The physicians confirmed the results, Kho said.
The electronic health records allowed researchers to identify patients'
diseases with 73 to 98 percent accuracy.
The researchers also were able to reproduce previous genetic findings
from prospective studies using the electronic medical records. The five
institutions that participated in the study collected genetic samples
for research. Patients agreed to the use of their records for studies.
Sequencing individuals' genomes is becoming faster and cheaper. It soon
may be possible to include patients' genomes in their medical records,
Kho noted. This would create a bountiful resource for genetic research.
"With permission from patients, you could search electronic health
records at not just five sites but 25 or 100 different sites and
identify 10,000 or 100,000 patients with diabetes, for example," Kho said.
The larger the group of patients for genetic studies, the better the
ability to detect rarer affects of the genes and the more detailed
genetic sequences that cause a person to develop a disease.
The study also showed across-the-board weaknesses in institutions'
electronic medical records. The institutions didn't do a good job of
capturing race and ethnicity, smoking status and family history, all
which are important areas of study, Kho said. "It shows we need to focus
our efforts to use electronic medical records more meaningfully," he added.
The institutions participating in the study are part of a consortium
called the Electronic Medical Records and Genomics Network.
The research was supported by the National Human Genome Research
Institute with additional funding from the National Institute of General
Medical Sciences.
1. A. N. Kho, J. A. Pacheco, P. L. Peissig, L. Rasmussen, K. M. Newton,
N. Weston, P. K. Crane, J. Pathak, C. G. Chute, S. J. Bielinski, I. J.
Kullo, R. Li, T. A. Manolio, R. L. Chisholm, J. C. Denny. Electronic
Medical Records for Genetic Research: Results of the eMERGE Consortium.
Science Translational Medicine, 2011; 3 (79): 79re1 DOI:
10.1126/scitranslmed.3001807
http://stm.sciencemag.org/content/3/79/79re1
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