中国全科医学 ›› 2024, Vol. 27 ›› Issue (26): 3232-3239.DOI: 10.12114/j.issn.1007-9572.2023.0762

所属专题: 脑健康最新研究合辑

• 论著 • 上一篇    下一篇

维持性血液透析患者发生无症状脑梗死风险预测模型的建立及验证:一项多中心研究

李秋伶1, 唐文武2, 余艺雯1, 邓欢1, 杨小华3, 陈晓霞4, 季一飞1,*()   

  1. 1.637000 四川省南充市,川北医学院附属南充市中心医院神经内科
    2.637000 四川省南充市,川北医学院附属南充市中心医院肾内科
    3.628000 四川省广元市中心医院肾内科
    4.629000 四川省遂宁市中心医院肾内科
  • 收稿日期:2023-11-10 修回日期:2024-03-25 出版日期:2024-09-15 发布日期:2024-06-14
  • 通讯作者: 季一飞

  • 作者贡献:

    李秋伶提出主要研究目标,负责研究的构思、设计和实施,撰写、修订论文;李秋伶、唐文武进行数据的采集与整理,统计学处理,绘制与展示图表;余艺雯、邓欢进行数据的采集与整理;杨小华、陈晓霞进行数据采集;季一飞负责文章的质量控制与审查,提供监督和指导。

  • 基金资助:
    国家自然科学基金面上项目(81870966); 四川省科技厅自然科学基金(2022NSFSC0756)

Establishment and Verification of Risk Prediction Model for Silent Brain Infarction in Maintenance Hemodialysis Patients: a Multicenter Study

LI Qiuling1, TANG Wenwu2, YU Yiwen1, DENG Huan1, YANG Xiaohua3, CHEN Xiaoxia4, JI Yifei1,*()   

  1. 1.Department of Neurology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong Central Hospital, Nanchong 637000, China
    2.Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong Central Hospital, Nanchong 637000, China
    3.Department of Nephrology, Guangyuan Central Hospital, Guangyuan 628000, China
    4.Department of Nephrology, Suining Central Hospital, Suining 629000, China
  • Received:2023-11-10 Revised:2024-03-25 Published:2024-09-15 Online:2024-06-14
  • Contact: JI Yifei

摘要: 背景 维持性血液透析(MHD)患者具有较高无症状脑梗死(SBI)发病率,且是症状性脑梗死和血管性痴呆的临床前阶段。因此非常有必要探讨MHD患者SBI风险,以早期识别并减少不良预后。 目的 探讨MHD患者发生SBI的危险因素,构建预测模型并评价其效能。 方法 纳入2017年1月—2022年10月4个中心(川北医学院附属南充市中心医院、广元市中心医院、遂宁市中心医院、蓬安县人民医院)的486例MHD患者。以MHD患者是否发生SBI为结局事件,分为SBI组(n=102)和非SBI组(n=384),比较两组研究对象的基线特征。按照7∶3的比例将患者随机分为建模集(n=340)和验证集(n=146)。通过LASSO回归和多因素Logistic回归分析确定预测变量,构建MHD患者发生SBI的风险预测模型并绘制列线图;采用受试者工作特征(ROC)曲线下面积、校准曲线和决策曲线分析评估模型的预测性能、准确性和临床应用价值。 结果 建模集70例(20.6%)MHD患者发生SBI,验证集32例(21.9%)患者发生SBI。LASSO回归结合多因素Logistic回归分析结果显示,年龄(OR=1.027,95%CI=1.005~1.050)、饮酒史(OR=4.487,95%CI=2.075~9.706)、BMI(OR=1.082,95%CI=1.011~1.156)、睡眠时间<5 h/d或>9 h/d(OR=6.286,95%CI=3.560~11.282)、慢性病史(慢性阻塞性肺疾病、糖尿病、慢性乙肝)(OR=1.873,95%CI=1.067~3.347)、血清乳酸水平(OR=1.452,95%CI=1.152~1.897)、尿素清除率(URR)(OR=0.922,95%CI=0.875~0.970)和抗血小板药用药史(OR=0.149,95%CI=0.030~0.490)是MHD患者发生SBI的独立影响因素(P<0.05)。构建包含上述8个影响因素的预测模型并绘制列线图。该预测模型在建模集和验证集的ROC曲线下面积分别为0.816(95%CI=0.759~0.873)和0.808(95%CI=0.723~0.893),校准曲线表现出良好的一致性。DCA曲线提示该模型可使患者获得最大临床收益。 结论 基于年龄、饮酒史、BMI、睡眠不足或睡眠过长、慢性病史(慢性阻塞性肺疾病、糖尿病、慢性乙肝)、血清乳酸水平、URR和抗血小板药用药史建立的MHD患者发生SBI风险预测模型有较好的预测效能和临床实用性,有望对MHD患者发生SBI风险进行准确、个性化的评估并实施早期干预以降低发病率。

