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Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss
The current diagnostic aids for acute vision loss are static flowcharts that do not provide dynamic, stepwise workups. We tested the diagnostic accuracy of a novel dynamic Bayesian algorithm for acute vision loss. Seventy-nine “participants” with acute vision loss in Windsor, Canada were assessed by...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036270/ https://www.ncbi.nlm.nih.gov/pubmed/35466273 http://dx.doi.org/10.3390/vision6020021 |
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author | Basilious, Amy Govas, Chris N. Deans, Alexander M. Yoganathan, Pradeepa Deans, Robin M. |
author_facet | Basilious, Amy Govas, Chris N. Deans, Alexander M. Yoganathan, Pradeepa Deans, Robin M. |
author_sort | Basilious, Amy |
collection | PubMed |
description | The current diagnostic aids for acute vision loss are static flowcharts that do not provide dynamic, stepwise workups. We tested the diagnostic accuracy of a novel dynamic Bayesian algorithm for acute vision loss. Seventy-nine “participants” with acute vision loss in Windsor, Canada were assessed by an emergency medicine or primary care provider who completed a questionnaire about ocular symptoms/findings (without requiring fundoscopy). An ophthalmologist then attributed an independent “gold-standard diagnosis”. The algorithm employed questionnaire data to produce a differential diagnosis. The referrer diagnostic accuracy was 30.4%, while the algorithm’s accuracy was 70.9%, increasing to 86.1% with the algorithm’s top two diagnoses included and 88.6% with the top three included. In urgent cases of vision loss (n = 54), the referrer diagnostic accuracy was 38.9%, while the algorithm’s top diagnosis was correct in 72.2% of cases, increasing to 85.2% (top two included) and 87.0% (top three included). The algorithm’s sensitivity for urgent cases using the top diagnosis was 94.4% (95% CI: 85–99%), with a specificity of 76.0% (95% CI: 55–91%). This novel algorithm adjusts its workup at each step using clinical symptoms. In doing so, it successfully improves diagnostic accuracy for vision loss using clinical data collected by non-ophthalmologists. |
format | Online Article Text |
id | pubmed-9036270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90362702022-04-26 Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss Basilious, Amy Govas, Chris N. Deans, Alexander M. Yoganathan, Pradeepa Deans, Robin M. Vision (Basel) Article The current diagnostic aids for acute vision loss are static flowcharts that do not provide dynamic, stepwise workups. We tested the diagnostic accuracy of a novel dynamic Bayesian algorithm for acute vision loss. Seventy-nine “participants” with acute vision loss in Windsor, Canada were assessed by an emergency medicine or primary care provider who completed a questionnaire about ocular symptoms/findings (without requiring fundoscopy). An ophthalmologist then attributed an independent “gold-standard diagnosis”. The algorithm employed questionnaire data to produce a differential diagnosis. The referrer diagnostic accuracy was 30.4%, while the algorithm’s accuracy was 70.9%, increasing to 86.1% with the algorithm’s top two diagnoses included and 88.6% with the top three included. In urgent cases of vision loss (n = 54), the referrer diagnostic accuracy was 38.9%, while the algorithm’s top diagnosis was correct in 72.2% of cases, increasing to 85.2% (top two included) and 87.0% (top three included). The algorithm’s sensitivity for urgent cases using the top diagnosis was 94.4% (95% CI: 85–99%), with a specificity of 76.0% (95% CI: 55–91%). This novel algorithm adjusts its workup at each step using clinical symptoms. In doing so, it successfully improves diagnostic accuracy for vision loss using clinical data collected by non-ophthalmologists. MDPI 2022-04-04 /pmc/articles/PMC9036270/ /pubmed/35466273 http://dx.doi.org/10.3390/vision6020021 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Basilious, Amy Govas, Chris N. Deans, Alexander M. Yoganathan, Pradeepa Deans, Robin M. Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss |
title | Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss |
title_full | Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss |
title_fullStr | Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss |
title_full_unstemmed | Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss |
title_short | Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss |
title_sort | evaluating the diagnostic accuracy of a novel bayesian decision-making algorithm for vision loss |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036270/ https://www.ncbi.nlm.nih.gov/pubmed/35466273 http://dx.doi.org/10.3390/vision6020021 |
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