Purpose. This study aims to explore the risk factors of asthenopia in the myopic population. Methods. In this cross-sectional study, myopia patients were inquired about their eye habits and were requested to complete an asthenopia questionnaire and ocular examinations. Age, gender, occupation, anisometropia, eye care education, weekly outdoor activity time, duration of continuous near work, daily screen time, dry eye, near phoria, and binocular accommodative facility were calculated using the Student’s test-test, Mann Whitney U test, and Pearson’s chi-square test. Spherical equivalents and astigmatism were calculated using a generalized estimating equation. Binary logistic regression was performed on factors with a p -value <0.05. Results. Of the 65 myopic patients, 57% showed asthenopia, 52% experienced blurry vision, 37% felt their eyes hurt or sore, and 28% felt tired when performing close work. Asthenopia patients were older than patients without asthenopia (Z = −2.887, p = 0.004 ). Daily screen time, continuous near-work time, eye care education, and dry eye were positively correlated with asthenopia (χ2 = 8.64, p = 0.003 ; χ2 = 13.873, p < 0.001 , χ2 = 9.643, p = 0.002 ; χ2 = 7.035, p = 0.008 ). After eliminating collinearity, eye care education and continuous near-work time were identified as independent risk factors of asthenopia, with odds ratios of 0.115 and 4.227, respectively. Conclusion. This study shows that receiving eye care education from schools and hospitals and limiting near-work duration to less than 45 minutes per session could reduce the occurrence of asthenopia in myopic patients. This approach may be a more economical and convenient way for myopic people to relieve asthenopia.

Objective: The incidence and risk factors of neovascular glaucoma (NVG) secondary proliferative diabetic retinopathy (PDR) after pars plana vitrectomy (PPV) are unclear and reports in the published literature are inconsistent. Therefore, a systematic review and meta-analysis were conducted to clarify the risk factors associated with neovascular glaucoma. Methods: PubMed, Embase, and The Cochrane Library were systematically searched without language limitations for studies related to NVG after PPV in PDR patients. We used R software to fit the correlation between incidence and the date of publication for studies and performed a Spearman analysis. For binary and continuous variables, the odds ratios (ORs) with 95% confidence intervals (CIs) were pooled, respectively, using Review Manager 5.3 (The Cochrane Collaboration). Results: Twenty-six studies with 5161 patients were included in our meta-analysis. The overall pooled incidence of NVG after PPV in PDR patients was 6% (95% CI, 0.05–0.07, p-value < 0.00001). Pooled estimates indicated a positive correlation for NVG after PPV in PDR patients with higher baseline IOP (OR, 1.26; 95%CI,0.56–1.95, p-value = 0.0004), preoperative iris neovascularization (INV) (OR, 5.66; 95% CI, 2.10–15.23, p-value = 0.0006), preoperative or intraoperative combined cataract surgery (OR, 2.00; 95% CI, 1.15–3.46, p-value = 0.01), postoperative vitreous hemorrhage (VH) (OR, 3.53; 95% CI, 1.63–7.66, p-value = 0.001), and a negative correlation with age (OR, −2.90; 95%CI, −5.00 to −0.81, p-value < 0.007). Conclusion: Our systematic review and meta-analysis indicated that the main risk factors for NVG after PPV in PDR patients included higher baseline IOP, preoperative INV, preoperative or intraoperative combined cataract surgery, postoperative VH, and was negatively correlated with age.

PurposeTo construct a proper model to screen for diabetic retinopathy (DR) with the RETeval.MethodThis was a cross-sectional study. Two hundred thirty-two diabetic patients and seventy controls were recruited. The DR risk assessment protocol was performed to obtain subjects’ DR risk score using the RETeval. Afterwards, the receiver operating characteristic (ROC) curve was used to determine the best cutoff for diagnosing DR. Random forest and decision tree models were constructed.ResultsWith increasing DR severity, the DR score gradually increased. When the DR score was used to diagnose DR, the ROC curve had an area under the curve of 0.881 (95% confidence interval: 0.836-0.927, P &lt; 0.001), with a best cutoff value of 22.95, a sensitivity of 74.3% (95 CI: 66.0%~82.6%), and a specificity of 90.6% (95 CI: 83.7% ~94.8%). The top four risk factors selected by the random forest were used to construct the decision tree for diagnosing DR, which had a sensitivity of 93.3% (95% CI: 86.3%~97.0%) and a specificity of 80.3% (95% CI: 72.1% ~86.6%).ConclusionsThe DR risk assessment protocol combined with the decision tree model was innovatively used to evaluate the risk of DR, improving the sensitivity of diagnosis, which makes this method more suitable than the current protocol for DR screening.