Purpose: To determine the prevalence of dry eye disease (DED) in patients scheduled for cataract surgery in a Norwegian eye clinic. Patients and Methods: 218 patients scheduled for cataract surgery were examined for DED in one randomly selected eye and questioned regarding symptoms and risk factors. Patients were diagnosed with DED if they fulfilled the DEWS II criteria with symptom score > 12/100 with Ocular Surface Disease Index (OSDI) questionnaire, and the presence of any of the three signs: tear osmolarity > 307 mOsm/L in either eye or a difference in osmolarity between the two eyes of > 8 mOsm/L, corneal fluorescein staining (CSF) ≥ grade 2 and non-invasive tear film breakup time (NIKBUT) of < 10 seconds. Additional tests were the Standard Patient Evaluation of Eye Dryness (SPEED) questionnaire, tear meniscus height (TMH), Schirmer 1, tear film thickness (TFT), corneal sensitivity and meibography (meiboscore). Dry eye test outcomes were correlated with risk factors for DED. Results: The prevalence of DED was 55.5% according to the DEWS II criteria. The abnormal osmolarity percentage was 66.5, while 29.8% had shortened NIKBUT and 19.7% exhibited CFS ≥ 2. 57% had Schirmer 1 ≤ 10 mm/5 min, and 81.1% had a meiboscore of ≥ 1. 71.2% of subjects scored positive for DED symptoms using the OSDI questionnaire and 69.3% using SPEED. Logistic regression analysis showed that higher age correlated with a lower OSDI symptom score, reduced corneal sensitivity and increased meibomian gland atrophy. Female sex was associated with higher odds of having DED, abnormal NIKBUT and abnormal CFS. Ocular tests for DED did not correlate with OSDI symptom scores when assessed with Spearman`s rank analysis. Conclusion: The prevalence of DED in an elderly Norwegian population scheduled for cataract surgery is high and associated with female sex. There was a lack of correlation between signs and symptoms of DED.

Purpose: The purpose of this study was to evaluate the diagnostic value of inter-eye osmolarity differences in relation to dry eye symptoms and other non-osmolar signs of dry eye disease. Patients and Methods: One hundred ninety one participants who attended a larger interventional study of dry eye disease prior to and after cataract surgery were analyzed for dry eye disease (DED). Dry eye diagnostics were performed for all subjects according to the DEWS II criteria: tear osmolarity was collected from both eyes with the TearLab system, non-invasive Tear film break up time (NIKBUT) was obtained on the test eye with Keratograph and ocular surface staining (OSS) was evaluated using the Oxford schema. The Ocular Surface Disease Index (OSDI) questionnaire was used to assess symptoms. Inter-eye osmolarity greater than 8, which is considered as a sign of DED according to the TearLab user manual, was evaluated and compared with other non-osmolar signs of DED. Results: The 191 subjects were divided into three groups according to osmolarity measurements. Sixty-five subjects had normal osmolarity (below 308 mOsmol/L in both eyes and less than 9 mOsmol/L difference between the eyes), 107 had high osmolarity (308 mOsmol/L or higher in one of the eyes) and 19 had an inter-eye difference > 8 mOsmol/L or higher, with neither eye having osmolarity higher than 307 mOsmol/L. Signs and symptoms in this last group were not correlated with the high osmolarity group or the normal group, though they appeared more similar to the normal group. Conclusion: The diagnostic value of inter-eye osmolarity difference in predicting symptoms or other non-osmolar signs of dry eyes appears weak. Our study suggests that the criterion of an inter-eye difference of 8 mOsmol/L is not a useful cut-off for diagnosing dry eyes based on osmolarity.

Purpose: To evaluate the agreement of refractive predictability of a swept-source optical coherence tomography (SS-OCT) biometer, which uses segmental AL calculation, with another SS-OCT biometer, and an optical low coherence reflectometry (OLCR) biometer. The secondary objective was to describe the refractive outcomes, visual acuities, and the agreement of different preoperative biometric parameters. Patients and Methods: The study was a retrospective one-arm study of refractive and visual outcomes after successful cataract surgery. Preoperative biometric data were collected with two different SS-OCT device (Argos, Alcon Laboratories and Anterion, Heidelberg Engineering) and an OLCR device (Lenstar 900, Haag-Streit). The Barrett Universal II formula was used to calculate IOL power for all three devices. Follow-up examination was 1– 2 months after surgery. The main outcome measure, refractive prediction error (RPE), was calculated as the achieved postoperative refraction minus the predicted refraction for each device. Absolute error (AE) was calculated by reducing the mean error to zero. Results: The study included 129 eyes of 129 patients. The mean RPE was 0.06, − 0.14 and 0.17 D for the Argos, Anterion and Lenstar, respectively (p < 0.01). The Argos also had the lowest absolute RPE, while the Lenstar had the lowest median AE, but this was not statistically significant (p > 0.2). The percentages of eyes with RPE within ± 0.5 was 76%, 71%, and 78% for the Argos, Anterion, and Lenstar, respectively. The percentages of eyes with AE within 0.5 D was 79%, 84%, and 82% for the Argos, Anterion and Lenstar, respectively. None of these percentages were statistically significantly different (p > 0.2). Conclusion: All three biometers showed good refractive predictability with no statistically significant differences in AE or percentages of eyes within ± 0.5 D of RPE or AE. The lowest arithmetic RPE was found with the Argos biometer.

