6406004d788823662c084d080c1d2441 302127.pdf 4c07a0a661f957789a2f447f3618350cd6962e4e 302127.pdf 2f9166821337d59b4fe7af81c3c9e5101c5850a3e6a1497cd4220dc90ae80bd4 302127.pdf Title: Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up Subject: (1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92–0.94) for any DR and 0.90 (95% CI, 0.89–0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient. Keywords: diabetic retinopathy; algorithm; sensitivity; specificity; artificial intelligence Author: Pedro Romero-Aroca, Raquel Verges, Jordi Pascual-Fontanilles, Aida Valls, Josep Franch-Nadal, Xavier Mundet, Antonio Moreno, Josep Basora, Eugeni Garcia-Curto and Marc Baget-Bernaldiz Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Wed Apr 17 16:01:19 2024 CEST ModDate: Wed Apr 17 16:04:35 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 11 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1164769 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- BUQZGR+VnURWPalladioL Type 1 Custom yes yes yes 10 0 BPKXQM+URWPalladioL-Bold Type 1 Custom yes yes yes 16 0 WVPXEQ+URWPalladioL-Roma Type 1 Custom yes yes yes 22 0 OFJOGV+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 BXZUUH+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 43 0 EFEHGT+CMSY10 Type 1 Builtin yes yes yes 60 0 GELFIP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 78 0 GELFJA+PalatinoLinotype CID TrueType Identity-H yes yes yes 81 0 GELFJB+PalatinoLinotype TrueType WinAnsi yes yes no 87 0 GELFJD+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 90 0 GELGAB+PalatinoLinotype TrueType MacRoman yes yes no 93 0 GELFIP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 105 0 GELFJA+PalatinoLinotype CID TrueType Identity-H yes yes yes 108 0 GELFJB+PalatinoLinotype TrueType WinAnsi yes yes no 114 0 GELFJD+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 117 0 GELGAB+PalatinoLinotype TrueType MacRoman yes yes no 120 0 QDCWHF+EURM10 Type 1 Builtin yes yes yes 128 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-11-07 02:43:50 CET RepresentationInformation: 302127.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-11-06 14:05:54 CET Size: 1164769 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 277 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Setting Destination: subsection.2.1 Item: Title: Sample Size Destination: subsection.2.2 Item: Title: Inclusion Criteria Destination: subsection.2.3 Item: Title: Exclusion Criteria Destination: subsection.2.4 Item: Title: Construction of the Algorithm Destination: subsection.2.5 Item: Title: Statistical Methods Destination: subsection.2.6 Item: Title: Results Destination: section.3 Children: Item: Title: Demographic Data Destination: subsection.3.1 Item: Title: Statistical Analysis of the Confusion Matrix/Contingency Destination: subsection.3.2 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: section.6 Info: Title: Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up Author: Pedro Romero-Aroca, Raquel Verges, Jordi Pascual-Fontanilles, Aida Valls, Josep Franch-Nadal, Xavier Mundet, Antonio Moreno, Josep Basora, Eugeni Garcia-Curto and Marc Baget-Bernaldiz Subject: (1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92 0.94) for any DR and 0.90 (95% CI, 0.89 0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient. 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