Determination of Eligibility for Influenza Research: A Clinical Informatics Approach. (10th June 2019)

• Record Type: Journal Article
• Authors: Silveira, Fernanda P; Saul, Melissa; Nowalk, Mary Patricia; Saul, Sean; Sax, Theresa M; Eng, Heather; Zimmerman, Richard K; Balasubramani, Goundappa K
• Is Part Of: Open forum infectious diseases. Volume 6:Number 6(2019)
• Publication Date: 2019-06-10
• Subjects: acute respiratory infection -- influenza vaccination -- respiratory viral panel; Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofz231
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: A clinical informatics algorithm (CIA) was developed to systematically identify potential enrollees for a test-negative, case-control study to determine influenza vaccine effectiveness, to improve enrollment over manual records review. Further testing may enhance the CIA for increased efficiency. Methods: The CIA generated a daily screening list by querying all medical record databases for patients admitted in the last 3 days, using specified terms and diagnosis codes located in admission notes, emergency department notes, chief complaint upon registration, or presence of a respiratory viral panel charge or laboratory result (RVP). Classification and regression tree analysis (CART) and multivariable logistic regression were used to refine the algorithm. Results: Using manual records review, 204 patients (<4/day) were approached and 144 were eligible in the 2014–2015 season compared with 3531 (12/day) patients who were approached and 1136 who were eligible in the 2016–2017 season using a CIA. CART analysis identified RVP as the most important indicator from the CIA list for determining eligibility, identifying 65%–69% of the samples and predicting 1587 eligible patients. RVP was confirmed as the most significant predictor in regression analysis, with an odds ratio (OR) of 4.9 (95% confidence interval [CI], 4.0–6.0). Other significant factors were indicators in admission notes (OR, 2.3 [95% CI, 1.9–2.8]) and emergency department notes (OR, 1.8 [95% CI, 1.4–2.3]). Conclusions: This study supports the benefits of a CIA to facilitate recruitment of eligible participants in clinical research over manual records review. Logistic regression and CART identified potential eligibility screening criteria reductions to improve the CIA's efficiency. Abstract : A clinical informatics algorithm (CIA) was developed to systematically identify potential enrollees for an influenza vaccine effectiveness study that improved enrollment efficiency over manual record review. Classification and Regression Tree analysis indicated modifications to further increase the CIA's efficiency.