{Reference Type}: Journal Article
{Title}: Assessing the Performance of a Novel Bayesian Algorithm at Point of Care for Red Eye Complaints.
{Author}: Deans AM;Basilious A;Hutnik CM;
{Journal}: Vision (Basel)
{Volume}: 6
{Issue}: 4
{Year}: Oct 2022 24
暂无{DOI}: 10.3390/vision6040064
{Abstract}: The current diagnostic aids for red eye are static flowcharts that do not provide dynamic, stepwise workups. The diagnostic accuracy of a novel dynamic Bayesian algorithm for red eye was tested. Fifty-seven patients with red eye were evaluated by an emergency medicine physician who completed a questionnaire about symptoms/findings (without requiring extensive slit lamp findings). An ophthalmologist then attributed an independent "gold-standard diagnosis". The algorithm used questionnaire data to suggest a differential diagnosis. The referrer's diagnostic accuracy was 70.2%, while the algorithm's accuracy was 68.4%, increasing to 75.4% with the algorithm's top two diagnoses included and 80.7% with the top three included. In urgent cases of red eye (n = 26), the referrer diagnostic accuracy was 76.9%, while the algorithm's top diagnosis was 73.1% accurate, increasing to 84.6% (top two included) and 88.5% (top three included). The algorithm's sensitivity for urgent cases was 76.9% (95% CI: 56-91%) using its top diagnosis, with a specificity of 93.6% (95% CI: 79-99%). This novel algorithm provides dynamic workups using clinical symptoms, and may be used as an adjunct to clinical judgement for triaging the urgency of ocular causes of red eye.