| Literature DB >> 29644943 |
Gregory R Madden1, Ian German Mesner2, Heather L Cox1, Amy J Mathers1, Jason A Lyman3, Costi D Sifri1, Kyle B Enfield4.
Abstract
We hypothesized that a computerized clinical decision support tool for Clostridium difficile testing would reduce unnecessary inpatient tests, resulting in fewer laboratory-identified events. Census-adjusted interrupted time-series analyses demonstrated significant reductions of 41% fewer tests and 31% fewer hospital-onset C. difficile infection laboratory-identified events following this intervention.Infect Control Hosp Epidemiol 2018;39:737-740.Entities:
Mesh:
Year: 2018 PMID: 29644943 PMCID: PMC6088779 DOI: 10.1017/ice.2018.53
Source DB: PubMed Journal: Infect Control Hosp Epidemiol ISSN: 0899-823X Impact factor: 3.254
FIGURE 1Two-part clinical decision support algorithm. NOTE. NAAT, nucleic acid amplification test for Clostridium difficile; C. diff, C. difficile; PPV, positive predictive value; WBC, white blood cell count.
FIGURE 2Monthly C. difficile tests and hospital-onset C. difficile infection (HO-CDI) laboratory-identified (LabID) events detected with CCDS tool pre- and postintervention. (a) Monthly rates of test results. (b) Trends of monthly HO-CDI rates over the same period. The dotted line depicts predicted values using the quasi-Poisson model.