目的分析重症监护病房(intensive care unit,ICU)重症患者发生谵妄的危险因素,建立预测模型并进行验证。方法回顾性分析2018年12月至2019年12月杭州市余杭区第一人民医院收治的ICU重症患者100例,根据是否发生谵妄将其分为谵妄组(n=21)...目的分析重症监护病房(intensive care unit,ICU)重症患者发生谵妄的危险因素,建立预测模型并进行验证。方法回顾性分析2018年12月至2019年12月杭州市余杭区第一人民医院收治的ICU重症患者100例,根据是否发生谵妄将其分为谵妄组(n=21)和非谵妄组(n=79),采用单因素和多因素Logistic回归分析探讨ICU重症患者发生谵妄的危险因素,建立预测模型后,前瞻性收集2020年1~6月的40例ICU重症患者(验证组)进行验证,采用H-L卡方检验模型拟合度,应用受试者操作特征曲线(receiver operator characteristic curve,ROC曲线)反映预测模型的区分能力。结果多因素Logistic回归分析发现,年龄、高血压病史、有创机械通气、住院时间、急性生理学和慢性健康状况评价Ⅱ(acute physiology and chronic health evaluationⅡ,APACHEⅡ)评分、酸碱失衡均为导致ICU重症患者发生谵妄的独立危险因素(P<0.05);构建的模型预测谵妄的ROC曲线下面积为0.891(P<0.05),预测模型阈值为19.25时敏感度、特异性最佳,分别为83.6%、82.9%;H-L卡方检验结果显示,验证组预测谵妄发生率与实际谵妄发生率比较差异无统计学意义(22.50%vs.20.00%,χ^(2)=0.075,P=0.785),拟合度较好,模型验证曲线下面积为0.838,最佳敏感度、特异性、准确度分别为81.0%、78.2%、81.4%。结论年龄、高血压病史、有创机械通气、住院时间、APACHEⅡ评分、酸碱失衡均为导致ICU重症患者发生谵妄的独立危险因素,建立的预测模型拟合度较好,有利于筛选谵妄高危人群。展开更多
In this paper,the approximate Bayesian computation combines the particle swarm optimization and se-quential Monte Carlo methods,which identify the parameters of the Mathieu-van der Pol-Duffing chaotic energy harvester...In this paper,the approximate Bayesian computation combines the particle swarm optimization and se-quential Monte Carlo methods,which identify the parameters of the Mathieu-van der Pol-Duffing chaotic energy harvester system.Then the proposed method is applied to estimate the coefficients of the chaotic model and the response output paths of the identified coefficients compared with the observed,which verifies the effectiveness of the proposed method.Finally,a partial response sample of the regular and chaotic responses,determined by the maximum Lyapunov exponent,is applied to detect whether chaotic motion occurs in them by a 0-1 test.This paper can provide a reference for data-based parameter iden-tification and chaotic prediction of chaotic vibration energy harvester systems.展开更多
基金supported by the National Key Research and Development Program(2019YFE0119900)US National Science Foundation(CBET1930866,CMMI2032464)+1 种基金National Natural Science Foundation of China(52106220)Natural Science Foundation of Shandong Province(ZR2020ME183).
文摘目的分析重症监护病房(intensive care unit,ICU)重症患者发生谵妄的危险因素,建立预测模型并进行验证。方法回顾性分析2018年12月至2019年12月杭州市余杭区第一人民医院收治的ICU重症患者100例,根据是否发生谵妄将其分为谵妄组(n=21)和非谵妄组(n=79),采用单因素和多因素Logistic回归分析探讨ICU重症患者发生谵妄的危险因素,建立预测模型后,前瞻性收集2020年1~6月的40例ICU重症患者(验证组)进行验证,采用H-L卡方检验模型拟合度,应用受试者操作特征曲线(receiver operator characteristic curve,ROC曲线)反映预测模型的区分能力。结果多因素Logistic回归分析发现,年龄、高血压病史、有创机械通气、住院时间、急性生理学和慢性健康状况评价Ⅱ(acute physiology and chronic health evaluationⅡ,APACHEⅡ)评分、酸碱失衡均为导致ICU重症患者发生谵妄的独立危险因素(P<0.05);构建的模型预测谵妄的ROC曲线下面积为0.891(P<0.05),预测模型阈值为19.25时敏感度、特异性最佳,分别为83.6%、82.9%;H-L卡方检验结果显示,验证组预测谵妄发生率与实际谵妄发生率比较差异无统计学意义(22.50%vs.20.00%,χ^(2)=0.075,P=0.785),拟合度较好,模型验证曲线下面积为0.838,最佳敏感度、特异性、准确度分别为81.0%、78.2%、81.4%。结论年龄、高血压病史、有创机械通气、住院时间、APACHEⅡ评分、酸碱失衡均为导致ICU重症患者发生谵妄的独立危险因素,建立的预测模型拟合度较好,有利于筛选谵妄高危人群。
基金This work is supported by the National Nature Science Founda-tion of China(Nos.11972019 and 12102237).
文摘In this paper,the approximate Bayesian computation combines the particle swarm optimization and se-quential Monte Carlo methods,which identify the parameters of the Mathieu-van der Pol-Duffing chaotic energy harvester system.Then the proposed method is applied to estimate the coefficients of the chaotic model and the response output paths of the identified coefficients compared with the observed,which verifies the effectiveness of the proposed method.Finally,a partial response sample of the regular and chaotic responses,determined by the maximum Lyapunov exponent,is applied to detect whether chaotic motion occurs in them by a 0-1 test.This paper can provide a reference for data-based parameter iden-tification and chaotic prediction of chaotic vibration energy harvester systems.
