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Published on: August 30, 2018
Crossroads of Antimicrobial and Diagnostic Stewardship: Assessing Risks to Develop Clinical Decision Support to
Iris Zou1, Daniel Abate2, Michelle Newman3
1Department of Nursing, University of Maryland Medical Center, Baltimore, Maryland, USA.
Background:
Early detection of multidrug-resistant Pseudomonas aeruginosa (MDRP) remains challenging. Existing risk prediction tools are difficult to translate to bedside application. The goal of this study was to develop a simple electronic medical record (EMR)-integrated tool for prediction of MDRP infection.
Methods:
This was a mixed-methods study. We conducted a split-sample cohort study of adult critical care patients with P aeruginosa infections. Two previously published tools were validated using c-statistic. A subset of variables based on strength of association and ease of EMR extraction was selected for further evaluation. A simplified tool was developed using multivariable logistic regression. Both c-statistic and theoretical trade-off of over- versus underprescribing of broad-spectrum MDRP therapy were assessed in the validation cohort. A qualitative survey of frontline clinicians assessed understanding of risks for MDRP and potential usability of an EMR-integrated tool to predict MDRP.
Results:
The 2 previous risk prediction tools demonstrated similar accuracy in the derivation cohort (c-statistic of 0.76 [95% confidence interval {CI}, .69-.83] and 0.73 [95% CI, .66-.8]). A simplified tool based on 4 variables demonstrated reasonable accuracy (c-statistic of 0.71 [95% CI, .57-.85]) without significant overprescribing in the validation cohort. The risk factors were prior MDRP infection, ≥4 antibiotics prior to culture, infection >3 days after admission, and dialysis. Fourteen clinicians completed the survey. An alert providing context regarding individual patient risk factors for MDRP was preferred.
Conclusions:
These results can be used to develop a local EMR-integrated tool to improve timeliness of effective therapy in those at risk of MDRP infections.
Insights
A new electronic medical record tool can predict multidrug-resistant Pseudomonas aeruginosa (MDRP) infections. This simple tool aids clinicians in timely, effective treatment for at-risk patients.
Area of Science:
- Critical care medicine
- Infectious diseases
- Medical informatics
Background:
- Early detection of multidrug-resistant Pseudomonas aeruginosa (MDRP) is difficult.
- Existing risk prediction tools lack bedside applicability.
- A simple electronic medical record (EMR)-integrated tool is needed for MDRP prediction.
Purpose of the Study:
- To develop and validate a simple EMR-integrated tool for predicting MDRP infection.
- To assess the usability and clinical preference for an MDRP prediction tool.
Main Methods:
- Mixed-methods study including a split-sample cohort study of adult critical care patients.
- Validation of existing tools and development of a simplified EMR-integrated tool using multivariable logistic regression.
- Qualitative survey of clinicians on MDRP risk perception and tool usability.
Main Results:
- Two previous tools showed similar accuracy (c-statistic 0.76 and 0.73).
- A simplified 4-variable tool demonstrated reasonable accuracy (c-statistic 0.71) without overprescribing.
- Identified risk factors: prior MDRP infection, multiple prior antibiotics, late infection onset, and dialysis. Clinicians preferred contextual alerts.
Conclusions:
- An EMR-integrated tool can be developed to improve timely effective therapy for MDRP infections.
- The simplified tool aids in identifying patients at risk for MDRP.
- Clinical input is valuable for optimizing EMR-integrated predictive tools.
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