通讯作者: 叶永强, n_taihu@163.com

DOI：10.12201/bmr.202501.00008

Construction and validation of nomogram model for prolonged length of stay in patients with acute cerebral infarction based on total cerebral small vessel disease burden scores

• 摘要：目的 基于脑小血管病(cerebral small vessel disease,CSVD)影像学总负荷构建急性脑梗死(acute cerebral infarction,ACI)患者住院时间延长列线图模型,并对其进行验证。方法 选取2021年1月至2023年12月湖州学院附属南太湖医院神经内科收治的462例ACI患者作为研究对象,根据患者住院时间将其分为住院时间延长组(≥14d,104例)和住院时间正常组(＜14d,358例)。按照7:3原则,将患者分为训练组323例和验证组139例,收集患者临床资料,基于训练组数据,采用Lasso-logistic回归分析ACI患者住院时间延长危险因素,构建列线图模型,基于验证组数据对模型进行验证,运用受试者工作特征曲线(receiver operating characteristic curves,ROC曲线)、校准曲线、决策曲线评价模型预测效能。结果 基于训练组数据,Lasso回归筛选出4个非零系数指标,分别为基线美国国立卫生研究院卒中量表(National Institutes of Health stroke scale,NIHSS)评分、年龄校正查尔森合并症指数(age-adjustedSCharlsonScomorbidity index,aCCI)评分、中性粒细胞与淋巴细胞比值(neutrophil-to-lymphocyte ratio,NLR)、CSVD总负荷评分。多因素Logistic回归分析显示,基线NIHSS评分、aCCI评分、NLR、CSVD总负荷评分均是ACI患者住院时间延长的独立危险因素(P＜0.05)。基于上述4项指标构建列线图模型,结果显示,该模型预测训练组、验证组患者住院时间延长的ROC曲线下面积为0.812(95%CI: 0.756～0.868)、0.820(95%CI: 0.730～0.909)；校准曲线显示,该模型在两组间预测概率与实际概率一致性较好(χ2值分别为9.129、3.728,P值分别为0.332、0.881)；决策曲线表明,该模型在两组间均具有较广的临床净收益。结论 基于CSVD总负荷评分构建的ACI患者住院时间延长列线图模型具有较好的预测效能,可作为筛查ACI患者住院时间延长评估工具。

  关键词： 急性脑梗死; 脑小血管病; 总负荷评分; 住院时间延长; 列线图

   

  Abstract: Objective Construct a nomogram model for prolonged length of stay in patients with acute cerebral infarction based on total cerebral small vessel disease burden scores, and validate its effectiveness. Methods A total of 462 ACI patients admitted to the department of neurology of South Taihu Hospital Affiliated To Huzhou College from January 2021 to December 2023 were selected as the study subjects. According to the length of stay, they were divided into the prolonged length of stay group (≥14 d, 104cases) and the normal length of stay group (<14 d, 358 cases). According to the ratio of 7:3, patients were divided into training group of 323 cases and validation group of 139 cases. Clinical data of patients were collected. Based on the training group data, Lasso logistic regression was used to analyze the risk factors for prolonged length of stay in ACI patients, construct a nomogram model and validate the model using validation data. In addition, Receiver operating characteristic curves (ROC curves), calibration curves and decision curves were used to evaluate the predictive performance of the model.

  Key words: Acute cerebral infarction; Cerebral small vessel disease; Total burden score; Prolonged length of stay; Nomogram

  提交时间：2025-01-03

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• 序号	提交日期	编号	操作
  1	2024-12-22	bmr.202501.00008V1	下载

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