Analysis of Refractive Errors in a Large Italian Cohort of Pediatric Subjects Post the COVID-19 Pandemic.

BACKGROUND: The prevalence of refractive errors has sharply risen over recent decades. Despite the established role of genetics in the onset and progression of such conditions, the environment was also shown to play a pivotal role. Indeed, the COVID-19 pandemic has majorly impacted people's lifestyles and healthy habits, especially among the youth, which might have led to a significant increase in this trend. Therefore, the aim of this study was to investigate the actual prevalence of refractive errors in a large cohort of pediatric patients. METHODS: A large cohort of 496 participants was screened through anamnesis, a non-cycloplegic autorefractometry, a corrected and uncorrected visual acuity assessment, and a questionnaire and was retrospectively evaluated. RESULTS: Overall, refractive errors were present in 25.1% of eyes, of which 14.6% were diagnosed with myopia/myopic astigmatism and 10.5% with hyperopia/hyperopic astigmatism. Among the patients enrolled, 298 (60%) had their eyes checked one year earlier or before and 122 (25%) had never had ophthalmological consultations; a total of 105 (21%) needed glasses and 34 (7%) required a change in their previous prescription. A substantial increase in daily electronic device screen exposure was declared by 426 patients (87.6%). CONCLUSIONS: Pediatric patients appear to have a higher prevalence of refractive errors than before.

Accuracy of Formulas for Intraocular Lens Power Calculation After Myopic Refractive Surgery.

PURPOSE: To assess the accuracy of the following intraocular lens (IOL) power formulas: Barrett True-K No History (BTKNH), Emmetropia Verifying Optical 2.0 Post Myopic LASIK/PRK (EVO 2.0), Haigis-L, American Society of Cataract and Refractive Surgery (ASCRS) average, and Shammas, designed for patients who have undergone previous myopic refractive surgery, independent of preexisting clinical history and corneal tomographic measurements. METHODS: Data from 302 eyes of 302 patients who previously underwent myopic refractive surgery and had cataract surgery done by a single surgeon with only one IOL type inserted were included. The predicted refraction was calculated for each of the formulas and compared with the actual refractive outcome to give the prediction error. Subgroup analysis based on the axial length and mean keratometry was performed. RESULTS: On the basis of mean absolute prediction error (MAE), the formulas were ranked as follows: Haigis-L (0.61 diopters [D]), ASCRS average (0.63 D), BTKNH (0.67 D), EVO 2.0 (0.68 D), and Shammas (0.69 D). The Haigis-L had a statistically significant lower MAE compared with all formulas (P < .05) except the ASCRS average. Hyperopic mean prediction errors were seen in all formulas for axial lengths of greater than 30 mm or mean keratometry values of 35.00 diopters or less. CONCLUSIONS: The Haigis-L and the ASCRS average formulas provided the most accurate results in the overall population evaluated in this study. Moreover, according to data observed, it is important to be careful handling very long eyes and very flat corneas because hyperopic refractions could be more common. [J Refract Surg. 2022;38(7):443-449.].

Classification Tree to Analyze Factors Connected with Post Operative Complications of Cataract Surgery in a Teaching Hospital.

BACKGROUND: Artificial intelligence (AI) is becoming ever more frequently applied in medicine and, consequently, also in ophthalmology to improve both the quality of work for physicians and the quality of care for patients. The aim of this study is to use AI, in particular classification tree, for the evaluation of both ocular and systemic features involved in the onset of complications due to cataract surgery in a teaching hospital. METHODS: The charts of 1392 eyes of 1392 patients, with a mean age of 71.3 ± 8.2 years old, were reviewed to collect the ocular and systemic data before, during and after cataract surgery, including post-operative complications. All these data were processed by a classification tree algorithm, producing more than 260 million simulations, aiming to develop a predictive model. RESULTS: Postoperative complications were observed in 168 patients. According to the AI analysis, the pre-operative characteristics involved in the insurgence of complications were: ocular comorbidities, lower visual acuity, higher astigmatism and intra-operative complications. CONCLUSIONS: Artificial intelligence application may be an interesting tool in the physician's hands to develop customized algorithms that can, in advance, define the post-operative complication risk. This may help in improving both the quality and the outcomes of the surgery as well as in preventing patient dissatisfaction.

Evaluation of Donor and Recipient Characteristics Involved in Descemet Stripping Automated Endothelial Keratoplasty Outcomes.

Aims: To evaluate both donor and recipient features involved in visual acuity restoring and complication insurgence in eyes that have undergone Descemet stripping automated endothelial keratoplasty (DSAEK). Methods: In this retrospective study, charts of 111 eyes of 96 patients (mean age 70.25 ± 8.58 years) that underwent DSAEK were evaluated. Only Fuch's Distrophy (FD) or Bullous Keratopathy (BK) due to cataract surgery eyes were included. A complete ophthalmic check with endothelial cell density (ECD) and central corneal thickness (CCT) measurement was performed before surgery and at 1, 3, 6, and 12 months follow-up. Each DSAEK was performed by the same well-trained surgeon; only pre-cut lenticules, provided by same Eye Bank, were implanted. Results: A total of 48 (43%) complications have been observed (most of them were 22 partial graft detachments and 17 IOP spikes). At the last follow-up (mean: 8.58 ± 4.09 months), a significant increase (p < 0.05) of best corrected visual acuity (BCVA) was detected. Overall mean BCVA of the eyes evaluated was 0.40 ± 0.43 LogMAR with BK eyes showing a significantly higher improvement (p < 0.05) compared to FD eyes. The only factor showing a significant correlation (p < 0.05) with visual acuity enhancement was the implant of a lenticule thinner than 100 µm. Recipient features significantly (p < 0.05) associated with complications observed after surgery were glaucoma and diabetes mellitus. Conclusion: The use of a graft thinner than 100 µm can provide better visual acuity recovery while recipients affected by glaucoma or diabetes mellitus are more prone to develop complications after surgery.

Application of Artificial Intelligence in the Analysis of Features Affecting Cataract Surgery Complications in a Teaching Hospital.

Aims: To evaluate the ocular and systemic factors involved in cataract surgery complications in a teaching hospital using artificial intelligence. Methods: One eye of 1,229 patients with a mean age of 70.2 ± 10.3 years old that underwent cataract surgery was selected for this study. Ocular and systemic details of the patients were recorded and then analyzed by means of artificial intelligence. A total of 1.25 billion simulations of artificial intelligence learning and testing were conducted on several variables and a customized model of analysis was developed. Results: A total of 73 complications were recorded in this study. According to the analysis performed, the main factors involved in cataract surgery complications were: a surgeon in training, axial length and intraocular lens power. The model predicted how long surgery would last with an error of <6 min compared to the effective time needed. Conclusions: According to the data here obtained, artificial intelligence could be an interesting option to build customized models able to prevent complications and to predict actual surgery time. The customized algorithm option allows the development of better models adaptable to different units as well as the possibility to be calibrated for the same unit along time.