Macular Telangiectasia Type 2

Emily Y. Chew, MD; Tunde Peto, MD, PhD; Traci E. Clemons, PhD; Ferenc B. Sallo, MD, PhD; Daniel Pauleikhoff, MD, PhD; Irene Leung, BA; Glenn J. Jaffe, MD; Tjebo F.C. Heeren, MD; Catherine A. Egan, MD; Peter Charbel Issa, MD, PhD; Konstantinos Balaskas, MD; Frank G. Holz, MD; Alain Gaudric, MD; Alan C. Bird, MD; Martin Friedlander, MD, PhD

Ophthalmology Science (Jun 2023)

Macular Telangiectasia Type 2

Emily Y. Chew, MD,
Tunde Peto, MD, PhD,
Traci E. Clemons, PhD,
Ferenc B. Sallo, MD, PhD,
Daniel Pauleikhoff, MD, PhD,
Irene Leung, BA,
Glenn J. Jaffe, MD,
Tjebo F.C. Heeren, MD,
Catherine A. Egan, MD,
Peter Charbel Issa, MD, PhD,
Konstantinos Balaskas, MD,
Frank G. Holz, MD,
Alain Gaudric, MD,
Alan C. Bird, MD,
Martin Friedlander, MD, PhD

Affiliations

Emily Y. Chew, MD: Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland; Correspondence: Emily Y. Chew, MD, Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Building 10-CRC, Room 3-2531, 10 Center Drive, Bethesda, MD 20892-1204.
Tunde Peto, MD, PhD: Centre for Public Health, Queen’s University, Belfast, United Kingdom
Traci E. Clemons, PhD: The Emmes Corporation, Rockville, Maryland
Ferenc B. Sallo, MD, PhD: Jules Gonin Eye Hospital, University of Lausanne, Lausanne, Switzerland
Daniel Pauleikhoff, MD, PhD: Department of Ophthalmology, St. Franziskus Hospital Muenster, Muenster, Germany
Irene Leung, BA: NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, Institute of Ophthalmology, London, United Kingdom
Glenn J. Jaffe, MD: Department of Ophthalmology, Duke University, Durham, North Carolina
Tjebo F.C. Heeren, MD: NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, Institute of Ophthalmology, London, United Kingdom
Catherine A. Egan, MD: NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, Institute of Ophthalmology, London, United Kingdom
Peter Charbel Issa, MD, PhD: Oxford Eye Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Laboratory of Ophthalmology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
Konstantinos Balaskas, MD: NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, Institute of Ophthalmology, London, United Kingdom
Frank G. Holz, MD: Department of Ophthalmology, University of Bonn, Bonn, Germany
Alain Gaudric, MD: Department of Ophthalmology, Université Paris Cité. APHP, Hôpital Lariboisière, Paris, France
Alan C. Bird, MD: NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, Institute of Ophthalmology, London, United Kingdom
Martin Friedlander, MD, PhD: The Lowy Medical Research Institute and the Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California

Journal volume & issue: Vol. 3, no. 2
p. 100261

Abstract

Read online

Purpose: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging. Design: An algorithm was used on data from a prospective natural history study of MacTel for classification development. Subjects: A total of 1733 participants enrolled in an international natural history study of MacTel. Methods: The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity. Main Outcome Measures: The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes. Results: The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed. Conclusions: This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.

Published in Ophthalmology Science

ISSN: 2666-9145 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Ophthalmology
Website: https://www.journals.elsevier.com/ophthalmology-science/

About the journal

Abstract

Keywords