Evidence map›Paper›PMID 35737376›Full record

ArticleTranslational vision science & technology2022

HIOP-Reader: Automated Data Extraction for the Analysis of Manually Recorded Nycthemeral IOPs and Glaucoma Progression.

Vaia Agorastou, Julian Schön, Raoul Verma-Fuehring, Mohamad Dakroub, Jost Hillenkamp, Frank Puppe, Nils A Loewen

Abstract read
In one paragraph

Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Inter-eye correlation analysis of 24-h IOPs and glaucoma progression.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Vaia AgorastouDepartment of Ophthalmology, University of Würzburg, Würzburg, Germany.
Julian SchönInstitute for Artificial Intelligence and Knowledge Systems, Department of Informatics, University of Würzburg, Würzburg, Germany.
Raoul Verma-FuehringDepartment of Ophthalmology, University of Würzburg, Würzburg, Germany.
Mohamad DakroubDepartment of Ophthalmology, University of Würzburg, Würzburg, Germany.
Jost HillenkampDepartment of Ophthalmology, University of Würzburg, Würzburg, Germany.
Frank PuppeInstitute for Artificial Intelligence and Knowledge Systems, Department of Informatics, University of Würzburg, Würzburg, Germany.
Nils A LoewenDepartment of Ophthalmology, University of Würzburg, Würzburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Nycthemeral (24-hour) intraocular pressure (IOP) monitoring in glaucoma has been used in Europe for more than 100 years to detect peaks missed during regular office hours. Data supporting this practice are lacking, because it is difficult to correlate manually drawn IOP curves to objective glaucoma progression. To address this, we developed an automated IOP data extraction tool, HIOP-Reader. Methods: Machine learning image analysis software extracted IOP data from hand-drawn, nycthemeral IOP curves of 225 retrospectively identified patients with glaucoma. The relationship between demographic parameters, IOP, and mean ocular perfusion pressure (MOPP) data to spectral-domain optical coherence tomography (SDOCT) data was analyzed. Sensitivities and specificities for the historical cutoff values of 15 mm Hg and 22 mm Hg in detecting glaucoma progression were calculated. Results: Machine data extraction was 119 times faster than manual data extraction. The IOP average was 15.2 ± 4.0 mm Hg, nycthemeral IOP variation was 6.9 ± 4.2 mm Hg, and MOPP was 59.1 ± 8.9 mm Hg. Peak IOP occurred at 10 am and trough at 9 pm. Progression occurred mainly in the temporal-superior and temporal-inferior SDOCT sectors. No correlation could be established between demographic, IOP, or MOPP variables and disease progression on OCT. The sensitivity and specificity of both cutoff points (15 and 22 mm Hg) were insufficient to be clinically useful. Outpatient IOPs were noninferior to nycthemeral IOPs. Conclusions: IOP data obtained during a single visit make for a poor diagnostic tool, no matter whether obtained using nycthemeral measurements or during outpatient hours. Translational Relevance: HIOP-Reader rapidly extracts manually recorded IOP data to allow critical analysis of existing databases.

Indexed as

GlaucomaGlaucoma, Open-AngleCircadian RhythmHumansIntraocular PressureRetrospective StudiesTonometry, Ocular

Identifiers

PMID35737376
PMCPMC9233288

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.