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使用手持式视网膜电图仪 RETeval 筛查糖尿病视网膜病变的诊断模型。

A Diagnostic Model for Screening Diabetic Retinopathy Using the Hand-Held Electroretinogram Device RETeval.

机构信息

Department of Ophthalmology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.

Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.

出版信息

Front Endocrinol (Lausanne). 2021 Apr 12;12:632457. doi: 10.3389/fendo.2021.632457. eCollection 2021.

Abstract

PURPOSE

To construct a proper model to screen for diabetic retinopathy (DR) with the RETeval.

METHOD

This was a cross-sectional study. Two hundred thirty-two diabetic patients and seventy controls were recruited. The DR risk assessment protocol was performed to obtain subjects' DR risk score using the RETeval. Afterwards, the receiver operating characteristic (ROC) curve was used to determine the best cutoff for diagnosing DR. Random forest and decision tree models were constructed.

RESULTS

With increasing DR severity, the DR score gradually increased. When the DR score was used to diagnose DR, the ROC curve had an area under the curve of 0.881 (95% confidence interval: 0.836-0.927, P < 0.001), with a best cutoff value of 22.95, a sensitivity of 74.3% (95 CI: 66.0%82.6%), and a specificity of 90.6% (95 CI: 83.7% ~94.8%). The top four risk factors selected by the random forest were used to construct the decision tree for diagnosing DR, which had a sensitivity of 93.3% (95% CI: 86.3%97.0%) and a specificity of 80.3% (95% CI: 72.1% ~86.6%).

CONCLUSIONS

The DR risk assessment protocol combined with the decision tree model was innovatively used to evaluate the risk of DR, improving the sensitivity of diagnosis, which makes this method more suitable than the current protocol for DR screening.

摘要

目的

利用 RETeval 构建合适的模型来筛查糖尿病视网膜病变(DR)。

方法

这是一项横断面研究。共招募了 232 名糖尿病患者和 70 名对照者。使用 RETeval 获得受试者的 DR 风险评分,进行 DR 风险评估方案。随后,使用受试者工作特征(ROC)曲线确定诊断 DR 的最佳截断值。构建随机森林和决策树模型。

结果

随着 DR 严重程度的增加,DR 评分逐渐升高。当使用 DR 评分诊断 DR 时,ROC 曲线下面积为 0.881(95%置信区间:0.836-0.927,P<0.001),最佳截断值为 22.95,灵敏度为 74.3%(95%CI:66.0%-82.6%),特异性为 90.6%(95%CI:83.7%-94.8%)。随机森林选择的前四个风险因素用于构建诊断 DR 的决策树,其灵敏度为 93.3%(95%CI:86.3%-97.0%),特异性为 80.3%(95%CI:72.1%-86.6%)。

结论

DR 风险评估方案与决策树模型相结合,创新性地用于评估 DR 风险,提高了诊断的灵敏度,使其比当前的 DR 筛查方案更适用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/476a/8074966/c6652f459800/fendo-12-632457-g001.jpg

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