目的探讨经皮氧和二氧化碳分压(PtcO_(2)和PtcCO_(2))及呼吸力学参数对呼吸衰竭病人有创通气撤呼吸机的预测价值。方法选取本院2023年10月-2024年9月收治的呼吸衰竭病人185例,按撤呼吸机状况分为成功组(n=150)和失败组(n=35)。分析PtcO_...目的探讨经皮氧和二氧化碳分压(PtcO_(2)和PtcCO_(2))及呼吸力学参数对呼吸衰竭病人有创通气撤呼吸机的预测价值。方法选取本院2023年10月-2024年9月收治的呼吸衰竭病人185例,按撤呼吸机状况分为成功组(n=150)和失败组(n=35)。分析PtcO_(2)和PtcCO_(2)及呼吸力学参数预测呼吸衰竭病人有创通气撤机失败的价值。结果两组病人合并基础疾病、急性生理与慢性健康评分Ⅱ(APACHEⅡ)评分、ICU入住时间、0.1 s气道闭合压(P0.1)、浅快呼吸指数(RSBI)、通气后6 h PtcO_(2)、通气后12 h PtcO_(2)、通气后12 h PtcCO_(2)等比较差异有统计学意义(t=2.784~8.595,χ^(2)=17.433,P<0.05)。合并基础疾病、P0.1、RSBI、通气后6 h PtcO_(2)、通气后12 h PtcO_(2)、通气后12 h PtcCO_(2)是呼吸衰竭病人有创通气撤机失败的独立影响因素(P<0.05)。P0.1、RSBI、通气后6 h PtcO_(2)、通气后12 h PtcO_(2)、通气后12 h PtcCO_(2)预测呼吸衰竭病人有创通气撤机失败的受试者工作特征曲线下面积(AUC)分别为0.746、0.798、0.816、0.830、0.668,五者联合预测的AUC为0.996,灵敏度为97.1%,特异度为100.0%。结论PtcO_(2)和PtcCO_(2)及呼吸力学参数联合对预测呼吸衰竭病人有创通气撤机失败具有较高的价值。展开更多
Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision p...Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision payoff functions hinge on individual covariates and the choices of their friends.However,peer pressure would be misidentified and induce a non-negligible bias when incomplete covariates are involved in the game model.For this reason,we develop a generalized constant peer effects model based on homogeneity structure in dynamic social networks.The new model can effectively avoid bias through homogeneity pursuit and can be applied to a wider range of scenarios.To estimate peer pressure in the model,we first present two algorithms based on the initialize expand merge method and the polynomial-time twostage method to estimate homogeneity parameters.Then we apply the nested pseudo-likelihood method and obtain consistent estimators of peer pressure.Simulation evaluations show that our proposed methodology can achieve desirable and effective results in terms of the community misclassification rate and parameter estimation error.We also illustrate the advantages of our model in the empirical analysis when compared with a benchmark model.展开更多
文摘目的探讨经皮氧和二氧化碳分压(PtcO_(2)和PtcCO_(2))及呼吸力学参数对呼吸衰竭病人有创通气撤呼吸机的预测价值。方法选取本院2023年10月-2024年9月收治的呼吸衰竭病人185例,按撤呼吸机状况分为成功组(n=150)和失败组(n=35)。分析PtcO_(2)和PtcCO_(2)及呼吸力学参数预测呼吸衰竭病人有创通气撤机失败的价值。结果两组病人合并基础疾病、急性生理与慢性健康评分Ⅱ(APACHEⅡ)评分、ICU入住时间、0.1 s气道闭合压(P0.1)、浅快呼吸指数(RSBI)、通气后6 h PtcO_(2)、通气后12 h PtcO_(2)、通气后12 h PtcCO_(2)等比较差异有统计学意义(t=2.784~8.595,χ^(2)=17.433,P<0.05)。合并基础疾病、P0.1、RSBI、通气后6 h PtcO_(2)、通气后12 h PtcO_(2)、通气后12 h PtcCO_(2)是呼吸衰竭病人有创通气撤机失败的独立影响因素(P<0.05)。P0.1、RSBI、通气后6 h PtcO_(2)、通气后12 h PtcO_(2)、通气后12 h PtcCO_(2)预测呼吸衰竭病人有创通气撤机失败的受试者工作特征曲线下面积(AUC)分别为0.746、0.798、0.816、0.830、0.668,五者联合预测的AUC为0.996,灵敏度为97.1%,特异度为100.0%。结论PtcO_(2)和PtcCO_(2)及呼吸力学参数联合对预测呼吸衰竭病人有创通气撤机失败具有较高的价值。
基金supported by the National Nature Science Foundation of China(71771201,72531009,71973001)the USTC Research Funds of the Double First-Class Initiative(FSSF-A-240202).
文摘Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision payoff functions hinge on individual covariates and the choices of their friends.However,peer pressure would be misidentified and induce a non-negligible bias when incomplete covariates are involved in the game model.For this reason,we develop a generalized constant peer effects model based on homogeneity structure in dynamic social networks.The new model can effectively avoid bias through homogeneity pursuit and can be applied to a wider range of scenarios.To estimate peer pressure in the model,we first present two algorithms based on the initialize expand merge method and the polynomial-time twostage method to estimate homogeneity parameters.Then we apply the nested pseudo-likelihood method and obtain consistent estimators of peer pressure.Simulation evaluations show that our proposed methodology can achieve desirable and effective results in terms of the community misclassification rate and parameter estimation error.We also illustrate the advantages of our model in the empirical analysis when compared with a benchmark model.
