# Classification Maps: A New Mathematical Tool Supporting the Diagnosis of Age-Related Macular Degeneration

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

- AREDS 1 group (as a control group)—no or only a few small drusen with a diameter of <63 $\mathsf{\mu}$m;
- AREDS 2 group—early form of AMD—the co-occurrence of numerous small drusen with a diameter of >15 $\mathsf{\mu}$m, several drusen with a diameter of 63–125 $\mathsf{\mu}$m or RPE abnormalities in the form of increased pigmentation or depigmentation;
- AREDS 3 group—moderate AMD—numerous medium-sized drusen, at least one large druse > 125 $\mathsf{\mu}$m in diameter, geographic atrophy not occupying the center of the macula;
- AREDS 4 group—advanced form of AMD—geographic atrophy of the RPE with involvement of the macula, neovascular maculopathy, which includes: CNV, i.e., pathological vessels originating from the choroid, serous or hemorrhagic retinal detachment or RPE, exudation and hard, fibrovascular proliferations under the retina and under the RPE, discoid scar (choroidal fibrosis) [45,81].

## 3. Results and Discussion

- AREDS 4 using (CRT, ETDRS) variables—Figure 1;
- AREDS 4 using (CRT, Snellen) variables—Figure 2;
- AREDS 4 using (CRT, Age) variables—Figure 3;
- AREDS 4 using (GCC, ETDRS) variables—Figure 4;
- AREDS 4 using (GCC, Snellen) variables—Figure 5;
- AREDS 4 using (GCC, Age) variables—Figure 6;
- AREDS 4 using (ETDRS, Age) variables—Figure 7.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Fleckenstein, M.; Keenan, T.D.L.; Guymer, R.H.; Chakravarthy, U.; Schmitz-Valckenberg, S.; Klaver, C.C.; Wong, W.T.; Chew, E.Y. Age-related macular degeneration. Nat. Rev. Dis. Prim.
**2021**, 7, 31. [Google Scholar] [CrossRef] [PubMed] - Age-related Eye Disease Study Research Group. Potential public health impact of age-related disease study results. Arch. Ophthalmol.
**2003**, 12, 1621–1624. [Google Scholar] - Pennington, K.; DeAngelis, M. Epidemiology of age-related macular degeneration (AMD): Asssociations with cardiovascular disease phenotypes and lipid factors. Eye Vis.
**2016**, 3, 34. [Google Scholar] [CrossRef] [Green Version] - Partyka, O.; Wysocki, M.J. Epidemiology of eye diseases and infrastructure of ophthalmology in Poland. Przegl. Epidemiol.
**2015**, 69, 905–908. [Google Scholar] - Deng, Y.; Qiao, L.; Du, M.; Qu, C.; Wan, L.; Li, J.; Huang, L. Age-related macular degeneration: Epidemiology, genetics, pathophysiology, diagnosis, and targeted therapy. Genes Dis.
**2021**, 9, 62–79. [Google Scholar] [CrossRef] [PubMed] - Wong, W.L.; Su, X.; Li, X.; Cheung, C.M.G.; Klein, R.; Cheng, C.Y.; Wong, T.Y. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: A systematic review and meta-analysis. Lancet Glob. Health
**2014**, 2, 106–116. [Google Scholar] [CrossRef] [Green Version] - Rudnicka, A.R.; Jarrar, Z.; Wormald, R.; Cook, D.G.; Fletcher, A.; Owen, C.G. Age and gender variations in age-related macular degeneration prevalence in populations of European ancestry: A meta-analysis. Ophthalmology
**2012**, 119, 571–580. [Google Scholar] [CrossRef] - McConnell, V.; Silvestri, G. Age-related macular degeneration. Ulster Med. J.
**2005**, 74, 82–92. [Google Scholar] - Gass, J.D. Drusen and disciform macular detachment and degeneration. Arch. Ophthalmol.
**1973**, 90, 206–217. [Google Scholar] [CrossRef] [Green Version] - Gass, J.D. Pathogenesis of disciform detachment of the neuroepithelium. Am. J. Ophthalmol.
