急性缺血性卒中患者住院时间延长风险网络计算器的构建  

Construction of a web-based calculator of the risk of prolonged length of stay in patients with acute ischemic stroke

作　　者：彤小聪 彤萌 任应国[3] Rong Xiaocong;Rong Meng;Ren Yingguo(Special Needs Ward of Neurology Department,Nanyang Central Hospital,Nanyang 473000,China;Department of Hematology,Nanyang Central Hospital,Nanyang 473000,China;Rehabilitation Ward of Neurology Department,Nanyang Central Hospital,Nanyang 473000,China)

机构地区：[1]南阳市中心医院神经内科特需病区,南阳473000 [2]南阳市中心医院血液内科,南阳473000 [3]南阳市中心医院神经内科康复病区,南阳473000

出　　处：《中国实用护理杂志》2025年第13期978-986,共9页Chinese Journal of Practical Nursing

基　　金：南阳市科技发展计划(KJGG099)。

摘　　要：目的基于可解释机器学习模型构建网络计算器预测急性缺血性卒中(AIS)患者住院时间延长风险,为患者制订个性化干预方案提供工具。方法采用回顾性分析的方法,单纯随机抽样选取南阳市中心医院2022年7月至2024年7月收治的537例AIS患者。将住院时间超过中位住院时间定义为住院时间延长。通过Boruta算法筛选住院时间延长风险特征变量。以3:2比例将537例患者按照随机数字表法分为训练集(322例)和测试集(215例)构建和训练9种机器学习模型并进行10倍交叉验证。采用受试者工作特征曲线(ROC)分析并计算曲线下面积(AUC),评估最佳预测性能模型。使用校正曲线、临床影响曲线和决策曲线分析评估模型预测准确性及临床实用价值。使用Shapley加法解释(SHAP)条形图、摘要图、依赖图和力图附加解释和可视化机器学习模型。使用相应R包构建预测AIS患者住院时间延长风险的网络计算器。结果537例AIS患者中,男312例,女225例,年龄(58.40±9.00)岁,住院时间延长发生率为43.0%(231/537)。Boruta算法筛选出10个特征变量。ROC曲线分析显示9种机器学习模型中xgboost包-极限梯度提升(XGBoost)模型的10次随机抽样AUC均最高。在训练集和测试集中,校正曲线及临床影响曲线分析显示C指数分别为0.815和0.816,XGBo0st模型预测结果与实际观察结果间一致性较高;决策曲线分析显示,当风险值分别>0.18和>0.22时临床净收益>0,模型在实际临床决策中应用价值较高。SHAP条形图显示重要性排序为肺炎、尿路感染、年龄、肌红蛋白、三酰甘油、神经元特异性烯醇化酶(NSE)、血红蛋白、总胆固醇、同型半胱氨酸(HCY)和美国国立卫生研究院卒中量表(NIHSS)评分。SHAP摘要图可视化10个特征变量贡献度,呈“两端分布”现象。SHAP依赖图显示10个变量观测值与SHAP值间依赖关系,其中肺炎患者趋势最为显著。SHAP力图为单个�Objective eTo construct a network calculator based on interpretable machine learning models to predict the risk of prolonged length of stay in patients with acute ischemic stroke(AIS),and to provide a tool for the development of individualized intervention plans for patients.MethodsAdopting a retrospective analysis method.The 537 patients with AIS admitted to the Nanyang Central Hospital from July 2022 to July 2024 were selected.Length of stay exceeding the median length of stay was defined as length of stay prolongation.Length of stay prolongation risk profile variables were screened by Boruta algorithm.The 537 patients were randomly divided into a training set(322 cases)and a test set(215 cases)in a 3:2 ratio according to the random number table method to construct and train nine machine learning models and perform tenfold cross-validation.The best predictive performance model was assessed using receiver operating characteristic(ROC)curve analysis and calculating the area under the curve(AUC).The predictive accuracy and clinical utility of the model was assessed using calibration curve,clinical impact curve and decision curve analyses.Additional interpretation and visualisation of the machine learning model using Shapley additive explanations(SHAP)bar charts,summary,dependency and force diagrams.A network calculator for predicting the risk of length of stay prolongation in patients with AIS was constructed using the corresponding R package.Results Among 537 AIS patients,there were 312 males and 225 females with an age of(58.40±9.00)years old.The incidence of length of stay prolongation was 43.0%(231/537).Boruta algorithm screened 10 characteristic variables.The results of R0C curve analysis showed that the AUC of the extreme gradient boosting(XGBoost)model was at the highest among the 9 machine learning models in 10 random samples.In the training and test sets,the calibration curve and clinical impact curve analysis showed that the C-index was 0.815 and 0.816,respectively,indicating high consistency between th

关 键 词：卒中 住住院时间延长 Boruta算法 SHAP XGBoost模型 网络计算器 

分 类 号：R743.3[医药卫生—神经病学与精神病学]