基于视网膜血流密度指标的随机森林模型诊断慢性肾脏病的效能分析  

Analysis on the efficacy of random forest model based on retinal blood flow density indicators in diagnosis of chronic kidney disease

作　　者：兰林 雷代在 杨映霞 洪祎祎 胡湘钰 叶琨[4] 徐帆 LAN Lin;LEI Daizai;YANG Yingxia;HONG Yiyi;HU Xiangyu;YE Kun;XU Fan(Guilin Medical University,Guangxi 541199,China;Department of Ophthalmology,the People′s Hospital of Guangxi Zhuang Autonomous Region(Guangxi Academy of Medical Sciences),Nanning 530021,China;Department of Radiology,Guangxi Hospital Division of the First Affiliated Hospital,Sun Yat-sen University,Nanning 530021,China;Department of Nephrology,the People′s Hospital of Guangxi Zhuang Autonomous Region(Guangxi Academy of Medical Sciences),Nanning 530021,China)

机构地区：[1]桂林医学院,广西541199 [2]广西壮族自治区人民医院(广西医学科学院)眼科,南宁530021 [3]中山大学附属第一医院广西医院放射影像科,南宁530021 [4]广西壮族自治区人民医院(广西医学科学院)肾内科,南宁530021

出　　处：《中国临床新医学》2024年第12期1387-1392,共6页CHINESE JOURNAL OF NEW CLINICAL MEDICINE

基　　金：广西科技基地和人才专项(编号:桂科AD22035011);广西眼科疾病临床医学研究中心项目(编号:桂科AD19245193);广西医疗卫生适宜技术开发与推广应用项目(编号:S2019084)。

摘　　要：目的基于视网膜血流密度指标,构建可用于诊断慢性肾脏病(CKD)的随机森林模型,以促进CKD的早期诊断和个性化防治。方法招募2022年2月至2023年12月广西壮族自治区人民医院收治的CKD患者27例(44眼)作为病例组,同期招募健康人群47名(71眼)作为对照组。采用光学相干断层扫描血管成像(OCTA)获取视网膜血流密度指标。基于视网膜血流密度指标及一般临床指标构建用于诊断CKD的随机森林模型,通过夏普利重要性分析和夏普利方法提供模型的可解释性。结果病例组整个浅层毛细血管丛(SCP)血流密度、下半SCP血流密度高于对照组,差异有统计学意义(P<0.05),两组其他视网膜血流密度指标比较差异无统计学意义(P>0.05)。模型的准确度为86.96%,灵敏度为100.00%,特异度为72.73%。上半SCP血流密度、整个SCP血流密度、早期糖尿病视网膜病变治疗研究(ETDRS)分区的SCP血流密度、下半SCP血流密度、整个深层毛细血管丛(DCP)血流密度、上半DCP血流密度在预测模型中是贡献量较大的主要指标。结论基于视网膜血流密度指标的随机森林模型可有效辅助CKD诊断。Objective To construct a random forest model for the diagnosis of chronic kidney disease(CKD)based on retinal blood flow density indicators to promote the early diagnosis and personalized prevention and treatment of CKD.Methods Twenty-seven patients with CKD(44 eyes)who were admitted to the People′s Hospital of Guangxi Zhuang Autonomous Region from February 2022 to December 2023 were recruited as the case group,and 47 healthy people(71 eyes)were recruited as the control group during the same period.Retinal blood flow density indicators were obtained by using optical coherence tomography angiography(OCTA).A random forest model used for diagnosing CKD was constructed based on retinal blood flow density indicators and general clinical indicators,and the interpretability of the model was provided by Shapley importance analysis and Shapley method.Results The whole superficial capillary plexus(SCP)blood flow density and the lower half SCP blood flow density in the case group were higher than those in the control group,and the differences were statistically significant(P<0.05),while there were no statistically significant differences in the other indicators of retinal blood flow density between the two groups(P>0.05).The accuracy of the model was 86.96%,and the sensitivity of the model was 100.00%,and the specificity of the model was 72.73%.The upper half SCP blood flow density,the whole SCP blood flow density,Early Treatment Diabetic Retinopathy Study(ETDRS)region of the SCP blood flow density,the lower half SCP blood flow density,the whole deep capillary plexus(DCP)blood flow density,and the upper half DCP blood flow density were the main contributory indicators in the predictive model.Conclusion The random forest model based on retinal blood flow density indicators can effectively assist the diagnosis of CKD.

关 键 词：血流密度 慢性肾脏病 光学相干断层扫描血管成像 随机森林模型 

分 类 号：R774[医药卫生—眼科]