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Medicine

Comparing Objective Conjunctival Hyperemia Grading and the Ocular Surface Disease Index Score in Dry Eye Syndrome During COVID-19

Published: May 25, 2022 doi: 10.3791/63812

Summary

The present protocol describes the cross-sectional research performed on 40 healthy subjects between the ages of 20 and 45 to assess the prevalence of dry eye syndrome (DES) during COVID-19. The OSDI survey evaluated DES, and the advanced ophthalmic systems (AOS) software was used to assess limbal redness.

Abstract

The incidence of dry eye syndrome (DES) has increased due to wearing masks, utilizing digital devices, and working remotely during the pandemic. A survey was conducted during the COVID-19 pandemic to determine the prevalence of dry eye syndrome. A cross-sectional study investigated how prevalent DES is during COVID-19 in healthy patients aged 20-45 in the United States. An Ocular Surface Disease Index (OSDI) questionnaire was given to 40 individuals remotely from October 31, 2021, to December 1, 2021. The AOS and the OSDI survey were used to evaluate DES. The subjects were 29 years old on average (SD 14.14), with 23 males (57.5%) and 17 females (42.5%). According to the OSDI survey, low DES, moderate DES, and severe DES had prevalence rates of 15%, 77.5%, and 7.5%, respectively. White (W) people represent 50% of the population, while African Americans (AA) represent 35%, Asians represent 7.5%, and Hispanics represent 7.5%. Mild DES affected 77.5% of subjects, with 64.50% males and 35.50% females. According to the AOS objective grading system, mild (M) DES, moderate (MO) DES, and severe (S) DES had prevalence rates of 40%, 12.5%, and 15%, respectively. Linear regression was used to compare the two grading systems, and it demonstrated a strong relationship between the two grading systems.

Introduction

COVID-19, caused by a SARS-COV-2 virus infection, was discovered in Wuhan, China, in December 2019. Meduri et al.1 reported a high prevalence rate of mild ocular symptoms in COVID-19 patients. In Italy, eye surgical procedures were reduced due to the pandemic2. Since the outbreak, many have been working from home and wearing masks as a precaution. Each of these elements and the use of digital devices and online learning3 contributed to dry eye syndrome (DES) and eye strain3,4, respectively. Furthermore, there is evidence that wearing masks can cause DES. Wearing the mask may cause tear evaporation and conjunctival discomfort5. Giannaccare et al. reported that 10.3% of the individuals had rising ocular discomfort symptoms during the pandemic, and the mean score of the OSDI was 21, with a mean age of 28.5 years old6.

A cross-sectional study in Japan reported that the percentage of Japanese women who had a combined result of definite or probable dry eye disease was 76.5%, greater than the percentage of men office employees who utilized Visual Display Terminal7. According to Inomata et al., prolonged screen exposure of more than 8 h/day has been linked to the symptomatic dry eye compared to less than 4 h/day8. The OSDI has proven to be a valid and reliable questionnaire for assessing the severity of DES9,10. The AOS software has been used to determine conjunctival hyperemia, and it has been proven to be a very valid software11.

The present study investigated how common DES is in healthy people aged 20-45. An Ocular Surface Disease Index (OSDI) questionnaire was given to 40 people remotely from October 31, 2021, to December 1, 2021, for conducting the test. The AOS and OSDI surveys were used to assess DES. Finally, the two grading methods were compared: the OSDI score and the AOS software. The participants had to first fill up an eligibility questionnaire, which included the following inclusion criteria: (1) Healthy individuals; (2) Age range of 20-45 years; (3) The participants had to be located in the United States.

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Protocol

The present study was conducted following the declaration of Helsinki, and the protocol was approved by the Institutional Review Board at Solutions (IRB, 2021/09/14). The study followed the reporting guidelines of the Declaration of Helsinki. All the participants provided informed consent for the questionnaire. The survey was conducted entirely online via the Internet. If the participants met the eligibility requirements, the consent forms, research project flier, and OSDI questionnaire were emailed to them. After submitting the consent forms and completing the OSDI questionnaire, a $10 gift credit card was issued online for completing the survey.

