Version 1
: Received: 21 June 2021 / Approved: 23 June 2021 / Online: 23 June 2021 (11:13:08 CEST)
Version 2
: Received: 30 June 2021 / Approved: 1 July 2021 / Online: 1 July 2021 (11:08:41 CEST)
How to cite:
Agorastou, V.; Schoen, J.; Verma-Fuehring, R.; Dakroub, M.; Hillenkamp, J.; Puppe, F.; Loewen, N.A. HIOP-Reader: Automated Data Extraction for the Analysis of Manually Recorded Nycthemeral IOPs and Glaucoma Progression. Preprints2021, 2021060571. https://doi.org/10.20944/preprints202106.0571.v2
Agorastou, V.; Schoen, J.; Verma-Fuehring, R.; Dakroub, M.; Hillenkamp, J.; Puppe, F.; Loewen, N.A. HIOP-Reader: Automated Data Extraction for the Analysis of Manually Recorded Nycthemeral IOPs and Glaucoma Progression. Preprints 2021, 2021060571. https://doi.org/10.20944/preprints202106.0571.v2
Agorastou, V.; Schoen, J.; Verma-Fuehring, R.; Dakroub, M.; Hillenkamp, J.; Puppe, F.; Loewen, N.A. HIOP-Reader: Automated Data Extraction for the Analysis of Manually Recorded Nycthemeral IOPs and Glaucoma Progression. Preprints2021, 2021060571. https://doi.org/10.20944/preprints202106.0571.v2
APA Style
Agorastou, V., Schoen, J., Verma-Fuehring, R., Dakroub, M., Hillenkamp, J., Puppe, F., & Loewen, N.A. (2021). HIOP-Reader: Automated Data Extraction for the Analysis of Manually Recorded Nycthemeral IOPs and Glaucoma Progression. Preprints. https://doi.org/10.20944/preprints202106.0571.v2
Chicago/Turabian Style
Agorastou, V., Frank Puppe and Nils A. Loewen. 2021 "HIOP-Reader: Automated Data Extraction for the Analysis of Manually Recorded Nycthemeral IOPs and Glaucoma Progression" Preprints. https://doi.org/10.20944/preprints202106.0571.v2
Abstract
Purpose: Nycthemeral (24-hour) glaucoma inpatient intraocular pressure (IOP) monitoring has been used in Europe for more than 100 years to detect peaks missed during regular office hours. Data supporting this practice is lacking, partially because it is difficult to correlate manually drawn IOP curves to objective glaucoma progression. To address this, we deployed automated IOP data extraction tools and tested for a correlation to a progressive retinal nerve fiber layer loss on spectral-domain optical coherence tomography (SDOCT). Methods: We created and deployed a machine-learning image analysis software to extract IOP data from hand-drawn, nycthemeral IOP curves of 225 retrospectively identified glaucoma patients. The relationship between demographic parameters, IOP and mean ocular perfusion pressure (MOPP) data to SDOCT data was analyzed. Sensitivities and specificities for the historical cut-off values of 15 mmHg and 22 mmHg in detecting glaucoma progression were calculated. Results: IOP data could be extracted efficiently. The IOP average was 15.2±4.0 mmHg, nycthemeral IOP variation was 6.9±4.2 mmHg, and MOPP was 59.1±8.9 mmHg. Peak IOP occurred at 10 AM and trough at 9 PM. Disease progression occurred mainly in the temporal-superior and -inferior SDOCT sectors. No correlation could be established between demographic, IOP, or MOPP parameters and SDOCT disease progression. The sensitivity and specificity of both cut-off points (15 and 22 mmHg) were insufficient to be clinically useful. Outpatient IOPs were non-inferior to nycthemeral IOPs. Conclusion: IOP data obtained during a single visit make for a poor diagnostic tool, no matter whether obtained using nycthemeral measurements or during outpatient hours.
Keywords
glaucoma progression; nycthemeral intraocular pressure; mean ocular perfusion pressure
Subject
Medicine and Pharmacology, Ophthalmology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Mohamad Dakroub
Commenter's Conflict of Interests: Author