Factors determining success in reduction of Central Line Associated Blood Stream Infection CLABSI on statewide levels

1. Factors determining success in reduction of Central Line Associated Blood Stream Infection (CLABSI) on statewide levels HeeWon Lee, Doris Duke Clinical Research Fellow PI Peter Pronovost, M.D. PhD., Bradford Winters, M.D. PhD.

2. BackgroundCLABSI A common, costly, and fatal cause of hospital-related deaths, with approximately 31,000 annual deaths in the US $3 billion spent worldwide.1 However, CLABSIs are preventable.2,3 1. Klevens RM, Edwards JR, Richards CL Jr, et al. Estimating health care-associated infections and deaths in US hospitals, 2002. Public Health Rep. 2007; 122(2):160-166. 2. Edwards JR, Peterson KD, Mu Y, et al. National Healthcare Safety Network (NHSN) report: data summary for 2006 through 2008, issued December 2009. Am J Infect Control. 2009;37(10):783-805. 3. Berenholtz SM, Pronovost PJ, Lipsett PA, et al. Eliminating catheter-related blood stream infections in the intensive care unit. Crit Care Med. 2004;32(10):2014-2020. CLABSIs are preventable, health care dollars spent are avoidableCLABSIs are preventable, health care dollars spent are avoidable

3. BackgroundOn the CUSP: Stop BSI Project led by the Johns Hopkins Quality Safety Research Group Implementing a two-component, multifaceted hospital safety program has Saved lives, health care $ Reduced CLABSIs by 66% Sustained a median infection rate of 0, and mean of 1 infection per 1000 catheter-days for more than 3 years in Michigan.4 4. Pronovost PJ, Goeschel CA, Colantuoni E, et al. Sustaining reductions in catheter relate bloodstrea infections in Michigan intensive care units: observational study BMJ. 2010;240:c309. Hospital safety program run by the Johns Hopkins QSRG; CUSP = comprehensive unit based safety program; first demonstrated to decrease median infection rate to nearly 0 at JHMI SICU = hospital level, then expanded to state-wide level to Michigan (more than 100 ICUs) Hospital safety program run by the Johns Hopkins QSRG; CUSP = comprehensive unit based safety program; first demonstrated to decrease median infection rate to nearly 0 at JHMI SICU = hospital level, then expanded to state-wide level to Michigan (more than 100 ICUs)

4. Background On the CUSP: Stop BSI Timeline

5. BackgroundOn the CUSP: Stop BSI I. Evidence-based Behaviors to Prevent CLABSI5 Remove Unnecessary Lines Wash Hands Prior to Procedure Use Maximal Barrier Precautions Clean Skin with Chlorhexidine Avoid Femoral Lines 5. Marschall et al. Infect Control Hosp Epidemiol 2008. CDC.gov The On the CUSP: Stop BSI project has two main components: 1. Evidence based behaviors to prevent CLABSI The On the CUSP: Stop BSI project has two main components: 1. Evidence based behaviors to prevent CLABSI

6. BackgroundOn the CUSP: Stop BSI II. Multifaceted Safety Program (Team Checkup Toolkit)5 Learning from Defects Daily Goals Checklist Morning Briefing Observing Rounds Shadowing Another Profession Culture Debriefing Physician Call List And 2. Tools to improve culture and teamwork among physicians, nurses, and administrators => to establish a culture of safetyAnd 2. Tools to improve culture and teamwork among physicians, nurses, and administrators => to establish a culture of safety

7. Program focuses on teamwork and culture changeProgram focuses on teamwork and culture change

8. BackgroundExpansion of Stop BSI Project as calculated by the standardized infection ratio SIR = total # observed CLABSI/#expected CLABSI from a standardized population as indicated by the CDC’s National Healthcare Safety Network; SIR of .25 implies a 75% reduction for the nation (state or facility); based on current rates as calculated by the standardized infection ratio SIR = total # observed CLABSI/#expected CLABSI from a standardized population as indicated by the CDC’s National Healthcare Safety Network; SIR of .25 implies a 75% reduction for the nation (state or facility); based on current rates

9. But wait! Problems exist… Despite the expansion of the program to numerous states… Median rates of CLABSI remain high or unchanged Some hospitals claim to use the checklist, despite having high or unknown infection rates Some hospitals say that the ICU patients are too sick and that infection is inevitable Hospital enrollment in the program has been slow.6 6. Department of Health and Human Services. Department of Health and Human Services initiative http://www/jjs/gpv/ Accessed July 1, 2010.

