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
R743.33; 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(...
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| Published in | 中国全科医学 Vol. 27; no. 26; pp. 3232 - 3239 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
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%637000 四川省南充市,川北医学院附属南充市中心医院肾内科%628000 四川省广元市中心医院肾内科%629000 四川省遂宁市中心医院肾内科
15.09.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-9572 |
| DOI | 10.12114/j.issn.1007-9572.2023.0762 |
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| Abstract | R743.33; 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 |
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| AbstractList | R743.33; 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 |
| Abstract_FL | 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. |
| Author | 季一飞 陈晓霞 唐文武 余艺雯 杨小华 李秋伶 邓欢 |
| AuthorAffiliation | 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%637000 四川省南充市,川北医学院附属南充市中心医院肾内科%628000 四川省广元市中心医院肾内科%629000 四川省遂宁市中心医院肾内科 |
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| Author_FL | TANG Wenwu YU Yiwen DENG Huan YANG Xiaohua CHEN Xiaoxia JI Yifei LI Qiuling |
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| DocumentTitle_FL | Establishment and Verification of Risk Prediction Model for Silent Brain Infarction in Maintenance Hemodialysis Patients:a Multicenter Study |
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| Keywords | Multi-center Maintenance hemodialysis 危险因素 Prediction model 多中心 维持性血液透析 预测模型 无症状脑梗死 Silent brain infarction Risk factors |
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| Snippet | R743.33; 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... |
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| Title | 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 |
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