ArticleOpen forum infectious diseases2019
Determination of Eligibility for Influenza Research: A Clinical Informatics Approach.
Article in Open forum infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundA 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.
methodsThe 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.
resultsUsing 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]).
conclusionsThis 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.