Clinical predictors for laboratory-confirmed influenza infections: exploring case definitions for influenza-like illness.

OBJECTIVE: To identify clinical signs and symptoms (ie, "terms") that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza. DESIGN: Retrospective, cross-sectional study. SETTING: Large urban academic medical center hospital. PARTICIPANTS: A total of 1,581 emergency department (ED) patients who received a nasopharyngeal swab followed by rRT-PCR testing between August 30, 2009, and January 2, 2010, and between November 28, 2010, and March 26, 2011. METHODS: An electronic surveillance system (GUARDIAN) scanned the entire electronic medical record (EMR) and identified cases containing 29 clinical terms relevant to influenza. Analyses were conducted using logistic regressions, diagnostic odds ratio (DOR), sensitivity, and specificity. RESULTS: The best predictive model for identifying influenza for all ages consisted of cough (DOR=5.87), fever (DOR=4.49), rhinorrhea (DOR=1.98), and myalgias (DOR=1.44). The 3 best case definitions that included combinations of some or all of these 4 symptoms had comparable performance (ie, sensitivity=89%-92% and specificity=38%-44%). For children <5 years of age, the addition of rhinorrhea to the fever and cough case definition achieved a better balance between sensitivity (85%) and specificity (47%). For the fever and cough ILI case definition, using the entire EMR, GUARDIAN identified 37.1% more influenza cases than it did using only the chief complaint data. CONCLUSIONS: A simplified case definition of fever and cough may be suitable for implementation for all ages, while inclusion of rhinorrhea may further improve influenza detection for the 0-4-year-old age group. Finally, ILI surveillance based on the entire EMR is recommended.

Comparing the accuracy of syndrome surveillance systems in detecting influenza-like illness: GUARDIAN vs. RODS vs. electronic medical record reports.

BACKGROUND: A highly sensitive real-time syndrome surveillance system is critical to detect, monitor, and control infectious disease outbreaks, such as influenza. Direct comparisons of diagnostic accuracy of various surveillance systems are scarce. OBJECTIVE: To statistically compare sensitivity and specificity of multiple proprietary and open source syndrome surveillance systems to detect influenza-like illness (ILI). METHODS: A retrospective, cross-sectional study was conducted utilizing data from 1122 patients seen during November 1­7, 2009 in the emergency department of a single urban academic medical center. The study compared the Geographic Utilization of Artificial Intelligence in Real-time for Disease Identification and Alert Notification (GUARDIAN) system to the Complaint Coder (CoCo) of the Real-time Outbreak Detection System (RODS), the Symptom Coder (SyCo) of RODS, and to a standardized report generated via a proprietary electronic medical record (EMR) system. Sensitivity, specificity, and accuracy of each classifier's ability to identify ILI cases were calculated and compared to a manual review by a board-certified emergency physician. Chi-square and McNemar's tests were used to evaluate the statistical difference between the various surveillance systems.ResultsThe performance of GUARDIAN in detecting ILI in terms of sensitivity, specificity, and accuracy, as compared to a physician chart review, was 95.5%, 97.6%, and 97.1%, respectively. The EMR-generated reports were the next best system at identifying disease activity with a sensitivity, specificity, and accuracy of 36.7%, 99.3%, and 83.2%, respectively. RODS (CoCo and SyCo) had similar sensitivity (35.3%) but slightly different specificity (CoCo = 98.9%; SyCo = 99.3%). The GUARDIAN surveillance system with its multiple data sources performed significantly better compared to CoCo (χ2 = 130.6, p < 0.05), SyCo (χ2 = 125.2, p < 0.05), and EMR-based reports (χ2 = 121.3, p < 0.05). In addition, similar significant improvements in the accuracy (>12%) and sensitivity (>47%) were observed for GUARDIAN with only chief complaint data as compared to RODS (CoCo and SyCo) and EMR-based reports. CONCLUSION: In our study population, the GUARDIAN surveillance system, with its ability to utilize multiple data sources from patient encounters and real-time automaticity, demonstrated a more robust performance when compared to standard EMR-based reports and the RODS systems in detecting ILI. More large-scale studies are needed to validate the study findings, and to compare the performance of GUARDIAN in detecting other infectious diseases.

The impact of alternative diagnoses on the utility of influenza-like illness case definition to detect the 2009 H1N1 pandemic.

OBJECTIVE: To investigate the impact of excluding cases with alternative diagnoses on the sensitivity and specificity of the Centers for Disease Control and Prevention's (CDC) influenza-like illness (ILI) case definition in detecting the 2009 H1N1 influenza, using Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification, a disease surveillance system. DESIGN: Retrospective cross-sectional study design. SETTING: Emergency department of an urban tertiary care academic medical center. PATIENTS: 1,233 ED cases, which were tested for respiratory viruses from September 5, 2009 to May 5, 2010. MAIN OUTCOME MEASURE: The main outcome measures were positive predictive value, negative predictive value, sensitivity, specificity, and accuracy of the ILI case definition (both including and excluding alternative diagnoses) to detect H1N1. RESULTS: There was a significant decrease in sensitivity (chi2 = 9.09, p < 0.001) and significant improvement in specificity (chi2 = 179, p < 0.001), after excluding cases with alternative diagnoses. CONCLUSION: When early detection of an influenza epidemic is of prime importance, pursuing alternative diagnoses as part of CDC's ILI case definition may not be warranted for public health reporting due to the significant decrease in sensitivity, in addition to the resources required for detecting these alternative diagnoses.