Validating a Method to Identify Hospital-Associated Clostridioides difficile Infections
Author Information
Author(s): Ray Michael J. PhD, MPH, Lacanilao Kathleen L. BS, Lazaro Maela Robyne PharmD, Strnad Luke C. MD, Furuno Jon P. PhD, FSHEA, Royster Kelly PharmD, McGregor Jessina C. PhD, FSHEA
Primary Institution: Oregon Health & Science University
Hypothesis
A multi-component case definition would more accurately detect patients with CDI compared to any single-component case definition.
Conclusion
The study found that a combination of CDI-specific treatment and laboratory testing has 97% accuracy in identifying hospital-associated CDI cases.
Supporting Evidence
- The algorithm identified HA-CDI cases with 94% accuracy.
- 100% sensitivity and 89% specificity were achieved with the initial algorithm.
- Requiring a positive laboratory test improved diagnostic performance.
Takeaway
The researchers created a new way to find out if patients have a specific infection by looking at their treatment and test results, which works really well.
Methodology
The study involved reviewing electronic health records of inpatient encounters to validate a case definition for hospital-associated CDI.
Potential Biases
Potential misclassification of recurrent CDI as initial episodes due to reliance on EHR data.
Limitations
The study's gold-standard diagnosis relies on EHR documentation, which may not align with prospective evaluations, and results may not be generalizable to other facilities.
Participant Demographics
Inpatients at Oregon Health & Science University, excluding those under 18 or with recurrent/community-acquired CDI.
Statistical Information
Confidence Interval
95% CI: 88%–97%
Digital Object Identifier (DOI)
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