Join our hosts Jessie Swain and Nicki Shorr as they talk with Dr. Geetika Sood. In this study Dr. Sood and colleagues explore if the risk of a patient getting Clostridioides difficile Infection (CDI) is higher if the room was previously occupied by a C. diff patient. Previous single-center studies suggest that exposure to a room previously occupied by a patient with C. diff infection (CDI) may increase the risk of C. diff infection in subsequent patients. Dr. Sood and colleagues evaluated the risk of previous room occupant on CDI risk across 5 adult hospitals.
Guest: Geetika Sood, M.D., Sc.M.
Article: Clostridioides difficile infection (CDI) in a previous room occupant predicts CDI in subsequent room occupants across different hospital settings.
Authors:
Geetika Sood
Shaun Truelove
Geoff Dougherty
B Mark Landrum
Sonia Qasba
Mayank Patel
Amanda Miller
Christina Wilson
John Martin
Cindy Sears
Alyson Schuster
Mark Sulkowski
Richard Bennett
Noya Galai
Geetika Sood, M.D., Sc.M.
Geetika Sood, M.D. ScM is an Assistant Professor of Medicine at Johns Hopkins University and Hospital Epidemiologist at Johns Hopkins Bayview Medical Center. She completed her medical training, residency in Internal Medicine and fellowship training in Infectious Diseases at Temple University Medical School. Dr. Sood served as an Associate Program Director and Student Clerkship Director in Internal Medicine at Albert Einstein Medical Center in Philadelphia, before moving to Abington Memorial Hospital where she was the Hospital Epidemiologist. She was recruited to Johns Hopkins University in 2011 and she has been the Hospital Epidemiologist at Johns Hopkins Bayview Medical Center where she led several successful process improvement interventions particularly in the burn intensive care unit for which she won the Armstrong Clinical Excellence Award in Patient Safety in 2015. She is a member of the Maryland Healthcare-associated Infections Advisory Board and has taught at the SHEA fellow’s course since 2015. Her research interests include using big data and machine learning algorithms to predict and ultimately mitigate the risk for developing healthcare-associated infections.