1. OSDI survey for DES evaluation

  1. Use the following criteria: low OSDI score (0-20 points); moderate OSDI score (21-45 points); high OSDI score (46-100points).
    NOTE: The diagnostic criteria for DES by OSDI score is ≥13.
  2. Ask the eligible subjects to fill out the survey online (Table 1).

2. Determination of limbal redness via AOS software

  1. Take a picture of the eyes, one eye at a time.
    NOTE: The picture has to be clear for the analysis.
  2. Assign the subject an ID number. Save the subject ID.
  3. To add a subject, click on the Add tab.
    NOTE: The system automatically generates the patient ID.
  4. Fill in the Title tab. Fill in the First name and the surname.
  5. Fill in the date of birth.
  6. Fill in the email address, and then confirm the email address.
  7. Fill in the National Health Service ID (NHS ID) and the Medical Record Number (MRN) number.
  8. Fill in the Note tab. Then, click on Save.
  9. Now, select the Subject ID. Then, add/view images. Then, click on + Add.
  10. Upload the images to the AOS software (see Table of Materials) to evaluate the limbal redness. Upload the right eye first, then the left eye.
  11. Click on the New Exam tab. Then, click on the Image + tab.
  12. Add the right eye, and then click on the Selected Media tab.
  13. Click on the Bulbar icon. Then, click on the Area tab. Click on Analysis Bulbar Redness Grading Scale 0-4.
  14. Click on the image to start at the limbal area close to the pupil border and follow a pattern to cover the limbal area (Figure 1). Then, click on Save Analysis.
    NOTE: The redness grade and the % of vessels are saved.
  15. Click on the Redness Map and Save Analysis.
  16. Click on Generate Report. Click on both images to generate PDF.
    NOTE: The PDF report includes the patient's first name, surname, date of birth, Patient ID, NHS number, and by whom the patient was examined. The PDF report also includes the examination date and examination type.
  17. Repeat Steps 2.9-2.22; then, add the left-eye image.
  18. Grade the limbal redness using the AOS software. Analyze the images for bulbar conjunctival redness with a validated objective grading software using the "bulbar redness" function in the limbal areas of the conjunctiva, using an automatic continuous grading from 0-4 in 0.1 unit.
    NOTE: Grade 0 is minimum redness; Grade 4 is severe redness (Figure 1). Grades 0-1 are coded grade I, Grades 1-2 are coded grade II, Grades 2-3 are coded grade III, and Grades 3-4 are coded grade IV to match the OSDI survey grading system.

3. Statistical analysis

  1. Collect the data and analyze the data using Microsoft Excel.
    1. Summarize the Subject ID, age, gender, race, OSDI score, and the limbal redness in one table.
    2. Calculate the age, OSDI score, and the limbal redness score mean using Excel software.
  2. Determine the mean ± standard deviation of the age, OSDI score, and limbal redness score.
  3. Calculate the prevalence rate in percentage.
    1. Divide the number of subjects by the total number of subjects (40), and then multiply by 100.
      1. Divide the number of subjects with low DES (LDES) by the total number of subjects (40), and then multiply by 100.
      2. Divide the number of subjects with moderate DES (MODDES) by the total number of subjects (40), and then multiply by 100.
      3. Divide the number of subjects with severe DES (SDES) by the total number of subjects (40), and then multiply by 100.
    2. Perform the linear regression analysis using Excel software to determine the mean of the two values (x-axis) and the difference between the two means (y-axis). Also, determine the p-value.
      NOTE: The linear regression analysis determines the relationships between the OSDI score and the AOS redness score.

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Representative Results

The subjects were 29 years old on average (mean ± SD, 29 ± 14.14), with 23 males (57.5%) and 17 females (42.5%) (Table 2). White people account for 50% of the population, while African Americans account for 35%, Asians account for 7.5%, and Hispanics account for 7.5% (Figure 2). The average survey score of the OSDI was 6.17 ± 6.24, 37.94 ± 5.07, 46 ± 0 for low, moderate, and high (Figure 3). According to the OSDI Survey, low DES, moderate DES, and severe DES had prevalence rates of 15%, 77.5%, and 7.5%, respectively (Figure 4). Mild DES affects 77.5% of subjects, with males accounting for 64.50% and females accounting for 35.50%. The average redness score of the AOS was 0.47 ± 0.23, 1.50 ± 0.28, 2.60 ± 0.40, 3.65 ± 0.28, for Grade 0, Grade 1, Grade 2, and Grade 3 (Figure 5). According to the AOS objective grading system, mild DES, moderate DES, and severe DES had prevalence rates of 27.5%, 12.5%, and 10%, respectively (Figure 6). Linear regression was used to compare the two grading systems, and it demonstrated a strong relationship between them, with P < 0.001 statistically significant (Figure 7).

