Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach

Abstract

Objective:To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion centrality as a new predictive measure of CDI.Design:Retrospective cohort study.Methods:A mobility network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility.Results:Closeness centrality was a statistically significant measure associated with unit susceptibility (P < .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion centrality measure was statistically significant (P < .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems.Conclusions:Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.

Public Health and Epidemiology Informatics: Recent Research Trends Moving toward Public Health Data Science

“…EHRs are still a source of high quality information for public health researchers. Post marketing drug surveillance [12,13] and healthcare-associated outbreaks detection [14,15] continue to be hot topics of research.…”

A primer on data visualization in infection prevention and antimicrobial stewardship

“…Although healthcare epidemiologists tend to primarily use data visualization to present HAIs and their respective process metrics over time, more complex uses are increasingly being presented. Bush et al 19 created a mobility network and calculated network centrality measures for each hospital unit. They showed that measures calculated using inpatient transfers, unit-wide risk, and current infections helped warn about risk of Clostridiodes difficile Step, stepdown unit; ONC, hematology-oncology unit; SAAR, standardized antimicrobial administration ratio.…”

“…outbreaks using network plots. 19 Willemsen et al 5 described the infection risk scan (IRIS method) and spider plots as tools to measure and compare infection control practices in 2 hospitals in Europe and the United States. 5 Additionally, the almost inevitable introduction of whole-genome sequencing and machine learning in healthcare epidemiology will add a large amount of data that will be best understood if presented using data visualization.…”

Association between intrahospital transfers and hospital-acquired infection among elderly patients: A retrospective case-control study in one urban UK hospital trust

“…23 Bush et al also showed that units with high incoming transfer rates were statistically associated with new cases of CDI. 12 To the best of our knowledge analyses of intrahospital transfers have not yet been linked with a broad range of microbiology data, despite the fact that many other nosocomial pathogens in addition to CDI have been linked to hospital surface contamination. 24,25 In addition, no such analyses has been conducted using United Kingdom (UK) healthcare system data, or using information from multiple hospital sites.…”

“…2 However, despite being established as an avenue for transmission of pathogens between hospitals, [9][10][11] there is still a lack of clarity around the relationship between intrahospital patient movement and the risk of HAI. 2,12 HAIs are defined as infections which have developed in a hospital or other healthcare delivery setting 48 hours or more following admission, or are present on day 1 or day 2 of admission in a patient discharged in the preceding 48 hours. 13 They place a significant burden on health systems worldwide and lead to increased mortality, intensive care unit (ICU) admissions and longer hospital spells.…”