Identifying biomarkers for papilledema and pseudopapilledema Article Swipe
Rajnish Sekhri
,
Helen J. Kuht
,
Zhanhan Tu
,
Gail Maconachie
,
Ranjeetha Shenoy
,
Esha Prakash
,
Seema Teli
,
Sohaib R. Rufai
,
Nagini Sarvananthan
,
Tahir Islam
,
Michael Hisaund
,
Rebecca J. McLean
,
I. Choudhuri
,
Joris Dehaene
,
M. Barnes
,
Irène Gottlob
,
Ian De Silva
,
Mervyn G. Thomas
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-09778-2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-09778-2
Papilledema describes optic nerve head (ONH) swelling due to raised intracranial pressure which can lead to complications including visual loss. Differentiating it from pseudopapilledema is often challenging and may require invasive investigations. Optical Coherence Tomography (OCT) is a non-invasive imaging modality, allowing visualisation of the ONH. We identified OCT-derived biomarkers for papilledema and pseudopapilledema in the existing literature. 213 patients with confirmed papilledema, optic disc drusen (ODD), tilted optic discs (TOD) or crowded optic discs (COD) were included. OCT scans were analysed for qualitative and quantitative biomarkers, with sensitivity, specificity, and AUC calculated for individual and combined biomarkers. Logistic regression modelling including qualitative biomarkers to differentiate papilledema from ODD and COD demonstrated a sensitivity and specificity of 89% and AUC of 0.96. Inclusion of TOD reduced the sensitivity of the model to 66%. In a model differentiating papilledema from TOD, the best-performing biomarker achieved a sensitivity of 87%, specificity of 61% and AUC of 0.83. Using qualitative biomarkers, we identified a model with high sensitivity and specificity to differentiate papilledema from ODD and COD. Quantitative biomarkers displayed high AUCs for differentiating TOD from papilledema. Our findings show that OCT demonstrates promising utility in differentiating papilledema from pseudopapilledema.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-09778-2
- OA Status
- gold
- Cited By
- 2
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412176887
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412176887Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-09778-2Digital Object Identifier
- Title
-
Identifying biomarkers for papilledema and pseudopapilledemaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-10Full publication date if available
- Authors
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Rajnish Sekhri, Helen J. Kuht, Zhanhan Tu, Gail Maconachie, Ranjeetha Shenoy, Esha Prakash, Seema Teli, Sohaib R. Rufai, Nagini Sarvananthan, Tahir Islam, Michael Hisaund, Rebecca J. McLean, I. Choudhuri, Joris Dehaene, M. Barnes, Irène Gottlob, Ian De Silva, Mervyn G. ThomasList of authors in order
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https://doi.org/10.1038/s41598-025-09778-2Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1038/s41598-025-09778-2Direct OA link when available
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Papilledema, Computer science, Computational biology, Bioinformatics, Data science, Medicine, Biology, OphthalmologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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61Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Differentiating | 21 |
| abstract_inverted_index.best-performing | 142 |
| abstract_inverted_index.differentiating | 137, 181, 194 |
| abstract_inverted_index.investigations. | 32 |
| abstract_inverted_index.pseudopapilledema | 24, 54 |
| abstract_inverted_index.pseudopapilledema. | 197 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 18 |
| citation_normalized_percentile.value | 0.88698584 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |