Missing the Point in Point Prevalence: Harnessing EMR Data to Identify Epi-Linked Patients in an Outbreak Investigation

Background: A critical step during outbreak investigations is actively screening for additional cases to assess ongoing transmission. In the healthcare setting, one widely used method is point-prevalence screening on the whole unit where a positive patient is housed. Although this point-prevalence approach captures the “place,” it can miss the “person” and “time” elements that define the population-at-risk. Methods: At University of North Carolina (UNC) Hospitals, we used business intelligence tools to build a query that harnesses the admission, discharge, and transfer (ADT) data from the electronic medical record (EMR). Using this data identifies every patient who overlapped in time and space with a positive patient. An additional query identifies currently admitted overlap patients and their current location. During an outbreak investigation, an analyst executes these queries in the mornings when surveillance screens are scheduled. The queries generate a list of patients to screen that are prioritized on the number of days they were in the same unit with the positive patient. This overlap methodology successfully captures the person, place, and time associated with possible disease transmission. We implemented the overlap method for the last 3 months following 1 year of point-prevalence approach screening during a novel disease outbreak at UNC Hospitals. Results: In total, 4,385 unique patients overlapped with previously identified infected or colonized patients, of which 781 (17.8%) from 40 departments were screened over 15 months. During a subsequent, currently ongoing, outbreak, we are utilizing the overlap method and in 6 weeks have already screened 161 of the 1,234 overlapping patients (13%). After 3 rounds of overlap screening, we have already been able to identify 1 additional positive patient. This patient was on the same unit as patient zero 4 months prior but was readmitted to a unit that would not have received a point-prevalence screen using the standard approach. Conclusions: Surveillance screening is a time-consuming, resource-intensive effort that requires collaboration between infection prevention, clinical staff, patients, and the laboratory. By harnessing EMR ADT data, we can better target the population at risk and more efficiently utilize resources during outbreak investigations. In addition, the overlap method fills a gap in the current CDC guidelines by focusing on patients who were on the same unit with any positive patient, including those who discharged and readmitted. Most importantly, we identified an additional positive patient that would not have been detected through a point-prevalence screen, helping us prevent further disease transmission.Funding: NoneDisclosures: None

Under- and upgrading of prostate cancer after radical prostatectomy in a screening setting.

105 Background: It has been well established that there is an upward drift in assigned Gleason score (GS) to prostate cancer (PC) biopsies. However, few studies have examined the concordance between biopsy GS and pathological GS after radical prostatectomy (RP) in a screening setting over time. We assessed the proportion of under and upgrading of PC after RP by comparing the prevalence screen with the incidence screens from the European Randomized Study of Screening for Prostate Cancer (ERSPC), section Rotterdam. Methods: From 1993 to 1999, a total of 42.376 men identified from population registries in the Rotterdam region (55-74 yrs) were randomized into a screening or control arm. Men with PSA ≥3.0 ng/ml were recommended for sextant prostate biopsy, which were lateralized since 1996. The screening interval was 4 yrs. Men with screen-detected PC, who underwent RP within 1 yr, were eligible for this study. Results: In all, biopsy and pathological GS were known for 639 men who underwent RP. Of these RPs, 405 were performed between 1994 and 2000 (prevalence screen) whereas 235 cases were performed between 1998 and 2008 (incidence screens). In the prevalence screen, 49.8% of the biopsy GS matched the pathological GS ( Table ). This percentage improved to 58.3% in the incidence screens (p=0.045). The proportion of undergrading after RP was 23.0% in the prevalence screen, which declined to 16.2% in the incidence screens (p=0.049). Upgrading occurred in 27.2% of the cases in the prevalence and 25.5% in the incidence screens (p=0.71). Conclusions: We have demonstrated an improved concordance between biopsy and pathological GS over time by comparing cancers detected in the prevalence screen with those detected in the incidence screens, which is in line with the upward shift of GS classification over time. In addition, significant less undergrading occurred in the incidence screens. However, one should keep in mind that within this screening program still a quarter of the biopsy GS were upgraded after RP in the incidence screens. [Table: see text] [Table: see text]