**1967**, 63, 573–585. [Google Scholar] - Wojtkowski, M.; Bajraszewski, T.; Targowski, P.; Kowalczyk, A. Real-time in vivo imaging by high-speed spectral optical coherence tomography. Opt. Lett.
**2003**, 28, 1745–1747. [Google Scholar] [CrossRef] [PubMed] - Wojtkowski, M.; Bajraszewski, T.; Gorczyńska, I.; Targowski, P.; Kowalczyk, A.; Wasilewski, W.; Radzewicz, C. Ophthalmic imaging by spectral optical coherence tomography. Am. J. Ophthalmol.
**2004**, 138, 412–419. [Google Scholar] - Gin, T.J.; Wu, Z.; Chew, S.K.; Guymer, R.H.; Luu, C.D. Quantitative analysis of the ellipsoid zone intensity in phenotypic variations of intermediate age-related macular degeneration. Investig. Ophthalmol. Vis. Sci.
**2017**, 58, 2079–2086. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bezerra, H.G.; Costa, M.A.; Guagliumi, G.; Rollins, A.M.; Simon, D.I. Intracoronary optical coherence tomography: A comprehensive review clinical and research applications. JACC Cardiovasc. Interv.
**2009**, 2, 1035–1046. [Google Scholar] [CrossRef] [Green Version] - Nguyen, F.T.; Zysk, A.M.; Chaney, E.J.; Adie, S.G.; Kotynek, J.G.; Oliphant, U.J.; Bellafiore, F.J.; Rowland, K.M.; Johnson, P.A.; Boppart, S.A. Optical coherence tomography: The intraoperative assessment of lymph nodes in breast cancer. IEEE Eng. Med. Biol. Mag.
**2010**, 29, 63–70. [Google Scholar] [CrossRef] [Green Version] - Tsai, T.H.; Fujimoto, J.G.; Mashimo, H. Endoscopic Optical Coherence Tomography for Clinical Gastroenterology. Diagnostics
**2014**, 4, 57–93. [Google Scholar] [CrossRef] [Green Version] - Schuman, J.S.; Puliafito, C.A.; Fujimoto, J.G. Optical Coherence Tomography of Ocular Diseases, 2nd ed.; Slack Inc.: Thorofare, NJ, USA, 2004. [Google Scholar]
- de Carlo, T.E.; Romano, A.; Waheed, N.K.; Duker, J.S. A review of optical coherence tomography angiography (OCTA). Int. J. Retin. Vitr.
**2015**, 1, 5. [Google Scholar] [CrossRef] [Green Version] - Fontaine, V.; Balducci, C.; Dinan, L.; Monteiro, E.; Boumedine, T.; Fournié, M.; Nguyen, V.; Guibout, L.; Clatot, J.; Latil, M.; et al. Anti-Inflammatory Effects and Photo- and Neuro-Protective Properties of BIO203, a New Amide Conjugate of Norbixin, in Development for the Treatment of Age-Related Macular Degeneration (AMD). Int. J. Mol. Sci.
**2023**, 24, 5296. [Google Scholar] [CrossRef] - Li, W.; Chen, L.; Gu, Z.; Chen, Z.; Li, H.; Cheng, Z.; Li, H.; Zou, L. Co-delivery of microRNA-150 and quercetin by lipid nanoparticles (LNPs) for the targeted treatment of age-related macular degeneration (AMD). J. Control. Release
**2023**, 355, 358–370. [Google Scholar] [CrossRef] - de Koning-Backus, A.P.M.; Kiefte-de Jong, J.C.; van Rooij, J.G.J.; AMD-Life Team; Uitterlinden, A.G.; Voortman, T.G.; Meester-Smoor, M.A.; Klaver, C.C.W. Lifestyle Intervention Randomized Controlled Trial for Age-Related Macular Degeneration (AMD-Life): Study Design. Nutrients
**2023**, 15, 602. [Google Scholar] [CrossRef] - Liu, T.Y.A.; Wang, J.; Csaky, K.G. Correlation between hyperreflective foci and visual function testing in eyes with intermediate age-related macular degeneration. Int. J. Retin. Vitr.
**2023**, 9, 24. [Google Scholar] [CrossRef] - Maeda, T.; Sugita, S.; Kurimoto, Y.; Takahashi, M. Trends of Stem Cell Therapies in Age-Related Macular Degeneration. J. Clin. Med.
