| Autor: |
Meghan A Baker, Edward J Septimus, Ken Kleinman, Julia Moody, Kenneth E Sands, Neha Varma, Amanda Isaacs, Laura E McLean, Micaela H Coady, Jackie Blanchard, Russell Poland, Deborah S Yokoe, John Stelling, Katherine Haffenreffer, Adam Clark, Taliser Avery, Selsebil Sljivo, Robert A Weinstein, Kimberly Smith, Brandon Carver, Brittany Meador, Caren Spencer-Smith, Chamaine Washington, Megha Bhattarai, Lauren Shimelman, Jonathan B Perlin, Richard Platt, Susan S Huang |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Open Forum Infectious Diseases. 9 |
| ISSN: |
2328-8957 |
| Popis: |
Background The CLUSTER trial assessed the impact of prospective identification of clusters coupled with a response protocol on the containment of hospital clusters. Methods This 82-hospital CRT in 16 states compared clusters of bacterial and fungal healthcare pathogens using a statistical outbreak detection tool (WHONET-SaTScan) coupled with a standardized response protocol (automated cluster detection arm) compared to routine surveillance with the response protocol (control arm). Trial periods: 24 mo Baseline (2/17–1/19); 5 mo Phase-in (2/19–6/19); 30 mo Intervention (7/19–1/22). The primary outcome was the number of additional cases occurring after initial cluster detection. Analyses used generalized linear mixed models to assess differences in additional cases between the intervention vs baseline periods across arms, clustering by hospital. Results were assessed overall and, to account for the effect of COVID-19 on hospital operations, stratified into pre-COVID-19 (7/19–6/20) and during COVID-19 (7/20–1/22) intervention periods. We also assessed the probability that a patient was in a cluster. Results In the baseline period, the automated cluster detection and control arms had 0.09 and 0.07 additional cluster cases/1000 admissions, respectively. The automated cluster detection arm had a 22% greater relative reduction in additional cluster cases in the intervention vs baseline period compared to control (P=0.5). Within the intervention period, the automated cluster detection arm had a significant 64% relative reduction pre-COVID-19 (P< 0.05) and a non-significant 6% relative reduction during COVID-19 (P=0.9) compared to control (Figure). When evaluating patient risk of being part of a cluster across the entire intervention period, the automated cluster detection arm had a significant 35% relative reduction vs control (P< 0.01). Conclusion A statistical automated tool coupled with a response protocol improved cluster containment by 64% pre-COVID-19 but not during COVID-19; there were no significant differences between the arms when using the entire intervention period. Automated cluster detection may substantially improve outbreak containment in non-pandemic periods when infection prevention programs are able to optimize containment protocols. Disclosures Susan S. Huang, MD, MPH, Medline: Conducted studies in which hospitals and nursing homes received contributed antiseptic and/or environmental cleaning products|Molnlyke: Conducted clinical studies in which hospitals received contributed antiseptic product|Stryker: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic products|Xttrium Laboratories: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic product. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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