PDF(Pubmed)
Abstract：
UNASSIGNED: A considerable proportion of hypertensive patients may experience lacunar infarction. Therefore, early identification of the risk for lacunar infarction in hypertensive patients is particularly important. This study aimed to develop and validate a concise nomogram for predicting lacunar infarction in hypertensive patients.
UNASSIGNED: Retrospectively analyzed the clinical data of 314 patients with accurate history of hypertension in the Second Affiliated Hospital of Wannan Medical College from January 2021 to December 2022. All the patients were randomly assigned to the training set (n=220) and the validation set (n=94) with 7:3. The diagnosis of lacunar infarction in patients was confirmed using cranial CT or MRI. The independent risk factors of lacunar infarction were determined by Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis. The nomogram was built based on the independent risk factors. The nomogram\'s discrimination, calibration, and clinical usefulness were evaluated by receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA) analysis, respectively.
UNASSIGNED: The incidence of lacunar infarction was 34.50% and 33.00% in the training and validation sets, respectively. Five independent predictors were made up of the nomogram, including age (OR=1.142, 95% CI: 1.089-1.198, P<0.001), diabetes mellitus (OR=3.058, 95% CI: 1.396-6.697, P=0.005), atrial fibrillation (OR=3.103, 95% CI: 1.328-7.250, P=0.009), duration of hypertension (OR=1.130, 95% CI: 1.045-1.222, P=0.002), and low-density lipoprotein (OR=2.147, 95% CI: 1.250-3.688, P=0.006). The discrimination with area under the curve (AUC) was 0.847 (95% CI: 0.789-0.905) in the training set and was a slight increase to 0.907 (95% CI: 0.838-0.976) in the validation set. The calibration curve showed high coherence between the predicted and actual probability of lacunar infarction. Moreover, the DCA analysis indicated that the nomogram had a higher overall net benefit of the threshold probability range in both two sets.
UNASSIGNED: Age, diabetes mellitus, atrial fibrillation, duration of hypertension, and low-density lipoprotein were significant predictors of lacunar infarction in hypertensive patients. The nomogram based on the clinical data was constructed, which was a useful visualized tool for clinicians to assess the risk of the lacunar infarction in hypertensive patients.
UNASSIGNED: Retrospectively analyzed the clinical data of 314 patients with accurate history of hypertension in the Second Affiliated Hospital of Wannan Medical College from January 2021 to December 2022. All the patients were randomly assigned to the training set (n=220) and the validation set (n=94) with 7:3. The diagnosis of lacunar infarction in patients was confirmed using cranial CT or MRI. The independent risk factors of lacunar infarction were determined by Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis. The nomogram was built based on the independent risk factors. The nomogram\'s discrimination, calibration, and clinical usefulness were evaluated by receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA) analysis, respectively.
UNASSIGNED: The incidence of lacunar infarction was 34.50% and 33.00% in the training and validation sets, respectively. Five independent predictors were made up of the nomogram, including age (OR=1.142, 95% CI: 1.089-1.198, P<0.001), diabetes mellitus (OR=3.058, 95% CI: 1.396-6.697, P=0.005), atrial fibrillation (OR=3.103, 95% CI: 1.328-7.250, P=0.009), duration of hypertension (OR=1.130, 95% CI: 1.045-1.222, P=0.002), and low-density lipoprotein (OR=2.147, 95% CI: 1.250-3.688, P=0.006). The discrimination with area under the curve (AUC) was 0.847 (95% CI: 0.789-0.905) in the training set and was a slight increase to 0.907 (95% CI: 0.838-0.976) in the validation set. The calibration curve showed high coherence between the predicted and actual probability of lacunar infarction. Moreover, the DCA analysis indicated that the nomogram had a higher overall net benefit of the threshold probability range in both two sets.
UNASSIGNED: Age, diabetes mellitus, atrial fibrillation, duration of hypertension, and low-density lipoprotein were significant predictors of lacunar infarction in hypertensive patients. The nomogram based on the clinical data was constructed, which was a useful visualized tool for clinicians to assess the risk of the lacunar infarction in hypertensive patients.
