Étude et rapport

An algorithm in ophthalmic emergencies to evaluate the necessity of physical consultation during COVID-19 lockdown in Paris: Experience of the first 100 patien

GRATUIT

Auteur(s) :

H. Bourdona, A. Herbauta, L. Trinha, E. Tuilb, J.F. Girmensb, C. Baudouina

Éditeur(s) :

Elsevier Masson France

Date de publication :10/02/2021

7 pages

EN BREF ...

"This study aimed to evaluate the ability of a freely accessible internet algorithm to correctly identify the need for emergency ophthalmologic consultation for correct diagnosis and management. This retrospective observational cohort study was based on the first 100 patients who requested recommendations on the necessity of breaking the lockdown for emergency ophthalmology consultation during the period from March to May 2020. Ninety-one patients completed questionnaires. Forty-nine were directed to emer- gency consultation and 42 to differed scheduled visits or telemedicine visits. One patient sent for emergency consultation had an overestimated severity and could have been seen later, while two patients initially recommended for a scheduled visit were considered appropriate for emergency consultation. However, these patients’ management did not suffer as a consequence of the delay. The sensitivity of the algorithm, defined as the number of emergency consul- tations suggested by the algorithm divided by the total number of emergency consultationsdeemed appropriate by the practitioner’s final evaluation, was 96.0%. The specificity of the algorithm, defined as the number of patients recommended for delayed consultation by the algorithm divided by the number of patients deemed clinically appropriate for this approach, was 97.5%. The positive predictive value, defined as the number of appropriate emergency consultations divided by the total number of emergency consultations suggested by the algo- rithm, was 97.9%. Finally, the negative predictive value, defined as the number of appropriately deferred patients divided by the number of deferred patients recommended by the algorithm, was 95.2%." En bref issu de l'étude.

Rédacteur(s) de la fiche : Beesens TEAM


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