MedicalResearch.com Interview with:
Douglas Krakower, MD
Infectious Disease Division
Beth Israel Deaconess Medical Center
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: There are 45,000 new HIV infections in the US annually, so effective HIV prevention strategies are needed. HIV pre-exposure prophylaxis (PrEP), whereby a person who is HIV-uninfected uses an HIV treatment medication on a daily basis to protect themselves from becoming infected with HIV, is over 90% effective when taken with high adherence. The Centers for Disease Control and Prevention estimates that there are 1.2 million Americans who are likely to benefit from using PrEP. However, only 80,000 persons have been prescribed PrEP. One of the barriers to implementing PrEP is that clinicians face challenges with identifying persons who are most likely to benefit from PrEP, given infrequent sexual health history assessments during routine clinical care. We thus sought to develop an automated algorithm that uses structured data from electronic health records (EHRs) to identify patients who are most likely to benefit from using PrEP. Our methods included extracting potentially relevant EHR data for patients with incident HIV and without HIV from nearly a decade of EHR data from a large ambulatory practice in Massachusetts. We then used machine learning algorithms to predict HIV infection in those with incident HIV and those without HIV. We found that some algorithms could offer clinically useful predictive power to identify persons who were more likely to become infected with HIV as compared to controls. When we applied these algorithms to the general population and identified a subset of about 1% of the population with risk scores above an inflection point in the total distribution of risk scores; these persons may be appropriate for HIV testing and/or discussions about PrEP.
MedicalResearch.com: What should readers take away from your report?
Response: Electronic health records data offer a rich source of information about identifying persons who may benefit from PrEP or other HIV prevention strategies. Automated algorithms can potentially identify these persons and be used to screen large populations efficiently and effectively.
MedicalResearch.com: What recommendations do you have for future research as a result of this study?
Response: Studies to test the impact of these algorithms on clinical care, such as PrEP utilization, are warranted.
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ID Week abstract: Presented October 2016
Automated Identification of Potential Candidates for HIV Pre-Exposure Prophylaxis using Electronic Health Record Data
Douglas Krakower, MD1, Susan Gruber, PhD2, John T. Menchaca, BA3, Judith C. Maro, PhD, MS2, Noelle Cocoros, DSc, MPH4, Benjamin Kruskal, MD, PhD5, Ira B. Wilson, MD, MSc6, Kenneth Mayer, MD7 and Michael Klompas, MD, MPH, FRCPC, FIDSA3, (1)Infectious Disease Division, Beth Israel Deaconess Medical Center, Boston, MA, (2)Department of Population Medicine, Harvard Medical School, Boston, MA, (3)Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, (4)Department of Population Medicine, Harvard Medical School & Harvard Pilgrim Health Care Institute, Boston, MA, (5)Atrius Health, Somerville, MA, (6)Health Services, Policy and Practice, Brown University, Providence, RI, (7)The Fenway Institute, Fenway Health, Boston, MA
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