Development and validation of a nomogram for predicting the risk of poor prognosis in patients with cerebral infarction.
Development and validation of a nomogram for predicting the risk of poor prognosis in patients with cerebral infarction.
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DOI：
10.1016/j.heliyon.2023.e23754
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发表时间：
2024-01-15
期刊：
影响因子：
4
通讯作者：
Xia Chen
中科院分区：
文献类型：
--
作者：
Zhenfeng Chen;Lixiang Zhang;Rui Li;Haiying Hu;Qiongdan Hu;Xia Chen
目的探讨脑梗死患者预后不良的相关因素，并建立和验证基于这些因素的个性化预测模型。回顾性分析了2020年4月至2022年3月在中国安徽省某三级甲等医院神经内科确诊的857例脑梗死患者的临床和随访资料。根据出院后1年随访资料及改良兰金量表（mRS）评分，将患者分为预后良好组（mRS ≤2分者793例）和预后不良组（mRS >2分者64例）。采用多因素Logistic回归分析确定独立的危险因素，建立诺模图模型。使用受试者工作特征曲线下面积（ROC，AUC）评价模型的预测性能，并使用校准曲线评价诺模图的校准。两组患者出院后使用双胍类降糖药、胰岛素、收缩压、运动状态、饮酒、吸烟、年龄、性别等8个变量的分布差异有统计学意义（P < 0.05）。多因素Logistic回归分析显示，性别、出院后吸烟、饮酒、缺乏运动、口服双胍类降糖药是脑梗死患者预后不良的独立危险因素（P < 0.05）。基于这5个预测因素构建的个性化不良预后诺模图具有良好的判别能力和预测稳定性，内部验证前后AUC分别为0.768（95%CI：0.712-0.825）和0.775（95%CI：0.725-0.836）。校准曲线证实了诺模图的准确性和一致性（P = 0.956）。女性、吸烟、饮酒、缺乏运动和出院后使用双胍类降糖药是脑梗死患者预后不良的独立危险因素。所构建的诺模图对出院后的预后有较好的预测效果，可为临床决策提供帮助。
To identify factors related to poor prognosis in patients with cerebral infarction (CI) and to construct and validate a personalized prediction model based on these factors. A retrospective analysis was conducted on the clinical and follow-up data of 857 patients with CI who were diagnosed in the neurology department of a tertiary A hospital in Anhui Province, China from April 2020 to March 2022. Based on follow-up data and the Modified Rankin Scale (mRS) score one year after discharge, patients were divided into a good prognosis group (793 cases, mRS ≤2) and a poor prognosis group (64 cases, mRS >2). Multivariate logistic regression analysis was used to identify independent risk factors, which were then used to establish a nomogram model. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (ROC, AUC), and the calibration curve was used to evaluate the calibration of the nomogram. There was a statistically significant difference in the distribution of eight variables between the groups, including post-discharge use of biguanide hypoglycemic drugs, insulin, systolic blood pressure, exercise status, alcohol consumption, smoking status, age, and gender (P < 0.05). Multivariate logistic regression analysis suggested that gender, smoking after discharge, alcohol consumption, lack of exercise, and oral administration of biguanide hypoglycemic drugs are independent risk factors for poor prognosis in patients with CI (P < 0.05). The personalized poor prognosis nomogram constructed based on these five predictive factors showed good discriminative ability and predictive stability, with AUCs of 0.768 (95 % CI: 0.712–0.825) and 0.775 (95 % CI: 0.725–0.836) before and after internal validation, respectively. The calibration curve confirmed the accuracy and consistency of the nomogram (P = 0.956). Female gender, smoking, alcohol consumption, lack of exercise, and post-discharge use of biguanide hypoglycemic drugs are independent risk factors for poor prognosis in patients with CI. The constructed nomogram shows good predictive efficiency for post-discharge prognosis and can help in clinical decision-making.