Evidence map›Paper›PMID 35300029›Full record

ArticleClinical ophthalmology (Auckland, N.Z.)2022

Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population.

Pedro Romero-Aroca, Marc Baget-Bernaldiz, Raul Navarro-Gil, Albert Feliu, Najla Maarof, Antonio Moreno, Julian Cristiano, Aida Valls

Abstract read
In one paragraph

Article in Clinical ophthalmology (Auckland, N.Z.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Pedro Romero-ArocaOphthalmology Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain.ORCID 0000-0002-7061-8987
Marc Baget-BernaldizOphthalmology Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain.
Raul Navarro-GilOphthalmology Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain.
Albert FeliuPediatric Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain.
Najla MaarofDepartment of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain.
Antonio MorenoDepartment of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain.ORCID 0000-0003-3945-2314
Julian CristianoDepartment of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain.ORCID 0000-0001-6317-6538
Aida VallsDepartment of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain.ORCID 0000-0003-3616-7809

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: The aim of the present study was to build a clinical decision support system (CDSS) that can predict the presence of diabetic retinopathy (DR) in type 1 diabetes (T1DM) patients. Material and Method: We built two versions of our CDSS to predict the presence of any-type DR and sight-threatening DR (STDR) in T1DM patients. The first version was trained using 324 T1DM and 826 T2DM patients. The second was trained with only the 324 T1DM patients. Results: The first version achieved an accuracy (ACC) = 0.795, specificity (SP) = 83%, and sensitivity (S) = 65.7% to predict the presence of any-DR, and an ACC = 0.918, SP = 87.1% and S = 87.8% for STDR. The second model achieved ACC = 0.799, SP = 87.5% and S = 86.3% when predicting any-DR and ACC = 0.937, SP = 90.9% and S = 83.0% for STDR. Conclusion: The two models better predict STDR than any-DR in T1DM patients. We will need a larger sample to strengthen our results.

Indexed as

clinical decision support systemdiabetic retinopathytype 1 diabetes mellitus

Identifiers

PMID35300029
PMCPMC8921832

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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.