11:19am Wednesday 20 September 2017

Personalizing Personalized Medicine By Applying “Patients-Like-Me” Statistics

Washington, D.C.—The use of statistics in medicine would seem to contradict the individualized care touted by doctors in this new generation of tailored treatments, but researchers at Georgetown University Medical Center (GUMC) say a new mathematical model examining treatment options patient-by-patient may be the odds-on favorite for advancing personalized medicine.

Personalized medicine, sometimes called systems medicine, is a holistic approach to a patient’s care that incorporates the basic tenets of evidence-based medicine along with the interactions between all components of health and disease including human genetics, environment and behavior.

For this kind of translational research to succeed, “clinicians need to have tools that can inform them regarding the level of confidence in recommendations for a specific patient,” says Farrokh Alemi, PhD, a professor in the Department of Health Systems Administration within the School of Nursing & Health Studies (NHS) at GUMC.

Alemi and his research colleagues propose a statistical tool that sequentially examines the most similar patients until a pre-determined stopping criterion is met. This procedure allows statistical inferences to be made one patient at a time.

“In contrast, traditional statistics focuses on the likelihood of success for the average patient,” Alemi and co-authors say in a paper titled, “Accessible Databases and Improved Statistical Methods are Needed to Advance Personalized Medicine,” published October 30, 2009, in The Open Translational Medicine Journal.

Alemi serves as the lead author on the article. Other authors include Charles H. Evans, Jr., MD, PhD, former chair of the Department of Human Science at NHS; Igor Griva, PhD, assistant professor in the Department of Computational and Data Sciences at George Mason University; and Harold Erdman, PhD, research associate in the Department of Health Systems Administration at Georgetown.

A “patients-like-me” algorithm may be a better approach to personalized medicine because it allows detection of cases in which the average recommendation is no longer valid, the authors say. They conclude that this is only one approach and that many more statistical models are possible.

“The challenge to statisticians is to design tools that can work well with the spirit of personalized medicine,” the authors note.

George Mason University has applied for a patent for the algorithm described in this paper on which Alemi is an inventor.

About the School of Nursing & Health Studies

Georgetown University School of Nursing & Health Studies—a part of Georgetown University Medical Center—translates science into outcomes that benefit the public’s health. NHS lives its mission “to improve the health and well being of all people” through innovative educational and research programs. The school houses a multi-million dollar research portfolio and includes the Departments of Health Systems Administration, Human Science, International Health, and Nursing, as well as the Center on Health and Education and—in partnership with Georgetown University Law Center—the Linda and Timothy O’Neill Institute for National and Global Health Law. Visit nhs.georgetown.edu.

About Georgetown University Medical Center

Georgetown University Medical Center is an internationally recognized academic medical center with a three-part mission of research, teaching and patient care (through Georgetown’s affiliation with MedStar Health). GUMC’s mission is carried out with a strong emphasis on public service and a dedication to the Catholic, Jesuit principle of cura personalis — or “care of the whole person.” The Medical Center includes the School of Medicine and the School of Nursing and Health Studies, both nationally ranked, the world-renowned Lombardi Comprehensive Cancer Center and the Biomedical Graduate Research Organization (BGRO), home to 60 percent of the university’s sponsored research funding.

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CONTACT:

Bill Cessato
202-687-7936
cessatow@georgetown.edu


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