Should We Perform Early Cataract Surgery? A Patient Reported Outcome Study

Visual acuity changes in the preoperative period in patients undergoing cataract surgery

OBJECTIVE

This study quantifies change in best visual acuity (BVA) over the preoperative period and assesses factors associated with postoperative outcomes.

DESIGN

Retrospective chart review conducted at a single institution.

PARTICIPANTS

A total of 691 patients underwent cataract surgery and had a preoperative assessment 0-30 days prior to surgery following their surgical evaluation.

METHODS

Baseline demographics and past medical and clinical data were collected through electronic medical record query. BVA was noted at initial surgical evaluation, preoperative assessment, and nearest postoperative assessment.

RESULTS

A total of 691 patients (911 eyes) were included with mean BVAs at the initial evaluation, preoperative assessment, and postoperative assessment of 68.3 ± 16.8, 64.6 ± 18.5, and 81.1 ± 12.0 ETDRS letters, respectively. Mean BVA was significantly higher postoperatively compared with the preoperative assessment and initial evaluation (p < 0.0001). There was a mean of 53.8 days between initial surgical evaluation and surgery date and a mean of 49.9 days between the preoperative assessment and initial surgical evaluation. The mean interval between the preoperative assessment and surgery was 11.7 days. In the preoperative period, 9.1% of eyes experienced worsening of BVA by >3 eyes and 0.9% experienced improvement of BVA by >3 lines. Time to surgery was significantly associated with change in postoperative BVA (effect size, -0.03 ETDRS letters; p = 0.015) but was not significant on multiple linear regression. BVA at initial evaluation, glaucoma, and glaucoma surgery were all significantly associated with postoperative outcomes.

CONCLUSION

Most eyes experienced stable vision in the preoperative period for cataract surgery. On average, patients with high BVAs at the time of initial surgical evaluation may be able to defer surgery without the risk of poorer surgical outcomes.

Accuracy of an artificial intelligence-based mobile application for detecting cataracts: Results from a field study

Purpose

To assess the accuracy of e-Paarvai, an artificial intelligence-based smartphone application (app) that detects and grades cataracts using images taken with a smartphone by comparing with slit lamp-based diagnoses by trained ophthalmologists.

Methods

In this prospective diagnostic study conducted between January and April 2022 at a large tertiary-care eye hospital in South India, two screeners were trained to use the app. Patients aged >40 years and with a best-corrected visual acuity <20/40 were recruited for the study. The app is intended to determine whether the eye has immature cataract, mature cataract, posterior chamber intra-ocular lens, or no cataract. The diagnosis of the app was compared with that of trained ophthalmologists based on slit-lamp examinations, the gold standard, and a receiver operating characteristic (ROC) curve was estimated. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed.

Results

The two screeners used the app to screen 2,619 eyes of 1,407 patients. In detecting cataracts, the app showed high sensitivity (96%) but low specificity (25%), an overall accuracy of 88%, a PPV of 92.3%, and an NPV of 57.8%. In terms of cataract grading, the accuracy of the app was high in detecting immature cataracts (1,875 eyes, 94.2%), but its accuracy was poor in detecting mature cataracts (73 eyes, 22%), posterior chamber intra-ocular lenses (55 eyes, 29.3%), and clear lenses (2 eyes, 2%). We found that the area under the curve in predicting ophthalmologists' cataract diagnosis could potentially be improved beyond the app's diagnosis based on using images only by incorporating information about patient sex and age (P < 0.0001) and best-corrected visual acuity (P < 0.0001).

Conclusions

Although there is room for improvement, e-Paarvai app is a promising approach for diagnosing cataracts in difficult-to-reach populations. Integrating this with existing outreach programs can enhance the case detection rate.