Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population
| Autor: | Rajiv Sonti, Elena M Welt, Yi Hu, Megan E Conroy, George Luta, Daniel B Jamieson |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
education.field_of_study business.industry Critically ill Population Drug resistance Logistic regression medicine.disease Intensive care unit law.invention Sepsis Infectious Diseases law Medicine Antimicrobial stewardship Pharmacology (medical) business Intensive care medicine education Original Research |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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