Validity of ICD‐10‐CM Diagnoses to Identify Hospitalizations for Serious Infections Among Patients Treated With Biologic Therapies

Basic Details
Thursday, April 22, 2021

The purpose of this analysis was to identify hospitalizations for serious infections among patients dispensed biologic therapies within healthcare databases which is important for post‐marketing surveillance of these drugs. We determined the positive predictive value (PPV) of an ICD‐10‐CM‐based diagnostic coding algorithm to identify hospitalization for serious infection among patients dispensed biologic therapy within the FDA's Sentinel Distributed Database.

We identified health plan members who met the following algorithm criteria: 1) hospital ICD‐10‐CM discharge diagnosis of serious infection between July 1, 2016 and August 31, 2018; 2) either outpatient/emergency department infection diagnosis or outpatient antimicrobial treatment within 7 days prior to hospitalization; 3) inflammatory bowel disease, psoriasis, or rheumatological diagnosis within one year prior to hospitalization, and 4) were dispensed outpatient biologic therapy within 90 days prior to admission. Medical records were reviewed by infectious disease clinicians to adjudicate hospitalizations for serious infection. The PPV (95% confidence interval [CI]) for confirmed events was determined after further weighting by the prevalence of the type of serious infection in the database.


Vincent Lo Re III, Dena M. Carbonari, Jerry Jacob, William R. Short, Charles E. Leonard, Jennifer G. Lyons, Adee Kennedy, Jolene Damon, Nicole Haug, Esther H. Zhou, David J. Graham, Cheryl N. McMahill‐Walraven, Lauren E. Parlett, Vinit Nair, Mano Selvan, Yunping Zhou, Gaia Pocobelli, Judith C. Maro, Michael D. Nguyen

Corresponding Author

Vincent Lo Re III, Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Center for Clinical Epidemiology and Biostatistics, Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.