关键词: 无症状脑梗死, 维持性血液透析, 预测模型, 多中心, 危险因素

Abstract:

Background

Maintenance hemodialysis (MHD) patients have a high incidence of silent brain infarction (SBI) and are in the preclinical stage of symptomatic stroke and vascular dementia. Therefore, there is a great need to explore the risk of SBI in patients with MHD for early detection and reduction of poor prognosis.

Objective

To explore the risk factors for the occurrence of SBI in MHD patients, a predictive model was constructed and its performance was evaluated.

Methods

486 MHD patients from 4 centers (Nanchong Central Hospital Affiliated to North Sichuan Medical College, Guangyuan Central Hospital, Suining Central Hospital, and Pengan County People's Hospital) from January 2017 to October 2022 were included. Patients with MHD were divided into an SBI group (n=102) and a non-SBI group (n=384) using the presence or absence of SBI as the outcome event, and the baseline characteristics of the two study groups were compared. Patients were randomized in a 7∶3 ratio to the modeling set (n=340) and the validation set (n=146). The predictor variables were identified through LASSO regression and multifactorial Logistic regression analyses, and a risk prediction model for the occurrence of SBI in patients with MHD was constructed and presented as a nomographic chart. The predictive performance, accuracy, and clinical utility of the model were evaluated using area under the ROC curve, calibration curve, and decision curve analysis.

Results

In the modeling set, 70 cases (20.6%) of MHD patients experienced SBI, while in the validation set, 32 cases (21.9%) of patients experienced SBI. The results of LASSO regression combined with multifactor logistic regression analysis showed that age (OR=1.027, 95%CI=1.005-1.050), history of alcohol consumption (OR=4.487, 95%CI=2.075-9.706), BMI (OR=1.082, 95%CI=1.011-1.156), insufficient sleep or excessive sleep (OR=6.286, 95%CI=3.560-11.282), history of chronic disease (chronic obstructive pulmonary disease, diabetes, chronic hepatitis B) (OR=1.873, 95%CI=1.067-3.347), serum lactate level (OR=1.452, 95%CI=1.152-1.897), urea reduction ratio (URR) (OR=0.922, 95%CI=0.875-0.970), and history of antiplatelet medication (OR=0.149, 95%CI=0.030-0.490) were independent influences on the occurrence of SBI in MHD patients (P<0.05). A predictive model incorporating the aforementioned 8 influencing factors was constructed, and a nomographic chart was developed. The area under the ROC curve of the predictive model in the modeling set and validation set were 0.816 (95%CI=0.759-0.873) and 0.808 (95%CI=0.723-0.893), respectively, and the calibration curves show good consistency. DCA curve suggested that this model could provide maximum clinical benefit to patients.

Conclusion

A prediction model for the risk of SBI in MHD patients based on age, history of alcohol consumption, BMI, insufficient sleep or excessive sleep, history of chronic disease (chronic obstructive pulmonary disease, diabetes, chronic hepatitis B), serum lactate level, URR, and history of antiplatelet medication demonstrated good predictive performance and clinical utility. It is expected to accurately and individually assess the risk of SBI in MHD patients and implement early interventions to reduce the incidence rate.

Key words: Silent brain infarction, Maintenance hemodialysis, Prediction model, Multi-center, Risk factors

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