目的探讨脓毒症患者发生脓毒症相关脑病(SAE)的危险因素,建立简便、易用的预测模型并进行验证。方法回顾性分析徐州医科大学附属医院2017年1月至2021年12月入住重症监护病房(ICU)脓毒症患者的临床资料,根据纳入排除标准,确定最终入选病...目的探讨脓毒症患者发生脓毒症相关脑病(SAE)的危险因素,建立简便、易用的预测模型并进行验证。方法回顾性分析徐州医科大学附属医院2017年1月至2021年12月入住重症监护病房(ICU)脓毒症患者的临床资料,根据纳入排除标准,确定最终入选病例,将2017年1月至2019年12月收集的病例作为训练队列组(n=640),将2020年1月至2021年12月收集的病例作为验证队列组(n=300)。将训练队列组患者资料进行Logistic回归分析,确定SAE发生的危险因素,建立回归方程,并可视化为列线图。验证队列组对建立的回归方程进行验证,通过绘制受试者工作特征(receiver operating characteristic,ROC)曲线及计算ROC曲线下面积(area under the curve,AUC)评价模型的区分度,通过Hosmer-Lemeshow检验和校准图评价模型的校准度。结果本研究共纳入940例患者,单因素及多因素Logistic回归结果表明,高龄、使用升压药、高中枢神经特异蛋白(S100β)水平、低脉搏血氧饱和度(SpO_(2))和低蛋白血症5个因素为SAE发病的独立危险因素(P<0.05),纳入预测模型,该预测模型的AUC在训练和验证队列组分别为0.810(95%CI 0.763~0.857)和0.813(95%CI 0.740~0.885),模型的校准曲线在训练和验证队列组均与平面直角坐标系中45°的直线重合度较高,提示该模型的表现良好。结论本研究建立的预测模型可以科学、有效地对SAE的发生进行预测,操作简便、快速,具有重要的临床价值。展开更多
This research aims to develop a methodology for applying the geostatistical method to generate a groutability classification for granular soils.To ensure the precision of the suggested technique,a total of 103 data sa...This research aims to develop a methodology for applying the geostatistical method to generate a groutability classification for granular soils.To ensure the precision of the suggested technique,a total of 103 data samples were used.Predicting the groutability of granular soils has always been difficult because of many soil characteristics.As a result,a new two-dimensional graph,the groutability classification of granular soil(GCS)chart,was developed.GCS establishment was based on data analysis of the grain size of soil and cement-based grouts(N1 and N2),relative density(Dr)and fines content of the soil(FC),water/cement ratio of grout mixture(w/c),and grouting pressure(P),all of which have a direct impact on the groutability of soil media.The geostatistical method was used to develop and compile the GCS graph based on the aforementioned parameters with the use of coefficient S,which is a coefficient of the scoring set of parameters including P,w/c,Dr,and FC.The validation process was carried out hierarchically,with an additional set of 30 data.The proposed method has a prediction accuracy of roughly 96.7%,demonstrating a helpful tool.The proposed approach can be easily implemented in practical engineering situations because it has a comparable syntax to commonly used formulae.It should be noted that the proposed formula was only tested using the data samples collected,and the applicability of the produced procedure to other situations requires more examination.展开更多
文摘目的探讨脓毒症患者发生脓毒症相关脑病(SAE)的危险因素,建立简便、易用的预测模型并进行验证。方法回顾性分析徐州医科大学附属医院2017年1月至2021年12月入住重症监护病房(ICU)脓毒症患者的临床资料,根据纳入排除标准,确定最终入选病例,将2017年1月至2019年12月收集的病例作为训练队列组(n=640),将2020年1月至2021年12月收集的病例作为验证队列组(n=300)。将训练队列组患者资料进行Logistic回归分析,确定SAE发生的危险因素,建立回归方程,并可视化为列线图。验证队列组对建立的回归方程进行验证,通过绘制受试者工作特征(receiver operating characteristic,ROC)曲线及计算ROC曲线下面积(area under the curve,AUC)评价模型的区分度,通过Hosmer-Lemeshow检验和校准图评价模型的校准度。结果本研究共纳入940例患者,单因素及多因素Logistic回归结果表明,高龄、使用升压药、高中枢神经特异蛋白(S100β)水平、低脉搏血氧饱和度(SpO_(2))和低蛋白血症5个因素为SAE发病的独立危险因素(P<0.05),纳入预测模型,该预测模型的AUC在训练和验证队列组分别为0.810(95%CI 0.763~0.857)和0.813(95%CI 0.740~0.885),模型的校准曲线在训练和验证队列组均与平面直角坐标系中45°的直线重合度较高,提示该模型的表现良好。结论本研究建立的预测模型可以科学、有效地对SAE的发生进行预测,操作简便、快速,具有重要的临床价值。
文摘This research aims to develop a methodology for applying the geostatistical method to generate a groutability classification for granular soils.To ensure the precision of the suggested technique,a total of 103 data samples were used.Predicting the groutability of granular soils has always been difficult because of many soil characteristics.As a result,a new two-dimensional graph,the groutability classification of granular soil(GCS)chart,was developed.GCS establishment was based on data analysis of the grain size of soil and cement-based grouts(N1 and N2),relative density(Dr)and fines content of the soil(FC),water/cement ratio of grout mixture(w/c),and grouting pressure(P),all of which have a direct impact on the groutability of soil media.The geostatistical method was used to develop and compile the GCS graph based on the aforementioned parameters with the use of coefficient S,which is a coefficient of the scoring set of parameters including P,w/c,Dr,and FC.The validation process was carried out hierarchically,with an additional set of 30 data.The proposed method has a prediction accuracy of roughly 96.7%,demonstrating a helpful tool.The proposed approach can be easily implemented in practical engineering situations because it has a comparable syntax to commonly used formulae.It should be noted that the proposed formula was only tested using the data samples collected,and the applicability of the produced procedure to other situations requires more examination.
