NEW EVOLUTION IN MEDICINE: “PRECISION CUSTOM DIAGNOSIS AND TREATMENT” VIA DATA MINING

Posted on November 3, 2011 by

A new data network that incorporates real time information on emerging research on the molecular makeup of diseases with clinical data on individual patients is expected to lead quickly to the development of more accurate classification of disease and enhancement of diagnosis and treatment. These are the conclusions of a new report from the National Research Council. The “new taxonomy” that will emerge from the new data will define diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms. This unique aspect is what will help researchers understand more quickly what is causing disease and what is needed to use the body’s own biological mechanisms, developed over millions of years, to manage or cure the disorder. The report adds that the new data network will significantly improve biomedical research by enabling scientists to access patients’ information during treatment while still protecting their rights. This merger of molecular research and clinical findings at the point of care, as opposed to research information continuing to reside primarily in academia is now a hallmark of a new evolution in medicine. The new model will dissolve the present disconnect between the scientific advances in research and the information learned in the clinic through actual treament of patients. The report was co-authored by Susan Desmond-Hellmann, of the University of California, San Francisco.

“Developing this new network and the associated classification system will require a long-term perspective and parallels the challenges of building Europe’s great cathedrals — one generation will start building them, but they will ultimately be completed by another, with plans changing over time,” said committee co-chair Charles Sawyers, a Howard Hughes Medical Institute investigator and the inaugural director of the Human Oncology and Pathogenesis Program at Memorial Sloan-Kettering Cancer Center. “Dramatic advances in biology and technology have enabled rapid, comprehensive, and cost-efficient analysis of patients’ health information, which has resulted in an explosion of data that could dramatically alter disease classification. Health care costs have also steadily increased without translating into significantly improved clinical outcomes. These circumstances make it a perfect time to modernize disease classification.”

Typically, disease taxonomy refers to the International Classification of Diseases (ICD), a system established more than 100 years ago that is used to track and diagnose disease and determine reimbursement for care. Under ICD, which is in its 10th edition, disease classifications are primarily based on signs and symptoms and seldom incorporate rapidly emerging molecular data, incidental patient characteristics, or socio-environmental influences on disease.

This approach may have been adequate in an era when treatments were largely directed toward symptoms rather than underlying causes, but diagnosis based on traditional signs and symptoms alone carries the risk of missing or misclassifying diseases, the committee said. For instance, symptoms in patients are often nonspecific and rarely identify a disease unambiguously, and numerous diseases, such as cancer and HIV infection, are asymptomatic in the early stages. Moreover, many subgroups of certain diseases have diverse molecular causes and are classified as one disease and, conversely, multiple diseases share a common molecular cause and are not categorized in the same disease classification.The new database will involve a framework for creating a “knowledge network of disease” that integrates the rapidly expanding range of information on what causes diseases and allows researchers, health care providers, and the public to share and update this information. The first stage in developing the network would involve creating an “information commons” that links layers of molecular data, medical histories, including information on social and physical environments, and health outcomes to individual patients. The second stage would construct the network and require data mining of the information commons to highlight the data’s interconnectedness and integrate it with evolving research. Fundamentally, data would be continuously deposited by the research community and extracted directly from the medical records of participating patients.

To acquire information for the knowledge network, the committee recommended designing strategies to collect and integrate disease-relevant information; implementing pilot studies to assess the feasibility of integrating molecular parameters with medical histories in the ordinary course of care; and gradually eliminating institutional, cultural, and regulatory barriers to widespread sharing of individuals’ molecular profiles and health histories while still protecting patients’ rights. Much of the initial work necessary to develop the information commons should take the form of observational studies, which would collect molecular and other patient data during treatment. Having this access at point of care could reduce the cost of research, make scientific advances relevant to real-life medicine, and facilitate the use of electronic health records.

The committee noted that moving toward individualized medicine requires that researchers and health care providers have access to very large sets of health and disease-related data linked to individual patients. These data are also critical for developing the information commons, the knowledge network of disease, and ultimately the new taxonomy.

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