PDF(Pubmed)
Abstract：
UNASSIGNED: To develop a nomogram for mild cognitive impairment (MCI) in patients with subjective cognitive decline (SCD) undergoing physical examinations in China.
UNASSIGNED: We enrolled 370 patients undergoing physical examinations at the Medical Center of the First Hospital of Jilin University, Jilin Province, China, from October 2022 to March 2023. Of the participants, 256 were placed in the SCD group, and 74 were placed in the MCI group. The population was randomly divided into a training set and a validation set at a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The performance and clinical utility of the nomogram were determined using Harrell\'s concordance index, calibration curves, and decision curve analysis (DCA).
UNASSIGNED: Cognitive reserve (CR), age, and a family history of hypertension were associated with the occurrence of MCI. The predictive nomogram showed satisfactory performance, with a concordance index of 0.755 (95% CI: 0.681-0.830) in internal verification. The Hosmer-Lemeshow test results suggested that the model exhibited good fit (p = 0.824). In addition, DCA demonstrated that the predictive nomogram had a good clinical net benefit.
UNASSIGNED: We developed a simple nomogram that could help secondary preventive health care workers to identify elderly individuals with SCD at high risk of MCI during physical examinations to enable early intervention.
UNASSIGNED: We enrolled 370 patients undergoing physical examinations at the Medical Center of the First Hospital of Jilin University, Jilin Province, China, from October 2022 to March 2023. Of the participants, 256 were placed in the SCD group, and 74 were placed in the MCI group. The population was randomly divided into a training set and a validation set at a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The performance and clinical utility of the nomogram were determined using Harrell\'s concordance index, calibration curves, and decision curve analysis (DCA).
UNASSIGNED: Cognitive reserve (CR), age, and a family history of hypertension were associated with the occurrence of MCI. The predictive nomogram showed satisfactory performance, with a concordance index of 0.755 (95% CI: 0.681-0.830) in internal verification. The Hosmer-Lemeshow test results suggested that the model exhibited good fit (p = 0.824). In addition, DCA demonstrated that the predictive nomogram had a good clinical net benefit.
UNASSIGNED: We developed a simple nomogram that could help secondary preventive health care workers to identify elderly individuals with SCD at high risk of MCI during physical examinations to enable early intervention.
摘要：
■建立在中国进行体检的主观认知功能减退（SCD）患者的轻度认知障碍（MCI）的列线图。
■我们在吉林大学第一医院医疗中心登记了370名接受体检的患者,吉林省,中国,从2022年10月到2023年3月。在参与者中，256个被放置在SCD组中，MCI组74例。将群体以7:3的比例随机分为训练集和验证集。应用最小绝对收缩和选择算子（LASSO）回归模型来优化模型的特征选择。应用多变量logistic回归分析构建预测模型。使用Harrell的一致性指数确定列线图的性能和临床实用性，校正曲线,和决策曲线分析(DCA)。
■认知储备（CR），年龄,高血压家族史与MCI的发生有关。预测列线图显示出令人满意的性能，内部验证的一致性指数为0.755（95%CI：0.681-0.830）。Hosmer-Lemeshow测试结果表明该模型表现出良好的拟合(p=0.824)。此外,DCA表明，预测列线图具有良好的临床净效益。
我们开发了一个简单的列线图，可以帮助二级预防保健工作者在体检期间识别患有MCI高风险的SCD的老年人，以便进行早期干预。
■我们在吉林大学第一医院医疗中心登记了370名接受体检的患者,吉林省,中国,从2022年10月到2023年3月。在参与者中，256个被放置在SCD组中，MCI组74例。将群体以7:3的比例随机分为训练集和验证集。应用最小绝对收缩和选择算子（LASSO）回归模型来优化模型的特征选择。应用多变量logistic回归分析构建预测模型。使用Harrell的一致性指数确定列线图的性能和临床实用性，校正曲线,和决策曲线分析(DCA)。
■认知储备（CR），年龄,高血压家族史与MCI的发生有关。预测列线图显示出令人满意的性能，内部验证的一致性指数为0.755（95%CI：0.681-0.830）。Hosmer-Lemeshow测试结果表明该模型表现出良好的拟合(p=0.824)。此外,DCA表明，预测列线图具有良好的临床净效益。
我们开发了一个简单的列线图，可以帮助二级预防保健工作者在体检期间识别患有MCI高风险的SCD的老年人，以便进行早期干预。
