Jennifer L Kuntz1, David H Smith2, Amanda F Petrik3, Xiuhai Yang4, Micah L Thorp5, Tracy Barton6, Karen Barton7, Matthew Labreche8, Steven J Spindel9, Eric S Johnson10. 1. Investigator at The Center for Health Research in Portland, OR. jennifer.l.kuntz@kpchr.org. 2. Senior Investigator at The Center for Health Research in Portland, OR. david.h.smith@kpchr.org. 3. Research Associate at The Center for Health Research in Portland, OR. amanda.f.petrik@kpchr.org. 4. Research Analyst at The Center for Health Research in Portland, OR. xiuhai.yang@kpchr.org. 5. Nephrologist for Northwest Permanente in Portland, OR. micah.l.thorp@kp.org. 6. Infectious Disease Pharmacist at the Sunnyside Medical Center in Clackamas, OR. tracy.a.barton@kp.org. 7. Infectious Disease Pharmacist at the Sunnyside Medical Center in Clackamas, OR. karen.l.barton@kpchr.org. 8. Infectious Disease Pharmacist at the Sunnyside Medical Center in Clackamas, OR. matthew.j.labreche@kp.org. 9. Infectious Disease Specialist at the Sunnyside Medical Center in Clackamas, OR. steven.j.spindel@kp.org. 10. Investigator at The Center for Health Research in Portland, OR. eric.s.johnson@kpchr.org.
Abstract
INTRODUCTION: Increasing morbidity and health care costs related to Clostridium difficile infection (CDI) have heightened interest in methods to identify patients who would most benefit from interventions to mitigate the likelihood of CDI. OBJECTIVE: To develop a risk score that can be calculated upon hospital admission and used by antimicrobial stewards, including pharmacists and clinicians, to identify patients at risk for CDI who would benefit from enhanced antibiotic review and patient education. METHODS: We assembled a cohort of Kaiser Permanente Northwest patients with a hospital admission from July 1, 2005, through December 30, 2012, and identified CDI in the six months following hospital admission. Using Cox regression, we constructed a score to identify patients at high risk for CDI on the basis of preadmission characteristics. We calculated and plotted the observed six-month CDI risk for each decile of predicted risk. RESULTS: We identified 721 CDIs following 54,186 hospital admissions-a 6-month incidence of 13.3 CDIs/1000 patient admissions. Patients with the highest predicted risk of CDI had an observed incidence of 53 CDIs/1000 patient admissions. The score differentiated between patients who do and do not develop CDI, with values for the extended C-statistic of 0.75. Predicted risk for CDI agreed closely with observed risk. CONCLUSION: Our risk score accurately predicted six-month risk for CDI using preadmission characteristics. Accurate predictions among the highest-risk patient subgroups allow for the identification of patients who could be targeted for and who would likely benefit from review of inpatient antibiotic use or enhanced educational efforts at the time of discharge planning.
INTRODUCTION: Increasing morbidity and health care costs related to Clostridium difficile infection (CDI) have heightened interest in methods to identify patients who would most benefit from interventions to mitigate the likelihood of CDI. OBJECTIVE: To develop a risk score that can be calculated upon hospital admission and used by antimicrobial stewards, including pharmacists and clinicians, to identify patients at risk for CDI who would benefit from enhanced antibiotic review and patient education. METHODS: We assembled a cohort of Kaiser Permanente Northwest patients with a hospital admission from July 1, 2005, through December 30, 2012, and identified CDI in the six months following hospital admission. Using Cox regression, we constructed a score to identify patients at high risk for CDI on the basis of preadmission characteristics. We calculated and plotted the observed six-month CDI risk for each decile of predicted risk. RESULTS: We identified 721 CDIs following 54,186 hospital admissions-a 6-month incidence of 13.3 CDIs/1000 patient admissions. Patients with the highest predicted risk of CDI had an observed incidence of 53 CDIs/1000 patient admissions. The score differentiated between patients who do and do not develop CDI, with values for the extended C-statistic of 0.75. Predicted risk for CDI agreed closely with observed risk. CONCLUSION: Our risk score accurately predicted six-month risk for CDI using preadmission characteristics. Accurate predictions among the highest-risk patient subgroups allow for the identification of patients who could be targeted for and who would likely benefit from review of inpatient antibiotic use or enhanced educational efforts at the time of discharge planning.
Authors: Robert C Owens; Curtis J Donskey; Robert P Gaynes; Vivian G Loo; Carlene A Muto Journal: Clin Infect Dis Date: 2008-01-15 Impact factor: 9.079
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