Infection Control & Hospital Epidemiology


followed by prospective data mining of the EHR to rapidly iden- tify potential outbreaks and their routes of transmission. The pur- pose of this study was to develop and validate data mining tools using retrospective outbreaks, with the ultimate goal of utilizing these tools as a component of EDS-HAT.


Methods Study setting


This study was conducted at the University of Pittsburgh Medical Center-Presbyterian Hospital (UPMC), an adultmedical and surgical tertiary-care hospital with 762 total beds, 150 critical care unit beds, >32,000 yearly inpatient admissions, and >400 solid organ transplants per year. The UPMC eRecord EHR systemhas >29,000 active users, including >5,000 physicians affiliated with UPMC, and it comprises >3.6 million unique electronic patient records. UPMC uses Cerner Millenium PowerChart (Cerner, Kansas City, MO) and EpicCare (Epic Systems, Madison WI) as the backbone of its inpatientandoutpatientEHRsystems, respectively.TheUniversity of Pittsburgh Institutional Review Board approved this study.


Characterization of retrospective outbreaks from 2011 to 2016


The subject of this study were outbreaks that occurred during 2011–2016 and that had been previously characterized using molecular typing and traditional epidemiologic methods to iden- tify the transmission route. The routine infection prevention prac- tice at the time was to notify the Microbial Genomic Epidemiology Laboratory (MiGEL) of suspected outbreaks caused by bacterial pathogens so that molecular subtyping could be performed. For each patient suspected of being included in the outbreak, the bac- terial isolate was obtained from the clinical microbiology labora- tory. For Clostridium difficile, which is diagnosed at our institution by culture-independent diagnostic testing, the nucleic acid amplification test–positive stool specimen was cultured for C. difficile. For identification of the common exposure responsible for individual outbreaks, our infection prevention teamanalyzed the health records of patients included in the outbreak to identify the responsible routes of transmission (eg, shared locations/staff, shared procedures/operations, or shared medications). Some outbreak investigations utilized environmental or device cultures to confirm the route of transmission. The transmission route defined by infec- tion prevention was used as the gold standard for comparison with transmission routes identified by the data mining algorithm. During the study period, our primary method for molecular


characterization of bacterial isolates was pulsed-field gel electro- phoresis (PFGE). To be considered part of the outbreak, patient isolates had to have 85% band similarity by PFGE. In 2016, WGSreplaced PFGE. Based on our experience usingWGSfor hos- pital outbreaks, a cutoff of ≤20 single-nucleotide polymorphisms (SNPs) was used to define genetically related patient isolates.


Extraction and processing of EHR data for data mining


All inpatient, emergency room, and same-day surgery encounters between January 1, 2011, and December 31, 2016, were identified through anEHR data repository that accepts data from Cerner and EpiCare as full-text medical records and integrates information from central transcription, laboratory, pharmacy, finance, admin- istrative, and other departmental databases.10 For each encounter, we obtained microbiology reports and charge transactions from the data repository. To maintain patient confidentiality, a research


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database was established in which each patient was assigned a study identification number (ID) using De-ID software (De-ID Data, Philadelphia, PA). Criteria for exemption from informed consent by the university’s institutional review board were met. Charge transaction data are in a data repository as charge codes


that contain patient account numbers, dates of service, cost centers, transaction codes, charge quantities, and charge amounts. Charge codes include any medication, procedure, location, or other billable itemin our hospital and therefore encompass a large number of pos- sible hospital exposures for each patient. Multiple charge codes can represent exposure to a single instrument; therefore, charge codes for key procedures (eg, endoscopic retrograde cholangiopancreatog- raphy [ERCP] and bronchoscopy) were collapsed into a single variable group that represented that exposure. For example, ERCP has 8 Current Procedure Terminology codes: 43260, ERCP diagnostic including collection of specimens; 43261, ERCP with biopsy; 43278, ERCP with ablation of tumors, etc. All were com- bined into a single variable called “ERCP,” although each charge code was also analyzed individually.


Data mining of the electronic health record (EHR)


The data mining program was designed using a case-control approach based upon the genotyping results using patient EHRdata unrelated to the outbreaks as controls.11 Case patients were defined as those who had clinical isolates with the same strain by PFGE or WGS, as defined above. Controls were patients who were hospital- ized during the 30 days before the case patients’ culture date and did not test positive for the genetically related bacterial species. Hospital exposures were then compared for cases and controls. The data mining program was run on all 9 previously identified


outbreaks identified by the infection prevention team at our insti- tution during 2011–2016 to determine the sensitivity of the algo- rithm for identifying the correct transmission route. The transmission route was deemed to be correct if the route was ranked in the 3 most likely routes of transmission and/or had odds ratios>1 with significant P values. Preventable infections were cal- culated based upon a hypothetical 7- or 14-day infection preven- tion intervention from the date of the positive culture assuming that the data mining program had been running in real time and that effective interventions were enacted (eg, removal of con- taminated equipment, disinfection of environment, and/or enhanced precautions). Outbreaks were deemed nonpreventable if there were only 2 isolates in the outbreak. We scored possible common routes of transmission within an


outbreak according to the formula: S ¼ a ln a=r


ðÞþ r a ðÞa ln  ðÞln 1a=r ðÞ; where a is the number of case patients exposed, r is the number


of patients exposed overall (ie, case patients who are part of the outbreak and control patients who are not), and γ is a parameter that balances the positive and negative evidence (γ=1e−4). For a given set of case patients, each patient can be said to have been infected through the hypothesized common route or by intermedi- ate transmission (ie, via transmission from another case patient). If we take θ to be the unknown probability a patient becomes infected upon exposure to the hypothetical route and γ to be the probability a patient is infected by intermediate transmission (ie, by some other means such as patient-to-patient transmission), then the likelihood of observing a particular set of case patients is propor- tional to θb(1 − θ)r-bγ-b, where b is the number of case patients


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