Figure 1
Figure 1: An example of the bulbar redness scale examination type. (A) Illustrates the redness scale. (B) Illustrates the vessels percentage. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Pie chart showing the ethnicity of 40 healthy subjects. Please click here to view a larger version of this figure.

Figure 3
Figure 3: The mean of the OSDI score of the low, moderate, and high DES. Please click here to view a larger version of this figure.

Figure 4
Figure 4: Pie chart showing the percentage of participants' OSDI scores. The score reflects low dry eye syndrome (LDES), moderate dry eye syndrome (MODDES) score, and a severe dry eye syndrome (SDES) score. Please click here to view a larger version of this figure.

Figure 5
Figure 5: The mean of the AOS redness grading scale. Please click here to view a larger version of this figure.

Figure 6
Figure 6: The AOS software's redness score. The score shows the percentage of participants with a healthy score (H), a mild dry eye syndrome (MDES) score, a moderate dry eye syndrome (MODDES) score, and a severe dry eye syndrome (SDES) score. Please click here to view a larger version of this figure.

Figure 7
Figure 7: Scatter plot and linear regression comparing the OSDI score to the AOS software redness grading score. P < 0.001. Please click here to view a larger version of this figure.

Table 1: Ocular Surface Disease Index (OSDI) questionnaire used in the study. Low OSDI score (0-20 points); Moderate OSDI score (21-45 points); High OSDI score ( 46-100 points). Please click here to download this Table.

Table 2: Participants' demographics and the grading score. One subject was 18 years old. White (W), African Americans (AA), Asians (A), Hispanics(H). Please click here to download this Table.

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Discussion

Several previous studies have reported DES using the Schirmer test, tear break-up time (TBUT), and the OSDI score12. The present study used the AOS software to determine the DES using the limbal redness. One of the important critical protocol steps is to have a clear image of the eyes; if the image is blurry, determining the limbal redness is very challenging, and most likely, accurate readings are not obtained. When all the images are collected, one of the troubleshooting techniques is to check the images for clarity. One of the software limitations is that if the image is blurry, the readings would not be accurate, and this subject could not be used in this case.

The current study determines the relationship between the OSDI score and the AOS limbal redness. The significance of using the AOS limbal redness is that it adds more information to the OSDI score, such as the scale of the limbal redness in relation to the OSDI score.

In our experience, using both scores, the OSDI survey, and the AOS software, is critical for a successful prevalence rate of DES. The AOS limbal redness survey determines the AOS limbal redness, and the OSDI survey determines the DES grade, which can be low, moderate, or severe. In the present study, a 0-1 redness scale for grade 0, a 1-2 redness scale for grade 1, a 2-3 redness scale for grade 2, and a 3-4 redness scale for grade 3 were used. Grade 0 indicates healthy eyes with no redness, while grade 3 indicates severe redness with severe DES. The use of digital screens has been shown to decrease TBUT, ocular surface staining, and signs of meibomian gland dysfunction, all of which contribute to DES13.

In summary, the prevalence rate of 40 healthy subjects between the ages of 20-45 is high. To assess the prevalence rate of DES during COVID-19, the OSDI survey was used to evaluate DES, and AOS software was used to evaluate limbal redness. In addition, the relationship between the OSDI and AOS is linear. Finally, the AOS software could be used to determine DES and add more information to the OSDI score and TBUT. However, to determine the prevalence rate of DES in a large cohort, future studies need to be performed, including the OSDI score, TBUT score, and the AOS software. In addition, it is still necessary to further verify the reliability and applicability of the AOS software concerning the OSDI score and TBUT score before using both models in day-to-day clinical practice.

