RESUMO

This review article discusses the relationship between ocular perfusion pressure and glaucoma, including its definition, factors that influence its calculation and epidemiological studies investigating the influence of ocular perfusion pressure on the prevalence, incidence and progression of glaucoma. We also list the possible mechanisms behind this association, and discuss whether it is secondary to changes in intraocular pressure, blood pressure or both. Finally, we describe the circadian variation of ocular perfusion pressure and the effects of systemic and topical medications on it. We believe that the balance between IOP and BP, influenced by the autoregulatory capacity of the eye, is part of what determines whether an individual will develop optic nerve damage. However, prospective, longitudinal studies are needed to better define the role of ocular perfusion pressure in the development and progression of glaucoma.

Assuntos

RESUMO

Purpose. To investigate misalignments (MAs) on retinal nerve fiber layer thickness (RNFLT) measurements obtained with Cirrus(©) SD-OCT. Methods. This was a retrospective, observational, cross-sectional study. Twenty-seven healthy and 29 glaucomatous eyes of 56 individuals with one normal exam and another showing MA were included. MAs were defined as an improper alignment of vertical vessels in the en face image. MAs were classified in complete MA (CMA) and partial MA (PMA), according to their site: 1 (superior, outside the measurement ring (MR)), 2 (superior, within MR), 3 (inferior, within MR), and 4 (inferior, outside MR). We compared RNFLT measurements of aligned versus misaligned exams in all 4 sectors, in the superior area (sectors 1 + 2), inferior area (sectors 3 + 4), and within the measurement ring (sectors 2 + 3). Results. RNFLT measurements at 12 clock-hour of eyes with MAs in the superior area (sectors 1 + 2) were significantly lower than those obtained in the same eyes without MAs (P = 0.043). No significant difference was found in other areas (sectors 1 + 2 + 3 + 4, sectors 3 + 4, and sectors 2 + 3). Conclusion. SD-OCT scans with superior MAs may present lower superior RNFLT measurements compared to aligned exams.

RESUMO

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Assuntos

RESUMO

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

OBJETIVO: Avaliar a sensibilidade e especificidade dos classificadores de aprendizagem de máquina no diagnóstico de glaucoma usando Spectral Domain OCT (SD-OCT) e perimetria automatizada acromática (PAA). MÉTODOS: Estudo transversal observacional. Sessenta e dois pacientes com glaucoma e 48 indivíduos normais foram incluídos. Todos os pacientes foram submetidos a exame oftalmológico completo, e perimetria automatizada acromática (24-2 SITA; Humphrey Field Analyzer II, Carl Zeiss Meditec, Inc., Dublin, CA) e exame de imagem da camada de fibras nervosas utilizando SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Curvas ROC (Receiver operator characteristic) foram obtidas para todos os parâmetros do SD-OCT e índices globais do campo visual (MD, PSD, GHT). Subsequentemente, os seguintes classificadores de aprendizagem de máquina (CAMs) foram testados usando parâmetros do OCT e CV: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA), Support Vector Machine Linear (SVML) e Support Vector Machine Gaussian (SVMG). Áreas abaixo da curva ROC (aROC) obtidas com os parâmetros isolados do campo visual (CV) e OCT foram comparados com os CAMs usando dados associados do OCT e CV. RESULTADOS: Combinando os dados do OCT e do CV, aROCs dos CAMs variaram entre 0,777(CTREE) e 0,946 (RAN). A maior aROC dos CAMs OCT+CV obtida com RAN (0,946) foi significativamente maior que o melhor parâmetro do OCT (p<0,05), mas não houve diferença estatística significativa com o melhor parâmetro do CV (p=0,19). CONCLUSÃO: Os classificadores de aprendizagem de máquina treinados com dados do OCT e CV podem discriminar entre olhos normais e glaucomatosos com sucesso. A combinação das medidas do OCT e CV melhoraram a acurácia diagnóstica comparados aos parâmetros do OCT.

Assuntos

RESUMO

PURPOSE: To evaluate the intrasession, intersession, and interexaminer variabilities of retinal nerve fiber layer measurements (RNFL) with spectral-domain optical coherence tomography (OCT). METHODS: A total of 32 healthy individuals and 34 patients with chronic glaucoma underwent RNFL measurements with the Cirrus HD-OCT Model 4000 (Carl Zeiss Meditec, Dublin, CA, USA) 5 times during the same sitting by one examiner to assess intrasession variability. The same examiner performed RNFL measurements in the same patients on 5 different days to assess intersession variability. A second examiner performed RNFL measurements in the same patients to assess interexaminer variability. The coefficients of variation and intraclass correlation coefficients were obtained for the following parameters: average thickness, quadrant thickness, and Clock hour thickness measurements. RESULTS: Intrasession variability: In patients with glaucoma, coefficients of variation ranged from 4.51% to 11.84%. Intraclass correlation coefficients ranged from 0.74 to 0.99. In healthy individuals, coefficients of variation ranged from 2.92% to 6.99%. Intraclass correlation coefficients ranged from 0.89 to 0.98. Intersession variability: In patients with glaucoma, coefficients of variation ranged from 3.68% to 10.50%. Intraclass correlation coefficients ranged from 0.82 to 0.99. In healthy individuals, coefficients of variation ranged from 3.13% to 6.92%. Intraclass correlation coefficients ranged from 0.87 to 0.99. Interexaminer variability: In patients with glaucoma, coefficients of variation ranged from 2.62% to 14.94%. Intraclass correlation coefficients ranged from 0.55 to 0.98. In healthy individuals, coefficients of variation ranged from 2.04% to 7.31%. Intraclass correlation coefficients ranged from 0.86 to 0.98. CONCLUSIONS: These findings indicate that RNFL measurements with spectral-domain OCT display excellent reproducibility, with low intrasession, intersession, and interexaminer variabilities.

Assuntos

RESUMO

OBJECTIVES: The purpose of this study was to evaluate the ERP P300 in non insulin dependent diabetes mellitus (NIDDM) patients without cognitive impairment and the relationship with clinical variables, the presence of retinopathy and previous hypoglycemic episodes. METHODS: NIDDM patients (N=44) without evidence of cognitive impairment and controls (N=17) were studied clinically and with ancillary exams and the ERPs P300 were recorded. Patients were examined clinically and with the Folstein Mini-Mental Examination (MMSE) for cognitive function and all patients showed a score higher than 26 (maximal value=30). Previous hypoglycemia was evaluated through a questionnaire establishing the number of episodes and the symptoms of hypoglycemia in a scale scoring from zero to 15. RESULTS: ERP P300 latencies were significantly higher in NIDDM patients than in controls (p<0.03). ERP P300 measures were significantly related to age (Pearson, p<0.01) and not to metabolic variables, disease duration or the presence of retinopathy. Severity of hypoglycemia was not associated to ERP P300 latency. CONCLUSIONS: Our study supports the evidence that NIDDM patients, without signs of nervous system involvement, have ERP P300 alterations and this is not related to retinopathy, metabolic variables or previous hypoglycemic episodes. Chronic hyperglycemia may alter brain glucose transport and increase tolerance to hypoglycemia effects in the nervous system.

Assuntos