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Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population
Rajiv Sonti1, Megan E Conroy1, Elena M Welt1
1Division of Pulmonary, Critical Care and Sleep Medicine, Medstar Georgetown University Hospital, Washington, DC, USA.
Purpose:
To create a model predictive of an individual's risk of developing a de novo multidrug-resistant (MDR) infection while in the intensive care unit (ICU).
Methods:
This is a case-control study in which 189 ICU patients diagnosed with their first infection with an MDR organism were compared on the basis of demographic, past medical and clinical variables to randomly selected ICU patients without such an infection, era-matched in a 2:1 ratio. A prediction tool was derived using multivariate logistic regression.
Results:
Five features remained predictive of developing an infection with a drug-resistant pathogen: hospitalization within a year [adjusted odds ratio (OR) 2.14], chronic hemodialysis (3.86), underlying oxygen-dependent pulmonary disease (1.86), endotracheal intubation within 24 h (2.46) and reason for ICU admission (respiratory failure 2.89, non-respiratory failure, non-shock presentation 1.85). Using a scoring system (0-7 points) based on the adjusted OR, risk categories were derived (low: 0-2 points, intermediate: 3-4 points and high risk: 5-7 points). The negative predictive value at a score cutoff of 2 is excellent (88.9%).
Conclusions:
A clinical prediction rule comprised of five easily measured ICU variables reasonably discriminates between patients who will develop their first MDR infection versus those who will not.
Insights
A new clinical prediction rule identifies intensive care unit (ICU) patients at high risk for developing multidrug-resistant (MDR) infections. This model uses five easily measured variables to predict the likelihood of a first-time MDR infection in the ICU.
Area of Science:
- Critical Care Medicine
- Infectious Diseases
- Epidemiology
Background:
- Multidrug-resistant (MDR) infections pose a significant threat to intensive care unit (ICU) patients.
- Predicting the risk of developing a *de novo* MDR infection is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a clinical prediction model for identifying individuals at risk of acquiring a first-time multidrug-resistant infection during their ICU stay.
Main Methods:
- A case-control study comparing 189 ICU patients with a first MDR infection against a 2:1 ratio of matched controls without MDR infections.
- Multivariate logistic regression was employed to identify predictive variables and construct the prediction tool.
Main Results:
- Key predictors for MDR infection include prior hospitalization, chronic hemodialysis, oxygen-dependent pulmonary disease, early endotracheal intubation, and ICU admission reason.
- A scoring system (0-7 points) was developed, categorizing patients into low, intermediate, and high-risk groups for MDR infections, with a negative predictive value of 88.9% at a score of 2.
Conclusions:
- A simple clinical prediction rule utilizing five readily available ICU variables effectively distinguishes patients likely to develop a first MDR infection.
- This rule offers a practical approach for risk stratification of ICU patients regarding *de novo* multidrug-resistant infections.
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