**2021**, 10, 1785. [Google Scholar] [CrossRef] [PubMed] - Yin, J.; Fang, K.; Gao, Y.; Ou, L.; Yuan, S.; Xin, C.; Wu, W.; Wu, W.W.; Hong, J.; Yang, H.; et al. Safeguarding genome integrity during gene-editing therapy in a mouse model of age-related macular degeneration. Nat. Commun.
**2022**, 13, 7867. [Google Scholar] [CrossRef] - Jung, W.; Park, J.; Jang, H.R.; Jeon, J.; Han, K.; Kim, B.; Yoon, J.M.; Lim, D.H.; Shin, D.W. Increased end-stage renal disease risk in age-related macular degeneration: A nationwide cohort study with 10-year follow-up. Sci. Rep.
**2023**, 13, 183. [Google Scholar] [CrossRef] - Sanabria, M.R.; Calles-Monar, P.S.; Alonso-Tarancón, A.M.; Coco-Martín, R.M.; Mayo-Iscar, A. Impact of COVID-19 Confinement on Quality of Life of Patients with Age-Related Macular Degeneration: A Two-Wave Panel Study. J. Clin. Med.
**2023**, 12, 2394. [Google Scholar] [CrossRef] [PubMed] - Choi, Y.A.; Jeong, A.; Woo, C.H.; Cha, S.C.; Park, D.Y.; Sagong, M. Aqueous microRNA profiling in age-related macular degeneration and polypoidal choroidal vasculopathy by next-generation sequencing. Sci. Rep.
**2023**, 13, 1274. [Google Scholar] [CrossRef] - Bowling, B.; Kanski, J. Okulistyka Kliniczna; Edra Urban & Partner: Wrocław, Poland, 2017; pp. 584–585. (In Polish) [Google Scholar]
- Hadziahmetovic, M.; Malek, G. Age-Related Macular Degeneration Revisited: From Pathology and Cellular stress to Potential Therapies. Front. Cell. Dev. Biol.
**2021**, 8, 612812. [Google Scholar] [CrossRef] [PubMed] - Stefánsson, E.; Geirsdóttir, A.; Sigurdsson, H. Metabolic physiology in age related macular degeneration. Prog. Retin. Eye Res.
**2011**, 30, 72–80. [Google Scholar] [CrossRef] - Dorrell, M.; Uusitalo-Jarvinen, H.; Aguilar, E.; Friedlander, M. Ocular neo-vascularization: Basic mechanisms and therapeutic advances. Surv. Ophthalmol.
**2007**, 52 (Suppl. 1), S3–S19. [Google Scholar] [CrossRef] - Matonti, F.; Korobelnik, J.F.; Dot, C.; Gualino, V.; Soler, V.; Mrejen, S.; Delyfer, M.N.; Baillif, S.; Streho, M.; Gascon, P.; et al. Comparative Effectiveness of Intravitreal Anti-Vascular Endothelial Growth Factor Therapies for Managing Neovascular Age-Related Macular Degeneration: A Meta-Analysis. J. Clin. Med.
**2022**, 11, 1834. [Google Scholar] [CrossRef] - Pikuleva, I.A.; Curcio, C.A. Cholesterol in the retina: The best is yet to come. Prog. Retin. Eye Res.
**2014**, 41, 64–89. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Fliesler, S.J.; Bretillon, L. The ins and outs of cholesterol in the vertebrate retina. J. Lipid Res.
**2010**, 51, 3399–3413. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mullins, R.F.; Russell, S.R.; Anderson, D.H.; Hageman, G.S. Drusen associated with aging and age-related macular degeneration contain proteins common to extracellular deposits associated with atherosclerosis, elastosis, amyloidosis, and dense deposit disease. FASEB J.
**2000**, 14, 835–846. [Google Scholar] [CrossRef] - Booij, J.C.; Baas, D.C.; Beisekeeva, J.; Gorgels, T.G.; Bergen, A.A. The dynamic nature of Bruch’s membrane. Prog. Retin. Eye Res.
**2010**, 29, 1–18. [Google Scholar] [CrossRef] [PubMed] - Friedman, E. A hemodynamic model of the pathogenesis of age-related macular degeneration. Am. J. Ophthalmol.
**1997**, 124, 677–682. [Google Scholar] [CrossRef] - Friedman, E. The role of the atherosclerotic process in the pathogenesis of age-related macular degeneration. Am. J. Ophthalmol.
