Preventing Clostridium difficile Infection: A Clinical Prediction Rule (Risk Score) for Preoperative Patients

2.50
Hdl Handle:
http://hdl.handle.net/10755/160969
Type:
Presentation
Title:
Preventing Clostridium difficile Infection: A Clinical Prediction Rule (Risk Score) for Preoperative Patients
Abstract:
Preventing Clostridium difficile Infection: A Clinical Prediction Rule (Risk Score) for Preoperative Patients
Conference Sponsor:Midwest Nursing Research Society
Conference Year:2010
Author:Krapohl, Greta, RN, MSN
P.I. Institution Name:University of Michigan
Contact Address:1502 Golden Ave, Ann Arbor, MI, 48104, USA
Contact Telephone:734-998-1102
Co-Authors:G.L. Krapohl, B.L. Metzger, M. Titler, School of Nursing, University of Michigan, Ann Arbor, MI; D.A. Campbell, , University of Michigan Hospital, Ann Arbor, MI; D.A. Campbell, School of Medicine, University of Michigan, Ann Arbor, MI;
Introduction: Clostridium difficile is now the most common organism causing hospital-acquired infections. The incidence of Clostridium difficile infection (CDI) has been steadily rising, growing in virulence, and demonstrating an increase in the severity and morbidity of the disease. To curb the escalation of CDI, current efforts are focused on decreasing the exposure to the organism (enhanced personal hygiene, and improved disinfection of surfaces) and maximizing the host resistance (reducing antimicrobial treatment). Despite these efforts, the rate of CDI continues to escalate without evidence of a peak or plateau. This problem is especially prevalent in surgical patients undergoing bowel surgery. Objectives: The purpose of this research is to develop, validate and implement a clinical prediction rule (risk score) for the quantification of infection risk for clinical bedside implementation. We propose that a clinical prediction rule to identify the patients most vulnerable to CDI early in their hospitalization is a strategy that can lead to preventive interventions and treatments targeted at high-risk patients before, not in response to, infectious disease. Methods: A retrospective, cohort design, will be implemented to collect from a sample population consisting of all adult patients (n=1800) enrolled in the Colectomy Project between July 2007 and December 2009. The Colectomy Project, a special subset of the larger Michigan Surgical Quality Collaborative (MSQC), encompasses 24 teaching and community hospitals across the state of Michigan. Multivariate logistic regression will be used to determine variables for inclusion in a CDI prediction rule for postoperative colectomy patients. A subset of independent variables found to be significantly associated with CDI at p < .05 in bivariate tests will be included in the logistic regression model. Results: In progress. Conclusions: The escalation of CDI in hospitals is emerging as a serious medical and public health problem. This research proposes a cost-effective, nontechnological, and nonpharmaceutical approach to prevent CDI and improve patient outcomes by facilitating the ability to identify the most vulnerable patients and target preventative intervention.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Midwest Nursing Research Society

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titlePreventing Clostridium difficile Infection: A Clinical Prediction Rule (Risk Score) for Preoperative Patientsen_GB
dc.identifier.urihttp://hdl.handle.net/10755/160969-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Preventing Clostridium difficile Infection: A Clinical Prediction Rule (Risk Score) for Preoperative Patients</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Midwest Nursing Research Society</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2010</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Krapohl, Greta, RN, MSN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Michigan</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">1502 Golden Ave, Ann Arbor, MI, 48104, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">734-998-1102</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">krapohlg@umich.edu</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">G.L. Krapohl, B.L. Metzger, M. Titler, School of Nursing, University of Michigan, Ann Arbor, MI; D.A. Campbell, , University of Michigan Hospital, Ann Arbor, MI; D.A. Campbell, School of Medicine, University of Michigan, Ann Arbor, MI;</td></tr><tr><td colspan="2" class="item-abstract">Introduction: Clostridium difficile is now the most common organism causing hospital-acquired infections. The incidence of Clostridium difficile infection (CDI) has been steadily rising, growing in virulence, and demonstrating an increase in the severity and morbidity of the disease. To curb the escalation of CDI, current efforts are focused on decreasing the exposure to the organism (enhanced personal hygiene, and improved disinfection of surfaces) and maximizing the host resistance (reducing antimicrobial treatment). Despite these efforts, the rate of CDI continues to escalate without evidence of a peak or plateau. This problem is especially prevalent in surgical patients undergoing bowel surgery. Objectives: The purpose of this research is to develop, validate and implement a clinical prediction rule (risk score) for the quantification of infection risk for clinical bedside implementation. We propose that a clinical prediction rule to identify the patients most vulnerable to CDI early in their hospitalization is a strategy that can lead to preventive interventions and treatments targeted at high-risk patients before, not in response to, infectious disease. Methods: A retrospective, cohort design, will be implemented to collect from a sample population consisting of all adult patients (n=1800) enrolled in the Colectomy Project between July 2007 and December 2009. The Colectomy Project, a special subset of the larger Michigan Surgical Quality Collaborative (MSQC), encompasses 24 teaching and community hospitals across the state of Michigan. Multivariate logistic regression will be used to determine variables for inclusion in a CDI prediction rule for postoperative colectomy patients. A subset of independent variables found to be significantly associated with CDI at p &lt; .05 in bivariate tests will be included in the logistic regression model. Results: In progress. Conclusions: The escalation of CDI in hospitals is emerging as a serious medical and public health problem. This research proposes a cost-effective, nontechnological, and nonpharmaceutical approach to prevent CDI and improve patient outcomes by facilitating the ability to identify the most vulnerable patients and target preventative intervention.</td></tr></table>en_GB
dc.date.available2011-10-26T23:13:45Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T23:13:45Z-
dc.description.sponsorshipMidwest Nursing Research Societyen_GB
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