Validation of a prediction rule for endocarditis in febrile injection drug users
| Autor: | Robert M. Rodriguez, Hangyul M. Chung-Esaki, Harrison J. Alter, Bitou Cisse |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Adult
Male Drug medicine.medical_specialty Fever media_common.quotation_subject Recursive partitioning Skin infection Logistic regression Risk Assessment Sensitivity and Specificity Predictive Value of Tests Internal medicine Humans Medicine Endocarditis Prospective Studies Substance Abuse Intravenous Intensive care medicine media_common business.industry General Medicine Emergency department medicine.disease Confidence interval Emergency Medicine Very low risk Female Emergency Service Hospital business |
| Zdroj: | The American Journal of Emergency Medicine. 32:412-416 |
| ISSN: | 0735-6757 |
| DOI: | 10.1016/j.ajem.2014.01.008 |
| Popis: | Background Infectious endocarditis (IE) in febrile injection drug users (IDUs) is a critical diagnosis to identify in the emergency department (ED). A decision tool that identifies patients at very low risk for endocarditis using readily available clinical data could reduce admissions and cost. Objective To evaluate the diagnostic performance of a previously derived decision instrument to rule out endocarditis in febrile IDUs (Prediction Rule for Endocarditis in Injection Drug Users [PRE-IDU]) and to develop a prediction model for likelihood of endocarditis for those who are not ruled out by PRE-IDU. Methods Febrile IDUs admitted to rule out endocarditis were prospectively enrolled from 2 urban EDs in June 2007 to March 2011. Clinical data from ED presentation (first 6 hours) and outcome data from inpatient records were recorded and reviewed by 2 independent investigators. Diagnosis of IE was based on modified Duke criteria and discharge summaries. The diagnostic performance of PRE-IDU, which combines tachycardia, cardiac murmur, and absence of skin infection, was determined using recursive partitioning and logistic regression modeling. Results Of the 249 subjects, 18 (7%) had IE. Recursive partitioning yielded an instrument with 100% sensitivity (95% confidence interval [CI], 84%-100%) and 100% negative predictive value (95% CI, 91%-100%), but low specificity (13%; 95% CI, 12%-13%). Multiple logistic regression modeling with the 3 clinical predictors allowed risk stratification with posttest probabilities ranging from 3% to 20%. Conclusion The PRE-IDU instrument predicted IE with high sensitivity and ruled out IE with high negative predictive value. Our logistic regression model provided posttest probabilities ranging from 3% to 20%. The PRE-IDU instrument and the associated model may help guide hospital admission and diagnostic testing in evaluation of febrile IDUs in the ED. |
| Databáze: | OpenAIRE |
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