**2000**, 130, 658–663. [Google Scholar] [CrossRef] - Friedman, E. Update of the vascular model of AMD. Br. J. Ophthalmol.
**2004**, 88, 161–163. [Google Scholar] [CrossRef] [Green Version] - Machalińska, A.; Kawa, M.P.; Marlicz, W.; Machaliński, B. Complement system activation and endothelial dysfunction in patients with age-related macular degeneration (AMD): Possible relationship between AMD and atherosclerosis. Acta Ophthalmol.
**2012**, 90, 695–703. [Google Scholar] [CrossRef] - Tan, P.L.; Rickman, C.B.; Katsanis, N. AMD and the alternative complement pathway: Genetics and functional implications. Hum. Genomics.
**2016**, 10, 23. [Google Scholar] [CrossRef] [Green Version] - Nozaki, M.; Raisler, B.J.; Sakurai, E.; Sarma, J.V.; Barnum, S.R.; Lambris, J.D.; Chen, Y.; Zhang, K.; Ambati, B.K.; Baffi, J.Z.; et al. Drusen complement components C3a and C5a promote choroidal neovascularization. Proc. Natl. Acad. Sci. USA
**2006**, 103, 2328–2333. [Google Scholar] [CrossRef] [Green Version] - Hollyfield, J.G.; Bonilha, V.L.; Rayborn, M.E.; Yang, X.; Shadrach, K.G.; Lu, L.; Ufret, R.L.; Salomon, R.G.; Perez, V.L. Oxidative damage-induced inflammation initiates age-related macular degeneration. Nat. Med.
**2008**, 14, 194–198. [Google Scholar] [CrossRef] [Green Version] - Zhou, J.; Jang, Y.P.; Kim, S.R.; Sparrow, J.R. Complement activation by photooxidation products of A2E, a lipofuscin constituent of the retinal pigment epithelium. Proc. Natl. Acad. Sci. USA
**2006**, 103, 16182–16187. [Google Scholar] [CrossRef] [Green Version] - Age-Related Eye Disease Study Research Group. The Age-Related Eye Disease Study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: The Age-Related Eye Disease Study Report Number 6. Am. J. Ophthalmol.
**2001**, 132, 668–681. [Google Scholar] [CrossRef] - Age-Related Eye Disease Study Research Group. A Randomized, Placebo-Controlled, Clinical Trial of High-Dose Supplementation With Vitamins C and E, Beta Carotene, and Zinc for Age-Related Macular Degeneration and Vision Loss: AREDS Report No. 8. Arch. Ophthalmol.
**2001**, 119, 1417–1436. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bielińska, A.; Majkowicz, M.; Wąż, P.; Bielińska-Wąż, D. A New Computational Method: Interdisciplinary Classification Analysis. AIP Conf. Proc.
**2019**, 2116, 450014. [Google Scholar] - Bielińska, A.; Majkowicz, M.; Bielińska-Wąż, D.; Wąż, P. A New Method in Bioinformatics—Interdisciplinary Similarity Studies. AIP Conf. Proc.
**2019**, 2116, 450013. [Google Scholar] - Bielińska-Wąż, D.; Wąż, P.; Basak, S.C. Similarity studies using statistical and genetical methods. J. Math. Chem.
**2007**, 42, 1003–1013. [Google Scholar] [CrossRef] - Hennemann, M.; Clark, T. A QSPR-Approach to the Estimation of the pK
_{HB}of Six-Membered Nitrogen-Heterocycles using Quantum Mechanically Derived Descriptors. J. Mol. Model.**2002**, 8, 95–101. [Google Scholar] [CrossRef] - Randić, M. On of molecular similarity based on a single molecular descriptor. Chem. Phys. Lett.
