Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

Antimicrobial drug use can contribute to the emergence of antimicrobial drug‐resistant organisms; therefore, judicious use of this important category of drugs is critical in both human and animal medicine to slow the development and spread of resistance. The US Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) is committed to advancing efforts to implement good antimicrobial stewardship practices in veterinary settings as part of our mission to protect human and animal health. In order to understand the drivers of resistance in veterinary settings and assess the impact of interventions designed to slow the development and spread of resistance, it is vital to have access to scientifically sound data on antimicrobial use and resistance. In 2016, FDA awarded funds in the form of cooperative agreements to support pilot projects for the collection of farm‐level antimicrobial use data in animal agriculture. These funded data collection efforts are intended to provide needed information on antimicrobial use practices in various animal production settings and to inform the development of long‐term strategies for collecting and reporting such data in a sustainable and nationally representative manner. Data were collected from records of participating dairy and feedlot cattle operations, swine companies, and broiler and turkey companies. Information from the first 2 years of the pilot projects is presented in this special issue, along with discussions related to challenges of collecting and reporting antimicrobial use data.

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BackgroundElectronic health records (EHRs) and big data tools offer the opportunity for surveillance of adverse events (patient harm associated with medical care). We chose the case of transfusion adverse events (TAEs) and potential TAEs (PTAEs) because 1.) real dates were obscured in the study data, and 2.) there was emerging recognition of new types during the study data period.ObjectiveWe aimed to use the structured data in electronic health records (EHRs) to find TAEs and PTAEs among adults.MethodsWe used 49,331 adult admissions involving critical care at a major teaching hospital, 2001-2012, in the MIMIC-III EHRs database. We formed a T (defined as packed red blood cells, platelets, or plasma) group of 21,443 admissions vs. 25,468 comparison (C) admissions. The ICD-9-CM diagnosis codes were compared for T vs. C, described, and tested with statistical tools.ResultsTAEs such as transfusion associated circulatory overload (TACO; 12 T cases; rate ratio (RR) 15.61; 95% CI 2.49 to 98) were found. There were also PTAEs similar to TAEs, such as fluid overload disorder (361 T admissions; RR 2.24; 95% CI 1.88 to 2.65), similar to TACO. Some diagnoses could have been sequelae of TAEs, including nontraumatic compartment syndrome of abdomen (52 T cases; RR 6.76; 95% CI 3.40 to 14.9) possibly being a consequence of TACO.ConclusionsSurveillance for diagnosis codes that could be TAE sequelae or unrecognized TAE might be useful supplements to existing medical product adverse event programs.