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Disclosures

The author has nothing to disclose.

Acknowledgments

We want to thank all the participants for their help and support in filling out the survey and sending the images of their eyes. The ERC Center grant provided funding for the IRB.

Materials

Name Company Catalog Number Comments
AOS SOFTWARE Advanced Ophthalmic Systems SPARCA software to access limbal redness
Microsoft excel Microsoft for data collection and analysis

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References

  1. Meduri, A., et al. Ocular surface manifestation of COVID-19 and tear film analysis. Scientific Reports. 10 (1), 20178 (2020).
  2. dell'Omo, R., et al. Effect of COVID-19-related lockdown on ophthalmic practice in Italy: A report from 39 institutional centers. European Journal of Ophthalmology. 32 (1), 695-703 (2022).
  3. Ganne, P., Najeeb, S., Chaitanya, G., Sharma, A., Krishnappa, N. C. Digital eye strain epidemic amid COVID-19 pandemic - A cross-sectional survey. Ophthalmic Epidemiology. 28 (4), 285-292 (2021).
  4. Al-Namaeh, M. Coronavirus disease pandemic and dry eye disease: A methodology concern on the causal relationship. Medical Hypothesis Discovery and Innovation in Ophthalmology. 11 (1), 42-43 (2022).
  5. Hayirci, E., Yagci, A., Palamar, M., Basoglu, O. K., Veral, A. The effect of continuous positive airway pressure treatment for obstructive sleep apnea syndrome on the ocular surface. Cornea. 31 (6), 604-608 (2012).
  6. Giannaccare, G., Vaccaro, S., Mancini, A., Scorcia, V. Dry eye in the COVID-19 era: how the measures for controlling pandemic might harm ocular surface. Graefe's Archive for Clinical and Experimental Ophthalmology. 258 (11), 2567-2568 (2020).
  7. Uchino, M., et al. Prevalence of dry eye disease and its risk factors in visual display terminal users: the Osaka study. American Journal of Ophthalmology. 156 (4), 759-766 (2013).
  8. Inomata, T., et al. Characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using a smartphone application. JAMA Ophthalmology. 138 (1), 58-68 (2020).
  9. Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D., Reis, B. L. Reliability and validity of the Ocular Surface Disease Index. Archives of Ophthalmology. 118 (5), 615-621 (2000).
  10. Finis, D., et al. Comparison of the OSDI and SPEED questionnaires for the evaluation of dry eye disease in clinical routine. Der Ophthalmologe. 111 (11), 1050-1056 (2014).
  11. Huntjens, B., Basi, M., Nagra, M. Evaluating a new objective grading software for conjunctival hyperaemia. Contact Lens & Anterior Eye. 43 (2), 137-143 (2020).
  12. Hwang, H. B., et al. Easy and effective test to evaluate tear-film stability for self-diagnosis of dry eye syndrome: blinking tolerance time (BTT). BMC Ophthalmology. 20 (1), 438 (2020).
  13. Wolffsohn, J. S., et al. Demographic and lifestyle risk factors of dry eye disease subtypes: A cross-sectional study. The Ocular Surface. 21, 58-63 (2021).

Tags

Objective Conjunctival Hyperemia Grading Ocular Surface Disease Index Score Dry Eye Syndrome COVID-19 AOS Software Redness Grading System Prevalence Of Dry Eye Syndrome OSDI Questionnaire Informed Consent Low Moderate And High Categories DES Evaluation Advanced Ophthalmic Systems Subject ID Number Pictures Of The Subject's Eyes Add Tab Title Tab First Name Surname Date Of Birth Email Address National Health Service ID Medical Record Number Note Tab Save Limbal Redness Evaluation
Comparing Objective Conjunctival Hyperemia Grading and the Ocular Surface Disease Index Score in Dry Eye Syndrome During COVID-19
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Cite this Article

Al-Namaeh, M. Comparing ObjectiveMore

Al-Namaeh, M. Comparing Objective Conjunctival Hyperemia Grading and the Ocular Surface Disease Index Score in Dry Eye Syndrome During COVID-19. J. Vis. Exp. (183), e63812, doi:10.3791/63812 (2022).

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