Interhospital patient transfers between Ontario's academic and large community hospitals increase the risk of Clostridium difficile infection

Transitions of care in Clostridioides difficile infection: a need of the hour

Clostridioides difficile infection (CDI) is a complex disease that by virtue of both its initial virulence and proclivity toward recurrent episodes causes a high morbidity, mortality, and financial burden. This burden is felt by patients and their families as well as the U.S. healthcare system. Recurrent CDI episodes can occur in 25-65% of patients, with a cycle of multiple recurrences in a single patient contributing to the complexity of care. Patients with or suspected of having CDI will receive treatment and their care will be managed across multiple healthcare settings and will include many different levels of healthcare workers. The understanding of this infection is essential for all who are involved in the care of these patients. A well-structured and implemented Transition of Care process can ease the burden on the healthcare system, patients, and their families; reduce the cost of care; and improve patient outcomes. We review the development of Transitions of Care processes, resource guides, and their relevance to improving the management of CDI.

Interfacility patient sharing and Clostridioides difficile infection incidence in the Ontario hospital system: A 13-year cohort study

OBJECTIVE

Interfacility patient movement plays an important role in the dissemination of antimicrobial-resistant organisms throughout healthcare systems. We evaluated how 3 alternative measures of interfacility patient sharing were associated with C. difficile infection incidence in Ontario acute-care facilities.

DESIGN

The cohort included adult acute-care facility stays of ≥3 days between April 2003 and March 2016. We measured 3 facility-level metrics of patient sharing: general patient importation, incidence-weighted patient importation, and C. difficile case importation. Each of the 3 patient-sharing metrics were examined against the incidence of C. difficile infection in the facility per 1,000 stays, using Poisson regression models.

RESULTS

The analyzed cohort included 6.70 million stays at risk of C. difficile infection across 120 facilities. Over the 13-year period, we included 62,189 new cases of healthcare-associated CDI (incidence, 9.3 per 1,000 stays). After adjustment for facility characteristics, general importation was not strongly associated with C. difficile infection incidence (risk ratio [RR] per doubling, 1.10; 95% confidence interval [CI], 0.97-1.24; proportional change in variance [PCV], -2.0%). Incidence-weighted (RR per doubling, 1.18; 95% CI, 1.06-1.30; PCV, -8.4%) and C. difficile case importation (RR per doubling, 1.43; 95% CI, 1.29-1.58; PCV, -30.1%) were strongly associated with C. difficile infection incidence.

CONCLUSIONS

In this 13-year study of acute-care facilities in Ontario, interfacility variation in C. difficile infection incidence was associated with importation of patients from other high-incidence acute-care facilities or specifically of patients with a recent history of C. difficile infection. Regional infection control strategies should consider the potential impact of importation of patients at high risk of C. difficile shedding from outside facilities.

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

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.