**2014**, 599, 1–6. [Google Scholar] [CrossRef] - Toropov, A.A.; Carbó-Dorca, R.; Toropova, A.P. Index of Ideality of Correlation: New possibilities to validate QSAR: A case study. Struct. Chem.
**2018**, 29, 33–38. [Google Scholar] [CrossRef] - Basak, S.C. Some Comments on the Three-Pronged Chemobiodescriptor Approach to QSAR-A Historical View of the Emerging Integration. Curr. Comput. Aided Drug Des.
**2021**, 17, 703–707. [Google Scholar] [CrossRef] [PubMed] - Carbó-Dorca, R. Quantum similarity and QSPR in Euclidean-, and Minkowskian–Banach spaces. J. Math. Chem.
**2023**, 61, 1016–1035. [Google Scholar] [CrossRef] - Wąż, P.; Bielińska-Wąż, D. Moments of Inertia of Spectra and Distribution Moments as Molecular Descriptors. MATCH Commun. Math. Comput. Chem.
**2013**, 70, 851–865. [Google Scholar] - Bielińska-Wąż, D.; Wąż, P. Correlations in spectral statistics. J. Math. Chem.
**2008**, 43, 1287–1300. [Google Scholar] [CrossRef] - Bielińska, A.; Bielińska-Wąż, D.; Wąż, P. Classification Maps in Studies on the Retirement Threshold. Appl. Sci.
**2020**, 10, 1282. [Google Scholar] [CrossRef] [Green Version] - Bielińska, A.; Wąż, P.; Bielińska-Wąż, D. A Computational Model of Similarity Analysis in Quality of Life Research: An Example of Studies in Poland. Life
**2022**, 12, 56. [Google Scholar] [CrossRef] - Wąż, P.; Bielińska-Wąż, D. 3D-dynamic representation of DNA sequences. J. Mol. Model.
**2014**, 20, 2141. [Google Scholar] [CrossRef] [Green Version] - Bielińska-Wąż, D.; Wąż, P. Non-standard bioinformatics characterization of SARS-CoV-2. Comput. Biol. Med.
**2021**, 131, 104247. [Google Scholar] [CrossRef] - Randić, M.; Novič, M.; Plavšić, D. Milestones in graphical bioinformatics. Int. J. Quant. Chem.
**2013**, 113, 2413–2446. [Google Scholar] [CrossRef] - Aram, V.; Iranmanesh, A.; Majid, Z. Spider representation of DNA sequences. J. Comput. Theor. Nanosci.
**2014**, 11, 418–420. [Google Scholar] [CrossRef] - Jin, X.; Jiang, Q.; Chen, Y.; Lee, S.J.; Nie, R.; Yao, S.; Zhou, D.; He, K. Similarity/dissimilarity calculation methods of DNA sequences: A survey. J. Mol. Graph. Model.
**2017**, 76, 342–355. [Google Scholar] [CrossRef] [PubMed] - Hu, H.; Li, Z.; Dong, H.; Zhou, T. Graphical Representation and Similarity Analysis of Protein Sequences Based on Fractal Interpolation. IEEE/ACMTrans. Comput. Biol. Bioinform.
**2017**, 14, 182–192. [Google Scholar] [CrossRef] [PubMed] - Bielińska-Wąż, D.; Wąż, P. Spectral-dynamic representation of DNA sequences. J. Biomed. Inform.
**2017**, 72, 1–7. [Google Scholar] [CrossRef] - Mizuta, S. Graphical Representation of Biological Sequences. In Bioinformatics in the Era of Post Genomics and Big Data; Abdurakhmonov, I.Y., Ed.; IntechOpen: London, UK, 2018. [Google Scholar]
- Mahmoodi-Reihani, M.; Abbasitabar, F.; Zare-Shahabadi, V. A novel graphical representation and similarity analysis of protein sequences based on physicochemical properties. Physica A
**2018**, 510, 477–485. [Google Scholar] [CrossRef] - Xie, G.S.; Jin, X.B.; Yang, C.L.; Pu, J.X.; Mo, Z.X. Graphical Representation and Similarity Analysis of DNA Sequences Based on Trigonometric Functions. Acta Biotheor.
**2018**, 66, 113–133. [Google Scholar] [CrossRef] [PubMed] - Liu, H.L. 2D graphical representation of dna sequence based on horizon lines from a probabilistic view. Biosci. J.
**2018**, 34, 744–750. [Google Scholar] [CrossRef] - Xie, X.L.; Zhao, Y.X. A 2D Non-degeneracy Graphical Representation of Protein Sequence and Its Applications. Curr. Bionformatics
**2020**, 15, 758–766. [Google Scholar] [CrossRef] - Wu, R.X.; Liu, W.J.; Mao, Y.Y.; Zheng, J. 2D Graphical Representation of DNA Sequences Based on Variant Map. IEEE Access
**2020**, 8, 173755–173765. [Google Scholar] [CrossRef] - Puell, M.C.; Hurtado-Cena, F.J.; Pérez-Carrasco, M.J.; Contreras, I. Association between central retinal thickness and low luminance visual acuity in early age-related macular degeneration. Eur. J. Ophthalmol.
