Kristen Bush1,2, Hugo Barbosa3, Samir Farooq1, Samuel J Weisenthal1,2, Melissa Trayhan1,2, Robert J White1,2, Ekaterina I Noyes4, Gourab Ghoshal3, Martin S Zand1,2,5. 1. Rochester Center for Health Informatics at the University of Rochester Medical Center, Rochester, New York. 2. Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester, New York. 3. Department of Physics and Astronomy, University of Rochester, Rochester, New York. 4. Department of Epidemiology and Environmental Health, State University of New York at Buffalo, Buffalo, New York. 5. Department of Medicine, Division of Nephrology, University of Rochester Medical Center, Rochester, New York.
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.
OBJECTIVE: To examine the relationship between unit-wide Clostridium difficileinfection (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.
Authors: Emanuela Estera Boncea; Paul Expert; Kate Honeyford; Anne Kinderlerer; Colin Mitchell; Graham S Cooke; Luca Mercuri; Céire E Costelloe Journal: BMJ Qual Saf Date: 2021-01-25 Impact factor: 7.035