Tomographic Findings in the Retina of Unvaccinated Patients with COVID Pneumonia: Prospective Longitudinal Study.

There is no definitive evidence on the extent of SARS-CoV-2's effect on the retina. This study aims to determine if the natural history of SARS-CoV-2 infection affects tomographic findings in the retina of patients with COVID-19 pneumonia. This is a prospective cohort study of patients hospitalized with COVID-19 pneumonia. The patients underwent ophthalmological explorations and optical coherence tomography during the acute phase of the infection and at a follow-up 12 weeks later. The primary outcomes were the central retinal thickness and central choroidal thickness, which were compared longitudinally and with non-COVID-19 historical controls. No statistically relevant differences were observed in the longitudinal analysis of the thickness of the central retina (p = 0.056), central choroid (p = 0.99), retinal nerve fiber layer (p = 0.21), or ganglion cell layer (p = 0.32). Patients with acute COVID-19 pneumonia showed significantly greater central retinal thickness than non-COVID controls (p = 0.006). In conclusion, tomographic measures of the retina and choroid are not influenced by the phase of COVID-19 infection and remain stable during 12 weeks. The central retinal thickness may increase in the acute phase of COVID-19 pneumonia, but more epidemiological studies using optical coherence tomography in the early stages of the disease are needed.

External validation of cut-off points for foveal thickness taking into account the intraretinal fluid using optical coherence tomography to diagnose diabetic macular oedema.

BACKGROUND: In late 2015, cut-off points were published for foveal thickness to diagnose diabetic macular oedema taking into account the presence of intraretinal fluid using optical coherence tomography (OCT) in primary care patients (90 µm in the presence of intraretinal fluid and 310 µm otherwise). METHODS: This cross-sectional observational study was carried out on 134 eyes of diabetic patients treated in specialised ophthalmology services in a Spanish region in 2012-2013, to externally validate the aforementioned cut-off points. The main variable (Clinical Standard) was the diagnosis of macular oedema through indirect ophthalmoscopy and posterior segment slit-lamp biomicroscopy. As validation variables, both the foveal thickness and the presence of intraretinal fluid obtained by OCT were used. Validation was performed using bootstrapping by calculating the area under the ROC curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR). RESULTS: Forty-one eyes presented diabetic macular oedema (30.6%). The bootstrapping validation parameters were: AUC, 0.88; sensitivity, 0.75; specificity, 0.95; PLR, 14.31; NLR, 0.26. These values were very similar to those of the original publication. CONCLUSION: We have externally validated in specialised care patients the cut-off points published for the diagnosis of diabetic macular oedema. We suggest that others carry out validation studies in their communities.

A predictive screening tool to detect diabetic retinopathy or macular edema in primary health care: construction, validation and implementation on a mobile application.

The most described techniques used to detect diabetic retinopathy and diabetic macular edema have to be interpreted correctly, such that a person not specialized in ophthalmology, as is usually the case of a primary care physician, may experience difficulties with their interpretation; therefore we constructed, validated and implemented as a mobile app a new tool to detect diabetic retinopathy or diabetic macular edema (DRDME) using simple objective variables. We undertook a cross-sectional, observational study of a sample of 142 eyes from Spanish diabetic patients suspected of having DRDME in 2012-2013. Our outcome was DRDME and the secondary variables were: type of diabetes, gender, age, glycated hemoglobin (HbA1c), foveal thickness and visual acuity (best corrected). The sample was divided into two parts: 80% to construct the tool and 20% to validate it. A binary logistic regression model was used to predict DRDME. The resulting model was transformed into a scoring system. The area under the ROC curve (AUC) was calculated and risk groups established. The tool was validated by calculating the AUC and comparing expected events with observed events. The construction sample (n = 106) had 35 DRDME (95% CI [24.1-42.0]), and the validation sample (n = 36) had 12 DRDME (95% CI [17.9-48.7]). Factors associated with DRDME were: HbA1c (per 1%) (OR = 1.36, 95% CI [0.93-1.98], p = 0.113), foveal thickness (per 1 µm) (OR = 1.03, 95% CI [1.01-1.04], p < 0.001) and visual acuity (per unit) (OR = 0.14, 95% CI [0.00-0.16], p < 0.001). AUC for the validation: 0.90 (95% CI [0.75-1.00], p < 0.001). No significant differences were found between the expected and the observed outcomes (p = 0.422). In conclusion, we constructed and validated a simple rapid tool to determine whether a diabetic patient suspected of having DRDME really has it. This tool has been implemented on a mobile app. Further validation studies are required in the general diabetic population.

Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.

UNLABELLED: No studies have yet evaluated jointly central foveal thickness (CFT) and the presence of intraretinal fluid (PIF) to diagnose diabetic macular oedema (DMO) using optic coherence tomography (OCT). We performed a cross-sectional observational study to validate OCT for the diagnosis of DMO using both CFT and PIF assessed by OCT (3D OCT-1 Maestro). A sample of 277 eyes from primary care diabetic patients was assessed in a Spanish region in 2014. OUTCOME: DMO diagnosed by stereoscopic mydriatic fundoscopy. OCT was used to measure CFT and PIF. A binary logistic regression model was constructed to predict the outcome using CFT and PIF. The area under the ROC curve (AUC) of the model was calculated and non-linear equations used to determine which CFT values had a high probability of the outcome (positive test), distinguishing between the presence or absence of PIF. Calculations were made of the sensitivity, specificity, and the positive (PLR) and negative (NLR) likelihood ratios. The model was validated using bootstrapping methodology. A total of 37 eyes had DMO. AUC: 0.88. Positive test: CFT ≥90 µm plus PIF (≥310 µm if no PIF). Clinical parameters: sensitivity, 0.83; specificity, 0.89; PLR, 7.34; NLR, 0.19. The parameters in the validation were similar. In conclusion, combining PIF and CFT provided a tool to very precisely discriminate the presence of DMO. Similar studies are needed to provide greater scientific evidence for the use of PIF in the diagnosis of DMO.

Validity of Optical Coherence Tomography as a Diagnostic Method for Diabetic Retinopathy and Diabetic Macular Edema.

To validate optical coherence tomography (OCT) for the diagnosis of referable retinopathy (severe, very severe or proliferative retinopathy, and macular edema) in diabetic patients. We performed a cross-sectional observational study. A random sample was analyzed comprising 136 eyes of diabetic patients referred to the hospital in Elche (Spain) with suspected referable retinopathy between October 2012 and June 2013. Primary variable: Referable retinopathy measured by ophthalmological examination of the retina. OCT data included: central foveal thickness, presence of intraretinal fluid, and fundus photographs. The receiver operating characteristic (ROC) curve was calculated to determine the minimum thickness value with a positive likelihood ratio >10. To determine the validity of OCT, the following diagnostic test was defined: Positive: if the patient had at least 1 of these criteria: foveal thickness greater than the point obtained on the previously defined ROC curve, intraretinal fluid, abnormal fundus photographs; Negative: none of the above criteria. Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and Kappa statistic were determined. Of the 136 eyes, 48 had referable retinopathy (35.3%, 95% confidence interval [CI]: 27.3-43.3). The minimum thickness value with a positive likelihood ratio >10 was 275 µm. The diagnostic test constructed showed: sensitivity, 91.67% (95% CI: 79.13-97.30); specificity, 93.18% (95% CI: 85.19-97.20); positive predictive value, 88.00% (95% CI: 75.00-95.03); negative predictive value, 95.35% (95% CI: 87.87-98.50); positive likelihood ratio, 13.44 (95% CI: 6.18-29.24); negative likelihood ratio, 0.09 (95% CI: 0.03-0.23). The Kappa value was 0.84 (95% CI: 0.75-0.94, P < 0.001. This study constructed a diagnostic test for referable diabetic retinopathy with type A evidence. Nevertheless, studies are needed to determine the validity of this test in the general diabetic population.