摘要：
■相当比例的高血压患者可能经历腔隙性脑梗死。因此,早期识别高血压患者腔隙性脑梗死的风险尤为重要。本研究旨在开发和验证预测高血压患者腔隙性脑梗死的简明列线图。
■回顾性分析皖南医学院第二附属医院2021年1月至2022年12月314例准确高血压病史患者的临床资料。将所有患者随机分配到7:3的训练集(n=220)和验证集(n=94)。使用头颅CT或MRI证实患者腔隙性脑梗死的诊断。采用最小绝对收缩和选择算子(LASSO)回归和多因素logistic回归分析确定腔隙性脑梗死的独立危险因素。列线图是根据独立的危险因素建立的。列线图的歧视，校准,通过受试者工作特征（ROC）曲线评估临床有用性，校正曲线,和决策曲线分析(DCA)分析，分别。
■在训练集和验证集中，腔隙性脑梗死的发生率分别为34.50%和33.00%，分别。五个独立的预测因子由列线图组成，包括年龄（OR=1.142，95%CI：1.089-1.198，P<0.001），糖尿病(OR=3.058,95%CI:1.396-6.697,P=0.005),心房颤动(OR=3.103,95%CI:1.328-7.250,P=0.009),高血压病程(OR=1.130,95%CI:1.045-1.222,P=0.002),低密度脂蛋白(OR=2.147,95%CI:1.250~3.688,P=0.006)。在训练集中，曲线下面积（AUC）的判别为0.847（95％CI：0.789-0.905），在验证集中略有增加至0.907（95％CI：0.838-0.976）。校准曲线显示腔隙性脑梗死的预测概率和实际概率之间的高度一致性。此外，DCA分析显示,在两组中,列线图的阈值概率范围的总体净获益均较高.
■年龄，糖尿病,心房颤动,高血压的持续时间，低密度脂蛋白是高血压患者腔隙性脑梗死的重要预测因子。根据临床数据构建列线图，这是临床医生评估高血压患者腔隙性脑梗死风险的有用可视化工具.
■回顾性分析皖南医学院第二附属医院2021年1月至2022年12月314例准确高血压病史患者的临床资料。将所有患者随机分配到7:3的训练集(n=220)和验证集(n=94)。使用头颅CT或MRI证实患者腔隙性脑梗死的诊断。采用最小绝对收缩和选择算子(LASSO)回归和多因素logistic回归分析确定腔隙性脑梗死的独立危险因素。列线图是根据独立的危险因素建立的。列线图的歧视，校准,通过受试者工作特征（ROC）曲线评估临床有用性，校正曲线,和决策曲线分析(DCA)分析，分别。
■在训练集和验证集中，腔隙性脑梗死的发生率分别为34.50%和33.00%，分别。五个独立的预测因子由列线图组成，包括年龄（OR=1.142，95%CI：1.089-1.198，P<0.001），糖尿病(OR=3.058,95%CI:1.396-6.697,P=0.005),心房颤动(OR=3.103,95%CI:1.328-7.250,P=0.009),高血压病程(OR=1.130,95%CI:1.045-1.222,P=0.002),低密度脂蛋白(OR=2.147,95%CI:1.250~3.688,P=0.006)。在训练集中，曲线下面积（AUC）的判别为0.847（95％CI：0.789-0.905），在验证集中略有增加至0.907（95％CI：0.838-0.976）。校准曲线显示腔隙性脑梗死的预测概率和实际概率之间的高度一致性。此外，DCA分析显示,在两组中,列线图的阈值概率范围的总体净获益均较高.
■年龄，糖尿病,心房颤动,高血压的持续时间，低密度脂蛋白是高血压患者腔隙性脑梗死的重要预测因子。根据临床数据构建列线图，这是临床医生评估高血压患者腔隙性脑梗死风险的有用可视化工具.
