Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach
| Autor: | Kristen Bush, Ekaterina I. Noyes, Martin S. Zand, Gourab Ghoshal, Samir A. Farooq, Melissa Trayhan, Samuel J. Weisenthal, Hugo Barbosa, Robert J. White |
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| Rok vydání: | 2019 |
| Předmět: |
Microbiology (medical)
medicine.medical_specialty genetic structures Epidemiology business.industry Context (language use) Retrospective cohort study 030501 epidemiology Clostridium difficile Logistic regression 03 medical and health sciences 0302 clinical medicine Infectious Diseases Emergency medicine Medicine Infection control 030212 general & internal medicine 0305 other medical science business Centrality Unit level Network approach |
| Zdroj: | Infection Control & Hospital Epidemiology. 40:1380-1386 |
| ISSN: | 1559-6834 0899-823X |
| DOI: | 10.1017/ice.2019.288 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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