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Use of Machine Learning on Contact Lens Sensor–Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma

2018; Elsevier BV; Volume: 194; Linguagem: Inglês

10.1016/j.ajo.2018.07.005

1879-1891

Keith R. Martin, Kaweh Mansouri, Robert N. Weinreb, Robert Wasilewicz, Christophe Gisler, Jean Hennebert, Dominique Genoud, Tarek Shaarawy, Carl Erb, Norbert Pfeiffer, Graham E. Trope, Felipe A. Medeiros, Yaniv Barkana, John H.K. Liu, Robert Ritch, André Mermoud, Delan Jinapriya, Catherine M. Birt, Iqbal Ike K. Ahmed, Christoph Kranemann, Peter Höh, Bernhard Lachenmayr, Yuri S. Astakhov, Enping Chen, Susana Duch, Giorgio Marchini, Stefano Gandolfi, Marek Rękas, А. V. Kuroyedov, A Cernák, Vicente Polo, José I. Belda, Swaantje Grisanti, Christophe Baudouin, Jean‐Philippe Nordmann, Carlos Gustavo De Moraes, Zvi Segal, Moshe Lusky, Haia Morori-Katz, Noa Geffen, Shimon Kurtz, Ji Liu, Donald L. Budenz, O’Rese J. Knight, Jean-Claude Mwanza, Anthony J. Viera, Fernando Castanera, Jemaima Che-Hamzah,

Corneal surgery and disorders

Purpose To test the hypothesis that contact lens sensor (CLS)-based 24-hour profiles of ocular volume changes contain information complementary to intraocular pressure (IOP) to discriminate between primary open-angle glaucoma (POAG) and healthy (H) eyes. Design Development and evaluation of a diagnostic test with machine learning. Methods Subjects: From 435 subjects (193 healthy and 242 POAG), 136 POAG and 136 age-matched healthy subjects were selected. Subjects with contraindications for CLS wear were excluded. Procedure: This is a pooled analysis of data from 24 prospective clinical studies and a registry. All subjects underwent 24-hour CLS recording on 1 eye. Statistical and physiological CLS parameters were derived from the signal recorded. CLS parameters frequently associated with the presence of POAG were identified using a random forest modeling approach. Main Outcome Measures: Area under the receiver operating characteristic curve (ROC AUC) for feature sets including CLS parameters and Start IOP, as well as a feature set with CLS parameters and Start IOP combined. Results The CLS parameters feature set discriminated POAG from H eyes with mean ROC AUCs of 0.611, confidence interval (CI) 0.493–0.722. Larger values of a given CLS parameter were in general associated with a diagnosis of POAG. The Start IOP feature set discriminated between POAG and H eyes with a mean ROC AUC of 0.681, CI 0.603–0.765. The combined feature set was the best indicator of POAG with an ROC AUC of 0.759, CI 0.654–0.855. This ROC AUC was statistically higher than for CLS parameters or Start IOP feature sets alone (both P < .0001). Conclusions CLS recordings contain information complementary to IOP that enable discrimination between H and POAG. The feature set combining CLS parameters and Start IOP provide a better indication of the presence of POAG than each of the feature sets separately. As such, the CLS may be a new biomarker for POAG.