Abstract：
OBJECTIVE: The aim of the study was to construct and validate a risk nomogram for clinically significant macular edema (CSME) prediction in diabetes mellitus (DM) patients using systemic variables.
METHODS: In this retrospective study, DM inpatients who underwent routine diabetic retinopathy screening were recruited and divided into training and validation sets according to their admission date. Ninety-three demographic and systemic variables were collected. The least absolute shrinkage and selection operator was used to select the predictive variables from the training set. The selected variables were used to construct the CSME prediction nomogram. Internal and external validations were performed. The C-index, calibration curve and decision curve analysis (DCA) were reported.
RESULTS: A total of 349 patients were divided into the training set (240, 68.77%) and the validation set (109, 31.23%). The presence of diabetic peripheral neuropathy (DPN) symptoms, uric acid, use of insulin only or not for treatment, insulin dosage, urinary protein grade and disease duration were chosen for the nomogram. The C-index of the prediction nomogram was 0.896, 0.878 and 0.837 in the training set, internal validation and external validation, respectively. The calibration curves of the nomogram showed good agreement between the predicted and actual outcomes. DCA demonstrated that the nomogram was clinically useful.
CONCLUSIONS: A nomogram with good performance for predicting CSME using systemic variables was developed. It suggested that DPN symptoms and renal function may be crucial risk factors for CSME. Moreover, this nomogram may be a convenient tool for non-ophthalmic specialists to rapidly recognize CSME in patients and to transfer them to ophthalmologists for early diagnosis and treatment.
METHODS: In this retrospective study, DM inpatients who underwent routine diabetic retinopathy screening were recruited and divided into training and validation sets according to their admission date. Ninety-three demographic and systemic variables were collected. The least absolute shrinkage and selection operator was used to select the predictive variables from the training set. The selected variables were used to construct the CSME prediction nomogram. Internal and external validations were performed. The C-index, calibration curve and decision curve analysis (DCA) were reported.
RESULTS: A total of 349 patients were divided into the training set (240, 68.77%) and the validation set (109, 31.23%). The presence of diabetic peripheral neuropathy (DPN) symptoms, uric acid, use of insulin only or not for treatment, insulin dosage, urinary protein grade and disease duration were chosen for the nomogram. The C-index of the prediction nomogram was 0.896, 0.878 and 0.837 in the training set, internal validation and external validation, respectively. The calibration curves of the nomogram showed good agreement between the predicted and actual outcomes. DCA demonstrated that the nomogram was clinically useful.
CONCLUSIONS: A nomogram with good performance for predicting CSME using systemic variables was developed. It suggested that DPN symptoms and renal function may be crucial risk factors for CSME. Moreover, this nomogram may be a convenient tool for non-ophthalmic specialists to rapidly recognize CSME in patients and to transfer them to ophthalmologists for early diagnosis and treatment.
摘要：
目的：本研究的目的是使用系统变量构建并验证糖尿病（DM）患者临床显着黄斑水肿（CSME）的风险列线图。
方法：在这项回顾性研究中，招募接受常规糖尿病视网膜病变筛查的DM住院患者，并根据其入院日期分为训练组和验证组。收集了93个人口统计学和系统变量。使用最小绝对收缩和选择运算符从训练集中选择预测变量。选择的变量用于构建CSME预测列线图。进行内部和外部验证。C指数，报告校准曲线和判定曲线分析(DCA)。
结果：共有349例患者被分为训练组（240，68.77%）和验证组（109，31.23%）。糖尿病周围神经病变（DPN）症状的存在，尿酸,仅使用或不使用胰岛素进行治疗，胰岛素剂量，列线图选择了尿蛋白分级和病程.预测列线图的C指数在训练集中分别为0.896、0.878和0.837,内部验证和外部验证，分别。列线图的校准曲线显示出预测结果和实际结果之间的良好一致性。DCA证明列线图在临床上是有用的。
结论：开发了一个使用系统变量预测CSME的性能良好的列线图。提示DPN症状和肾功能可能是CSME的重要危险因素。此外，此列线图可能是非眼科专家快速识别CSME患者并将其转交给眼科医生进行早期诊断和治疗的便捷工具.
方法：在这项回顾性研究中，招募接受常规糖尿病视网膜病变筛查的DM住院患者，并根据其入院日期分为训练组和验证组。收集了93个人口统计学和系统变量。使用最小绝对收缩和选择运算符从训练集中选择预测变量。选择的变量用于构建CSME预测列线图。进行内部和外部验证。C指数，报告校准曲线和判定曲线分析(DCA)。
结果：共有349例患者被分为训练组（240，68.77%）和验证组（109，31.23%）。糖尿病周围神经病变（DPN）症状的存在，尿酸,仅使用或不使用胰岛素进行治疗，胰岛素剂量，列线图选择了尿蛋白分级和病程.预测列线图的C指数在训练集中分别为0.896、0.878和0.837,内部验证和外部验证，分别。列线图的校准曲线显示出预测结果和实际结果之间的良好一致性。DCA证明列线图在临床上是有用的。
结论：开发了一个使用系统变量预测CSME的性能良好的列线图。提示DPN症状和肾功能可能是CSME的重要危险因素。此外，此列线图可能是非眼科专家快速识别CSME患者并将其转交给眼科医生进行早期诊断和治疗的便捷工具.
