Validation of a prediction rule for endocarditis in febrile injection drug users

• Published in: The American journal of emergency medicine Vol. 32; no. 5; pp. 412 - 416
• Main Authors: Chung-Esaki, Hangyul, MD, Rodriguez, Robert M., MD, Alter, Harrison, MD, MS, Cisse, Bitou, BA
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2014; Elsevier Limited
• Subjects: Adult; Cardiac arrhythmia; College admissions; Confidence intervals; Drug use; Emergency; Emergency medical care; Emergency Service, Hospital; Endocarditis - diagnosis; Female; Fever; Humans; Infections; Logistics; Male; Medical records; Mortality; Predictive Value of Tests; Prospective Studies; Ratios; Risk Assessment; Sensitivity and Specificity; Studies; Substance Abuse, Intravenous - complications; Variables
• ISSN: 0735-6757; 1532-8171
• DOI: 10.1016/j.ajem.2014.01.008

Abstract 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.