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Inicio Archivos de la Sociedad Española de Oftalmología (English Edition) Dry eye is matched by increased intrasubject variability in tear osmolarity as c...
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Vol. 94. Issue 7.
Pages 337-342 (July 2019)
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Vol. 94. Issue 7.
Pages 337-342 (July 2019)
Original article
DOI: 10.1016/j.oftale.2019.03.002
Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach
El ojo seco está relacionado a un aumento intrasujeto de la variabilidad de osmolaridad lagrimal confirmado por tecnología de aprendizaje de máquinas
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C. Cartesa, D. Lópeza, D. Salinasa, C. Segoviab, C. Ahumadab, N. Pérezb, F. Valenzuelac, N. Lanzad, R.O. López Solíse, V.L. Perezf, P. Zegersg, A. Fuentesg, C. Alarcónh, L. Traipea,
Corresponding author
ltraipe@gmail.com

Corresponding author.
a Centro de la Visión, Filial Clínica Las Condes, Santiago, Chile
b School of Medical Technology, Faculty of Medicine, University of Chile, Independencia, Santiago, Chile
c Fundación Oftalmológica Los Andes, Vitacura, Santiago, Chile
d Bascom Palmer Eye Institute, University of Miami, Miami, FL, United States
e Institute for Biomedical Sciences (Cellular and Molecular Biology), Faculty of Medicine, University of Chile, Independencia, Santiago, Chile
f Duke Eye Center for Ocular Immunology, Duke University School of Medicine, Durham, NC, United States
g College of Engineering and Applied Sciences, Universidad de los Andes, Santiago, Chile
h Private Practice, Santiago, Chile
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Received 08 November 2018. Accepted 05 March 2019
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Tables (3)
Table 1. Demographics of the study population.
Table 2. Comparison of clinical characteristics and ocular surface parameters in control vs. DED subjects.
Table 3. Comparison of osmolarity measurements in control vs. DED subjects.
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Abstract
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 5min 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±7.3mOsm/L and 300.6±11.2mOsm/L, respectively (p=0.004). Osmolarity variabilities were 7.5±3.6mOsm/L and 16.7±11.9mOsm/L, for the control and dry eye groups, respectively (p<0.001). 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.

Keywords:
Dry eye
Osmolarity
Variability
Machine learning
Resumen
Objetivo

La medición de la osmolaridad lagrimal en pacientes con ojo seco ha sido cuestionada debido a su alta variabilidad. El entendimiento del origen de dicha variabilidad ayudaría a la interpretación clínica de los valores obtenidos. Esta investigación evalúa la medición de la variabilidad lagrimal en la práctica clínica.

Material y métodos

Veinte pacientes con ojo seco y 20 controles fueron evaluados. Fueron realizadas 3 mediciones consecutivas de osmolaridad a intervalos de 5min. La variabilidad fue definida como la diferencia entre las mediciones más extremas obtenidas en cada ojo. Se utilizaron técnicas de aprendizaje de máquinas para evaluar la capacidad discriminadora de la osmolaridad lagrimal.

Resultados

La osmolaridad promedio en el grupo control y ojo seco fueron 295,1±7,3mOsm/L y 300,6±11,2mOsm/L, respectivamente (p=0,004). La variabilidad de la osmolaridad lagrimal fue 7,5±3,6mOsm/L en el grupo control y 16,7±11,9mOsm/L en los pacientes con ojo seco (p<0,001). Basado en la osmolaridad, un clasificador logístico obtuvo un 85% de precisión.

Conclusiones

La osmolaridad promedio y la variabilidad fueron significativamente más altas en pacientes con ojo seco. Por otra parte, las técnicas de aprendizaje de máquina demostraron buena precisión para clasificar a los pacientes. Por tanto, la alta variabilidad parece ser característica propia de la enfermedad de ojo seco.

Palabras clave:
Ojo seco
Osmolaridad
Variabilidad
Aprendizaje de máquinas

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