10. What factors are associated with success of reducing CLABSIs?  Not only on a hospital level, but specifically on statewide levels Not only on a hospital level, but specifically on statewide levels

11. Hypothesis States with higher rates of meeting the CLABSI reduction goals are associated with greater hospital participation and adherence to the two-component, multifaceted safety program. Grade hospital participation and adherence--not binary, but continuous variablesGrade hospital participation and adherence--not binary, but continuous variables

12. CLABSI definition7 For determining CLABSI rate Numerator: # of CLABSIs Denominator: # of central line-days Expressed as a rate of X CLABSI/1,000 central line days #CLABSI/# central line days X 1000 7. National Healthcare Safety Network (NHSN): Device-Associated (DA) Module www.cdc.gov/nhsn/psc_da.html. Accessed July 1, 2010.

13. Study Design Prospective observational cohort study Outcomes for state; data exist for each ICU/hospitalOutcomes for state; data exist for each ICU/hospital

14. Stratification Hospitals by… Teaching status Bed size Presence in a state with mandatory participation in the National Healthcare Safety Network (NHSN) States by… Number of teaching/academic institutions present Presence of mandate to report These factors have already been assumed to impact CLABSI rates--for example, a larger academic institution or a state with more numbers of teaching centers (as defined by having a residency program) or a hospital in a state that mandates report of CLABSI may more rigorously implement the On the CUSP: Stop BSI program and have lower rates of CLABSI. At the same time, smaller, private hospitals may have an easier time implementing the two-component, multifaceted program and may have lower CLABSI rates. It may be of great use to first truly examine whether these variables for which we are stratifying the data could be associated to CLABSI rates, and then if a variable is determined to be significantly associated, we may use it to stratify the data. These factors have already been assumed to impact CLABSI rates--for example, a larger academic institution or a state with more numbers of teaching centers (as defined by having a residency program) or a hospital in a state that mandates report of CLABSI may more rigorously implement the On the CUSP: Stop BSI program and have lower rates of CLABSI. At the same time, smaller, private hospitals may have an easier time implementing the two-component, multifaceted program and may have lower CLABSI rates. It may be of great use to first truly examine whether these variables for which we are stratifying the data could be associated to CLABSI rates, and then if a variable is determined to be significantly associated, we may use it to stratify the data.

15. Statistical methods Two sample Wilcoxon rank-sum test for comparison of medians with baseline CLABSI rates Poisson regression modeling for comparison of CLABSI rates before, during, and up to 3 and 28 months after implementation of program Wilcoxon rank-sum test: nonparametric test for comparing two independent samples with ordinal data or with numerical observations that are not normally distributed=> t-test; research question asks whether the means of the two groups are equal; Poisson distribution used to determine probability of rare events, when the outcome is the number of times an event occursWilcoxon rank-sum test: nonparametric test for comparing two independent samples with ordinal data or with numerical observations that are not normally distributed=> t-test; research question asks whether the means of the two groups are equal; Poisson distribution used to determine probability of rare events, when the outcome is the number of times an event occurs

16. Limitations of study Observational study Confounders? Inconsistent data from individual hospitals regarding use of multifaceted toolkit Observational study--causal inference is challenging and confounding variables--> ex. academic institution may be associated to both the rate of participation and adherence (variables) as well as reduced CLABSI rates; confounders not formally demonstrated to be significant; inconsistent data--taking 5 minutes to fill out “learning from defects tool” as opposed to having entire team take more time to go over the tool will both result in the hospitals/ICUs “having completed” the toolkit, but results of decreased CLABSI may be different. Observational study--causal inference is challenging and confounding variables--> ex. academic institution may be associated to both the rate of participation and adherence (variables) as well as reduced CLABSI rates; confounders not formally demonstrated to be significant; inconsistent data--taking 5 minutes to fill out “learning from defects tool” as opposed to having entire team take more time to go over the tool will both result in the hospitals/ICUs “having completed” the toolkit, but results of decreased CLABSI may be different.

17. Implications of study CLABSI are preventable! On the CUSP: Stop BSI project has demonstrated effective elimination of CLABSI on a statewide level in Michigan Many states are participating, but CLABSIs still exist Study is the first of its kind in examining all participating states and CLABSI rates By identifying factors associated with success in reducing CLABSI, we may be better able to reach the goal of reducing and eventually eliminating CLABSI, further helping save lives and healthcare $

18. Acknowledgements Small group leaders: Dr. Vered Stearns, Dr. Pete Miller Small group members: James Chen, Hormuz Dasenbrock, Andrew Ibrahim, Kevin Jeng, Yong Suh Research mentors: Dr. Bradford Winters, Dr. Peter Pronovost QSRG team members