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. 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. METHODS: This is a pooled analysis of data from 24 prospective clinical studies and a registry. All subjects underwent 24-hour CLS recording on one 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 modelling approach. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (ROC AUC) for feature sets including CLS parameters, 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 a 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<0.0001). 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.
Use of machine learning on contact lens sensor-derived parameters for the diagnosis of primary open angle glaucoma
Marchini, G.;
2018-01-01
Abstract
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. 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. METHODS: This is a pooled analysis of data from 24 prospective clinical studies and a registry. All subjects underwent 24-hour CLS recording on one 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 modelling approach. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (ROC AUC) for feature sets including CLS parameters, 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 a 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<0.0001). 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.
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