Comparison of two frailty identification tools for critically ill patients: A post-hoc analysis of a multicenter prospective cohort study.

PURPOSE: We aimed to describe the association of two frailty screening tools, the validated Clinical Frailty Scale (CFS) score and the recently described modified Frailty Index (mFI) in critically ill patients. MATERIALS AND METHODS: We performed a post-hoc analysis of a multicenter cohort of patients admitted to six Canadian Intensive Care Units (ICU) between 2010 and 2011. Frailty was screened using the CFS and the mFI. Concordance between these tools was evaluated, as well as discrimination and predictive ability for clinical outcomes after adjustments. RESULTS: The cohort included 421 patients. Prevalence of frailty was 32.8% with the CFS and 39.2% with the mFI. However, concordance between the two tools was low [(intraclass correlation of 0.37; 95% confidence interval [CI] 0.29-0.45) and partial Spearman rank correlation of 0.38 (95% CI 0.29-0.47)]. Hospital and 1-year mortality, as well as dependency after discharge and hospital readmission, were greater for frail compared to non-frail patients screened with the use of both tools. CONCLUSION: While the CFS and mFI showed low concordance, both showed good discrimination and predictive validity for hospital mortality. Both tools identify a subgroup of frail patients more likely to have worse clinical outcomes.

A Model-Based Strategy to Control the Spread of Carbapenem-Resistant Enterobacteriaceae: Simulate and Implement.

OBJECTIVE To reduce transmission of carbapenem-resistant Enterobacteriaceae (CRE) in an intensive care unit with interventions based on simulations by a developed mathematical model. DESIGN Before-after trial with a 44-week baseline period and 24-week intervention period. SETTING Medical intensive care unit of a tertiary care teaching hospital. PARTICIPANTS All patients admitted to the unit. METHODS We developed a model of transmission of CRE in an intensive care unit and measured all necessary parameters for the model input. Goals of compliance with hand hygiene and with isolation precautions were established on the basis of the simulations and an intervention was focused on reaching those metrics as goals. Weekly auditing and giving feedback were conducted. RESULTS The goals for compliance with hand hygiene and contact precautions were reached on the third week of the intervention period. During the baseline period, the calculated R0 was 11; the median prevalence of patients colonized by CRE in the unit was 33%, and 3 times it exceeded 50%. In the intervention period, the median prevalence of colonized CRE patients went to 21%, with a median weekly Rn of 0.42 (range, 0-2.1). CONCLUSIONS The simulations helped establish and achieve specific goals to control the high prevalence rates of CRE and reduce CRE transmission within the unit. The model was able to predict the observed outcomes. To our knowledge, this is the first study in infection control to measure most variables of a model in real life and to apply the model as a decision support tool for intervention. Infect Control Hosp Epidemiol 2016;1-8.