**2021**, 31, 2467–2473. [Google Scholar] [CrossRef] - Lee, Y.H.; Kim, Y.C. Central retinal thickness changes and risk of neovascular glaucoma after intravitreal bevacizumab injection in patients with central retinal vein occlusion. Sci. Rep.
**2022**, 12, 2051. [Google Scholar] [CrossRef] - Guo, Y.; Wu, J.; Zheng, X.; Yin, C.; Wu, Z. The First-Year Variation in Central Retinal Thickness Predicts Legal Blindness in Patients with Neovascular Age-Related Macular Degeneration. Ophthalmic Res.
**2022**, 66, 406–412. [Google Scholar] [CrossRef] [PubMed] - Yenice, E.; Şengün, A.; Soyugelen, D.G.; Turaçli, E. Ganglion cell complex thickness in nonexudative age-related macular degeneration. Eye
**2015**, 29, 1076–1080. [Google Scholar] [CrossRef] [Green Version] - Buyukavsar, C.; Sonmez, M.; Sagdic, S.K.; Unal, M.H. Relationship between ganglion cell complex thickness and vision in age-related macular degeneration treated with aflibercept. Eur. J. Ophthalmol.
**2022**. [Google Scholar] [CrossRef] [PubMed] - Ozawa, Y.; Shigeno, Y.; Nagai, N.; Suzuki, M.; Kurihara, T.; Minami, S.; Hirano, E.; Shinoda, H.; Kobayashi, S.; Tsubota, K. Absolute and estimated values of macular pigment optical density in young and aged Asian participants with or without age-related macular degeneration. BMC Ophthalmol.
**2017**, 17, 161. [Google Scholar] [CrossRef] [PubMed] - Molly, R.; Wilson, K.A.; Sandberg, B.; Foutch, K. Macular pigment optical density and visual quality of life. J. Optom.
**2021**, 14, 92–99. [Google Scholar] - Kaiser, P.K. Prospective evaluation of visual acuity assessment: A comparison of snellen versus ETDRS charts in clinical practice (An AOS Thesis). Trans. Am. Ophthalmol. Soc.
**2009**, 107, 311–324. [Google Scholar] - Ferris, F.L.; Davis, M.D.; Clemons, T.E.; Lee, L.Y.; Chew, E.Y.; Lindblad, A.S.; Milton, R.C.; Bressler, S.B.; Klein, R.; Age-Related Eye Disease Study (AREDS) Research Group. A simplified severity scale for age-related macular degeneration: AREDS Report No. 18. Arch Ophthalmol.
**2005**, 123, 1570–1574. [Google Scholar] - Davis, M.D.; Gangnon, R.E.; Lee, L.-Y.; Hubbard, L.D.; Klein, B.E.; Klein, R.; Ferris, F.L.; Bressler, S.B.; Milton, R.C.; Age-Related Eye Disease Study Group. The Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration: AREDS Report No. 17. Arch Ophthalmol.
**2005**, 123, 1484–1498. [Google Scholar] - Zagórski, Z. Podsumowanie Założeń dla Zalecanych Algorytmów Postȩpowania w Praktyce klinicznej (Preferred Practice Pattern
^{®}—PPP); American Academy of Ophthalmology: San Francisco, CA, USA, 2016. (In Polish) [Google Scholar] - R Core Team. R: A language and environment for statistical computing. In R Foundation for Statistical Computing; R Core Team: Vienna, Austria, 2018; Available online: https://www.r-project.org/ (accessed on 1 May 2023).
- Signorell, A.; Aho, K.; Alfons, A.; Anderegg, N.; Aragon, T.; Arachchige, C.; Arppe, A.; Baddeley, A.; Barton, K.; Bolker, B.; et al. DescTools: Tools for Descriptive Statistics. R Package Version 0.99.41. 2021. Available online: https://cran.r-project.org/web/packages/DescTools/index.html (accessed on 1 May 2023).

