Peripapillary retinal nerve fibre layer thickness in women with iron deficiency anaemia

Purpose. Retinal nerve fibre layer (RNFL) is a sensitive structure, which is affected by anaemia due to hypoxia. A timely detection of RNFL thinning may aid preventing devastating complications. Optical coherence tomography (OCT) measures RNFL thinning with accuracy and helps in detecting thinning of the retinal layer in anaemic patients. This study was destined to evaluate thinning of RNFL in anaemic patients and their correlation with the haemoglobin level. Methods. It was a prospective comparative study. Total of 151 patients were included in this study. Patients with retinal diseases were excluded from this study. After initial evaluation, haematological and ophthalmological parameters were measured. RNFL was measured with OCT and corroborated with the Hb level and analysed accordingly. EPI and SPSS softwares were used for detail analysis and the correlation between RNFL thinning and the Hb level. Initially, each eye was separately assigned a value (0, 1, and 2) (normal, borderline, and abnormal, respectively) as per the severity of thinning, and then, the sum of the scores of both eyes were considered as a separate variable, and a multiple linear regression analysis was performed with the independent variables. Results. RNFL thinning was found to be significant in each group of patients. There was a strong correlation of RNFL thinning with degree of anaemia. Conclusions. Thalassaemia, iron deficiency anaemia, and anaemia of chronic diseases are associated with the significant damage to RNFL. Degree of anaemia is the most important parameter for such thinning of the RNFL layer.

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“…Sex distribution among the study population was found to corroborate with various previous studies [13][14][15].…”

Section: Gender Distributionsupporting

confidence: 90%

Retinal Nerve Fibre Layer Thinning in Patients with Thalassaemia, Iron Deficiency Anaemia, and Anaemia of Chronic Diseases

Datta

Baidya

Banerjee

et al. 2020

Journal of Ophthalmology

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“…27,28 An adult study reported that thinning was observed in the nasal and inferior quadrants in the case of Fe deficiency. 29 None of our patients had Fe deficiency, so this factor was not a confounding factor in this study. The degree of peripapillary RNFL thinning may related to the severity of macular thinning.…”

Section: Discussionmentioning

confidence: 75%

Evaluation of retinal nerve fiber layer and choroidal thickness with spectral domain optical coherence tomography in children with sickle cell anemia

et al. 2021

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Background. The aim of this study is to examine the thickness of choroidal, macular and peripapillary retinal nerve fiber layer by spectral-domain optical coherence tomography (SD-OCT) in pediatric patients with sickle cell anemia (SCA) without retinopathy. Methods. A total of 75 children (30 SCA patients (Group 1) and 45 healthy individuals (Group 2) were included in the study. Macular (central, superior, inferior, nasal, temporal), choroidal (subfoveal, at nasal distances from the central fovea of 1000 μm [N1], 2000 μm [N2], 3000 μm [N3], at temporal distances from the central fovea of 1000 μm [T1], 2000 μm [T2], 3000 μm [T3]) and RNFL (average, temporal, superotemporal, inferotemporal, nasal, inferonasal and superonasal) measurements were performed by SD-OCT. These parameters were compared with healthy children with similar demographic characteristics. Results. The mean age was 14.11±3.86 (11-18) in sickle cell anemia patients and 13.15± 2.69 (10-18) in the healthy control group. Of the patients, 56.6% (n=17) of Group 1 and 44.4% (n=20) of Group 2 were male. Choroidal measurements made in the subfoveal, N1, N2, N3, T1, T2 and T3 quadrants showed that the choroid was thinner in 6 quadrants in SCA patients compared to the healthy group (p = 0.003, p = 0.039, p = 0.035, p = 0.595, p = 0.006, p = 0.005, p = 0.047, respectively). In RNFL measurements, there was significant thinning in the temporal, inferotemporal, and nasal quadrants of SCA patients compared to the healthy group. Changes in other quadrants were not significant. Conclusions. SD-OCT is a useful imaging method in the diagnosis and screening in patients with SCA without retinopathy. Early diagnosis of retinopathy during subclinical disease will prevent visual loss in these patients.

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“…This finding could be supported by some biological phenomena and observations. For example, patients with iron deficiency anemia (IDA) were reported to have significantly thinner peripapillary retinal nerve fiber layer (RNFL) thickness, and the Hgb levels were also significantly correlated with the average RNFL thickness value ( Türkyilmaz et al, 2013 ; Akdogan et al, 2015 ). Additionally, optic atrophy, disc edema, and epiretinal tissue on the optic disc have been identified in anemia patients ( Sethi et al, 2020 ; Ab Gani et al, 2021 ; Graf et al, 2021 ), which suggested that the optic disc was susceptible to be affected by anemia.…”

Section: Discussionmentioning

confidence: 99%

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

Zhao

Meng

Su³

et al. 2022

Front. Cell Dev. Biol.

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Background: Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images.Methods: The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model.Results: ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81–0.85 g/dl) and the AUC of the screening task of 0.93 (95%CI: 0.92–0.95). Compared with other screening approaches, it achieved the best performance of AUC and sensitivity when the test dataset size was larger than 1000. The model tended to focus on the area around the optic disc, retinal vessels, and some regions located at the peripheral area of the retina, which were undetected by non-UWF imaging.Conclusion: The deep-learning model ASModel_UWF could both predict Hgb concentration and screen anemia in a non-invasive and accurate way with high efficiency.

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Peripapillary retinal nerve fibre layer thickness in women with iron deficiency anaemia

Cited by 11 publications

References 19 publications

Retinal Nerve Fibre Layer Thinning in Patients with Thalassaemia, Iron Deficiency Anaemia, and Anaemia of Chronic Diseases

Retinal Nerve Fibre Layer Thinning in Patients with Thalassaemia, Iron Deficiency Anaemia, and Anaemia of Chronic Diseases

Evaluation of retinal nerve fiber layer and choroidal thickness with spectral domain optical coherence tomography in children with sickle cell anemia

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

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