Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach El ojo seco está relacionado con un aumento intrasujeto de la variabilidad de osmolaridad lagrimal confirmado por tecnología de aprendiza
Author
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Cartes, C.
Author
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López, D.
Author
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Salinas, D.
Author
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Segovia, C.
Author
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Ahumada, C.
Author
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Pérez, N.
Author
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Valenzuela F., Manuel A.
Author
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Lanza, N.
Author
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López Solís, R. O.
Author
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Perez, V. L.
Author
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Zegers, P.
Author
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Fuentes, A.
Author
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Alarcón, C.
Author
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Traipe, L.
Admission date
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2019-10-30T15:40:07Z
Available date
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2019-10-30T15:40:07Z
Publication date
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2019
Cita de ítem
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Archivos de la Sociedad Espanola de Oftalmologia, Volumen 94, Issue 7, 2019, Pages 337-342
Identifier
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03656691
Identifier
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10.1016/j.oftal.2019.03.007
Identifier
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https://repositorio.uchile.cl/handle/2250/172539
Abstract
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Objective: Because of high variability, tear film osmolarity measures have been questioned in dry eye assessment. Understanding the origin of such variability would aid data interpretation. This study aims to evaluate osmolarity variability in a clinical setting. Material and methods: Twenty dry eyes and 20 control patients were evaluated. Three consecutive osmolarity measurements per eye at 5 min intervals were obtained. Variability was represented by the difference between both extreme readings per eye. Machine learning techniques were used to quantify discrimination capacity of tear osmolarity for dry eye. Results: Mean osmolarities in the control and dry eye groups were 295.1 ± 7.3 mOsm/L and 300.6 ± 11.2 mOsm/L, respectively (P = .004). Osmolarity variabilities were 7.5 ± 3.6 mOsm/L and 16.7 ± 11.9 mOsm/L, for the control and dry eye groups, respectively (P < .001). Based on osmolarity, a logistic classifier showed an 85% classification accuracy. Conclusions: In the clinical setting, both mean osmolarity and osmolarity variability in the dry eye group were significantly higher than in the control group. Machine learning techniques showed good classification accuracy. It is concluded that higher variability of tear osmolarity is a dry eye feature.
Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach El ojo seco está relacionado con un aumento intrasujeto de la variabilidad de osmolaridad lagrimal confirmado por tecnología de aprendiza