**Table 1.**Characteristics of patients (the median, minimum and maximum values of variables for each of the groups).

Variable | Control Group | AREDS 2 | AREDS 3 | AREDS 4 |
---|---|---|---|---|

CRT ($\mathsf{\mu}$m) | 253.5 (214–306) | 240 (194–286) | 233 (131–307) | 314.5 (93–660) |

GCC ($\mathsf{\mu}$m) | 82.5 (74–88) | 78 (63–92) | 75 (62–88) | 61.5 (22–89) |

ETDRS | 70 (50–85) | 80 (35–85) | 65 (35–85) | 40 (5–76) |

Snellen | 0.50 (0.15–1) | 0.8 (0.1–1) | 0.4 (0.1–1) | 0.125 (0.01–0.60) |

MPOD (d.u.) | 0.36 (0.10–0.62) | 0.36 (0.0–0.7) | 0.555 (0.0–0.9) | 0.00 (0.00–0.72) |

Age (years) | 67.5 (61–85) | 70 (62–92) | 75 (61–84) | 77 (59–87) |

Variable | Coefficient | p-Value | OR | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|

CRT | 0.006839 | 0.024910 | 1.006862 | 1.001133 | 1.013316 |

ETDRS | −0.066421 | <0.000001 | 0.935736 | 0.912743 | 0.957144 |

Threshold coefficients | |||||

Control group/AREDS 2 | AREDS 2/AREDS 3 | AREDS 3/AREDS 4 | |||

−4.171311 | −2.748292 | −1.046439 |

Variable | Coefficient | p-Value | OR | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|

CRT | 0.007693 | 0.006367 | 1.007722 | 1.002561 | 1.013819 |

Snellen | −2.676137 | 0.000007 | 0.068829 | 0.020959 | 0.216983 |

Threshold coefficients | |||||

Control group/AREDS 2 | AREDS 2/AREDS 3 | AREDS 3/AREDS 4 | |||

−0.884703 | 0.50022 | 2.035632 |

Variable | Coefficient | p-Value | OR | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|

