慢性中央浆液性脉络膜病变和卵黄状营养不良成年患者的鉴别诊断方法

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研究目的：优化成年患者发生的慢性中央浆液性脉络膜病变（CSCR）和卵黄状营养不良（VD）的鉴别诊断。

研究任务：在多模式诊断的基础上研究VD和CSCR的特征，通过数学建模来开发诊所不同设施条件下的鉴别诊断方法。

材料和方法：该研究包括61名患有长期神经上皮脱落的患者（90只眼睛）。收集了所有患者的病史，包括家族史，并进行了常规眼科检查：测量最佳矫正视力、裂隙灯检查和眼底照相、视网膜光学相干断层扫描（OCT）和血管造影光学相干断层扫描（OCTA）、短波自发荧光（SW-AF）、视网膜荧光血管造影（FAG）、吲哚菁绿视网膜血管成像（ICGA）。将患者分为2组：30名（30只眼睛）患有卵黄状营养不良的患者和31名（31只眼睛）患有慢性中央浆液性脉络膜病变的患者。采用二元逻辑回归法来评估患病概率。

结果：研究了两组中出现的诊断预测因素，获得了估计检测疾病概率的数学模型。考虑到获得的计算疾病检测概率的公式，包括不同研究组合的标准，制定了鉴别诊断的方法：OCT（曲线下面积0.946）；SW-AF（曲线下面积0.955），OCT和SW-AF（曲线下面积0.980）；SW-AF、FAG和ICGA（曲线下面积0.989）。

结论：获得的模型可以在不同条件下的诊所对卵黄样营养不良和慢性中央浆液性脉络膜病变进行鉴别诊断。

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作者简介

Nataliia Matcko

S. Fyodorov Eye Microsurgery Federal State Institution, the Saint Petersburg Branch; I.I. Mechnikov North-Western State Medical University

编辑信件的主要联系方式.
Email: matsko.natalia@mail.ru
ORCID iD: 0000-0001-8909-9999
SPIN 代码: 9790-4066

Ophthalmologist, S. Fyodorov Eye Microsurgery Federal State Institution, Saint Petersburg Branch, PhD Student, I.I. Mechnikov North-Western State Medical University

俄罗斯联邦, St. Petersburg; St. Petersburg

Marina Gatsu

S. Fyodorov Eye Microsurgery Federal State Institution, the Saint Petersburg Branch; I.I. Mechnikov North-Western State Medical University

Email: m-gatsu@yandex.ru

Dr. Sci. (Med.), Deputy Director of Clinical Services, S. Fyodorov Eye Microsurgery Federal State Institution, the Saint Petersburg Branch, professor, I.I. Mechnikov North-Western State Medical University

俄罗斯联邦, St. Petersburg; St. Petersburg

参考

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2. Fig. 1. Optical coherence tomograms of patients with adult-onset vitelliform dystrophy (a, c) and with chronic central serous chorioretinopathy (b, d). On all scans, detachment of the neuroepithelium is visible, deposition of optically dense deposits on the posterior surface of the neuroepithelium detachment, elongation of photoreceptor cells: a, b — without deposition of subretinal material along the retinal pigment epithelium; c, d — with deposition of subretinal material along the retinal pigment epithelium

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3. Fig. 2. Example of multimodal diagnosis, patient with adult-onset foveomacular vitelliform dystrophy; a — fundus photo (in the macula, there is a light area less than 1 optic disc diameter, dyspigmentation area), b — short-wavelength autofluorescence (SW-AF) (hyperAF area, surrounded by a ring of hypoAF with a zone of confluent more intensive hyperAF under them, hyperAF brightness — 3, according to Grayscale); e — SD-OCT (NED, hyperreflective partially resorbed vitelliform material, massive subretinal deposits, diffusely enlarged choroidal vessels, hyperreflective dots in the subretinal space); f — OCT-A (shadow of the NED with subretinal material, rarefication of the choriocapillaris along the rand of the NED, in the areas of RPE atrophy; g — ICGA (blockage of the fluorescence in the vitelliform material’s deposition area, choroidal vessels are not enlarged); c, d — FA — arteriovenous phase and dye recirculation phase, respectively (blockage of the fluorescence by the vitelliform material, with subsequent dye accumulation in this area)

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4. Fig. 3. Example of multimodal diagnosis, patient with chronic CSCR. a — fundus photo (in the macula, there is a light area about 1 optic disc diameter), b — short-wavelength autofluorescence (SW-AF) of the eye fundus (background hypoAF, central confluent hyperAF, diffuse hyper AF along the lower rand of the lesion, hyperAF brightness — 2, according to Grayscale); c — fluorescein angiography (dye accumulation in the temporal parafoveolar area out of undetermined source); d – indocyanine green angiography, middle phase (enlargement of choroidal veins, choroidal hyperpermeability); e — spectral domain optical coherence tomography (neuroepithelial detachment, hyperreflective deposits along the retinal pigment epithelium, elongation of photoreceptors, deposits in the shape of “dense fringe”, diffusely enlarged choroidal vessels); f – optical coherence tomography angiography (half-shade from neuroepithelial detachment by subretinal material)

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5. Fig. 4. ROC — curves of the resulting models. Model 1 — criteria of spectral domain optical coherence tomography; Model 2 — criteria of spectral domain optical coherence tomography, and those of optical coherence tomography angiography; Model 3 — criteria of spectral domain optical coherence tomography, and those of short-wavelength autofluorescence; Model 4 — criteria of spectral domain optical coherence tomography, those of optical coherence tomography angiography, and those of short-wavelength autofluorescence; Model 5 (expert model) — criteria of spectral domain optical coherence tomography, those of optical coherence tomography angiography, those of short-wavelength autofluorescence, those of fluorescein angiography, and those of indocyanine green angiography

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6. Fig. 5. Example of applying the formula for calculating the probability of detecting central serous chorioretinopathy, Model 4: a — high-resolution spectral domain optical coherence tomography; b — short-wavelength autofluorescence

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7. Fig. 6. Example of applying the formula for calculating the probability of detecting vitelliform dystrophy, Model 4: a — high-resolution spectral domain optical coherence tomography; b — short-wavelength autofluorescence

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