Purpose: The primary objective was to investigate if subjects with dry eyes had increased variability of keratometry measurements prior to cataract surgery compared to subjects with non-dry eyes. Secondary objectives were to determine which separate signs affected keratometry. Patients and Methods: This study was part of a prospective interventional randomized controlled trial. After dry eye diagnostics were performed (signs only) subjects were divided into sign of dry eye (SDE) positive and negative groups. To investigate variability, we performed two keratometry measurements for each subject with three different optical biometers: Anterion (OCT optical biometer), Eyestar (combined OCT and reflection-based optical biometer), and Lenstar (reflection based-optical biometer). Results: One hundred and thirty-one subjects were available for analysis. The variability of astigmatism was significantly higher for subjects with hyperosmolarity compared to normal eyes for the Lenstar, as was the percentage of eyes with variability of astigmatism greater than 0.25 D. The percentage of eyes with variability of average K greater than 0.25 D was higher for subjects with non-invasive keratograph break-up time < 10 seconds (NIKBUT positive) compared to normal eyes for the Lenstar. Conclusion: Combined diagnostic criteria (signs only) showed no statistically significant differences for keratometry measurements between SDE positive and negative. Eyes with hyperosmolarity and NIKBUT positive showed statistically higher variability of keratometry measurements compared to normal eyes for Lenstar, but not for the Anterion or Eyestar biometers.

Purpose: To compare the effect of treatment with preservative-free dexamethasone, NSAIDs and trehalose/hyaluronic acid eye drops with the preservative benzalkonium chloride containing dexamethasone and NSAIDs after cataract surgery in dry versus non-dry eyes. Patients and Methods: In this prospective randomized intervention study, dry eye tests were performed before and 6 weeks after cataract surgery. Patients were considered as having dry eye, SDE (sign of dry eye), if at least one of the following dry eye tests were abnormal; corneal fluorescein staining (CFS), non-invasive keratograph breakup time (NIKBUT) or tear osmolarity. Patients with SDE were randomly assigned to one of two groups. Group 1 patients were treated with dexamethasone and bromfenac eye drops with the preservative benzalkonium chloride (BAC). Group 2 patients were treated with preservative-free dexamethasone and preservative-free diclofenac, as well as a preservative-free lubricant with trehalose and hyaluronic acid both before and after surgery. Patients with normal tear film status acted as the control group (group 3) and received same treatment as group 1. Results: A total of 215 patients were enrolled six weeks after surgery, the number of patients with SDE decreased significantly in groups 1 and 2 (p < 0.001). Subjective symptoms and objective measures including osmolarity, NIKBUT, CFS, and tear film thickness (TFT) improved after surgery, tear production remained unchanged, while corneal sensitivity and meibomian gland dysfunction (MGD) parameters worsened. In the control group with normal tear-film status, SDE increased significantly after the surgery (p < 0.001). There were no statistically significant differences in tear film parameters between the three groups after surgery. Conclusion: After cataract surgery, patients with mild to moderate dry eyes may experience improved tear film status and reduced symptoms. However, we found no additional beneficial effect on dry eye parameters with treatment with preservative-free dexamethasone, NSAIDs, and lubricants compared to preservative-containing eye drops.

Background: Dry eye disease is a common disorder of the ocular surface, leading patients to seek eye care. Clinical signs and symptoms are currently used to diagnose dry eye disease. Metabolomics, a method for analyzing biological systems, has been found helpful in identifying distinct metabolites in patients and in detecting metabolic profiles that may indicate dry eye disease at early stages. In this study, we explored the use of machine learning and metabolomics data to identify cataract patients who suffer from dry eye disease, a topic that, to our knowledge, has not been previously explored. As there is no one-size-fits-all machine learning model for metabolomics data, choosing the most suitable model can significantly affect the quality of predictions and subsequent metabolomics analyses. Methods: To address this challenge, we conducted a comparative analysis of eight machine learning models on two metabolomics data sets from cataract patients with and without dry eye disease. The models were evaluated and optimized using nested k-fold cross-validation. To assess the performance of these models, we selected a set of suitable evaluation metrics tailored to the data set’s challenges. Results: The logistic regression model overall performed the best, achieving the highest area under the curve score of $0.8378$, balanced accuracy of $0.735$, Matthew’s correlation coefficient of $0.5147$, an F1-score of $0.8513$, and a specificity of $0.5667$. Additionally, following the logistic regression, the XGBoost and Random Forest models also demonstrated good performance. Conclusions: The results show that the logistic regression model with L2 regularization can outperform more complex models on an imbalanced data set with a small sample size and a high number of features, while also avoiding overfitting and delivering consistent performance across cross-validation folds. Additionally, the results demonstrate that it is possible to identify dry eye in cataract patients from tear film metabolomics data using machine learning models.