CRT | 0.011008 | 0.000301 | 1.011069 | 1.005563 | 1.017702 |

Age | 0.080225 | 0.000337 | 1.083531 | 1.038016 | 1.133463 |

Threshold coefficients | |||||

Control group/AREDS 2 | AREDS 2/AREDS 3 | AREDS 3/AREDS 4 | |||

7.34911 | 8.713175 | 10.0887 |

Variable | Coefficient | p-Value | OR | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|

GCC | −0.091017 | 0.012630 | 0.913002 | 0.846850 | 0.978064 |

ETDRS | −0.099617 | 0.000005 | 0.905184 | 0.863657 | 0.942014 |

Threshold coefficients | |||||

Control group/AREDS 2 | AREDS 2/AREDS 3 | AREDS 3/AREDS 4 | |||

−16.201571 | −13.466137 | −10.26158 |

Variable | Coefficient | p-Value | OR | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|

GCC | −0.081473 | 0.013013 | 0.921758 | 0.860098 | 0.976664 |

Snellen | −4.343067 | 0.000060 | 0.012997 | 0.001337 | 0.096795 |

Threshold coefficients | |||||

Control group/AREDS 2 | AREDS 2/AREDS 3 | AREDS 3/AREDS 4 | |||

−11.06145 | −8.338816 | −5.754173 |

Variable | Coefficient | p-Value | OR | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|

GCC | −0.065804 | 0.027764 | 0.936315 | 0.877389 | 0.987024 |

Age | 0.118874 | 0.006711 | 1.126228 | 1.035529 | 1.231370 |

Threshold coefficients | |||||

Control group/AREDS 2 | AREDS 2/AREDS 3 | AREDS 3/AREDS 4 | |||

1.60897 | 3.884524 | 6.114169 |

Variable | Coefficient | p-Value | OR | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|

ETDRS | −0.069013 | <0.000001 | 0.933315 | 0.910198 | 0.954920 |

Age | 0.051857 | 0.021138 | 1.053225 | 1.008440 | 1.101663 |

Threshold coefficients | |||||

Control group/AREDS 2 | AREDS 2/AREDS 3 | AREDS 3/AREDS 4 | |||

−2.335994 | −0.832369 | 0.804257 |

Variable | Coefficient | p-Value | OR | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|

Snellen | −2.746244 | 0.000003 | 0.064168 | 0.019643 | 0.200547 |

Age | 0.055591 | 0.013359 | 1.057166 | 1.012328 | 1.105757 |

Threshold coefficients | |||||

Control group/AREDS 2 | AREDS 2/AREDS 3 | AREDS 3/AREDS 4 | |||

1.150177 | 2.610493 | 4.065317 |

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## Share and Cite

**MDPI and ACS Style**

Wąż, P.; Zorena, K.; Murawska, A.; Bielińska-Wąż, D.
Classification Maps: A New Mathematical Tool Supporting the Diagnosis of Age-Related Macular Degeneration. *J. Pers. Med.* **2023**, *13*, 1074.
https://doi.org/10.3390/jpm13071074

**AMA Style**

Wąż P, Zorena K, Murawska A, Bielińska-Wąż D.
Classification Maps: A New Mathematical Tool Supporting the Diagnosis of Age-Related Macular Degeneration. *Journal of Personalized Medicine*. 2023; 13(7):1074.
https://doi.org/10.3390/jpm13071074

**Chicago/Turabian Style**

Wąż, Piotr, Katarzyna Zorena, Anna Murawska, and Dorota Bielińska-Wąż.
2023. "Classification Maps: A New Mathematical Tool Supporting the Diagnosis of Age-Related Macular Degeneration" *Journal of Personalized Medicine* 13, no. 7: 1074.
https://doi.org/10.3390/jpm13071074