目的探讨外周灌注指数(perfusion index,PI)和氧负荷试验(oxygen challenge test,OCT)对脓毒症休克患者复苏后死亡率的预测作用。方法选取2018—2021年住院接受PICCO监测的脓毒症休克患者46例为观察组,无感染病情稳定患者20例为对照组。...目的探讨外周灌注指数(perfusion index,PI)和氧负荷试验(oxygen challenge test,OCT)对脓毒症休克患者复苏后死亡率的预测作用。方法选取2018—2021年住院接受PICCO监测的脓毒症休克患者46例为观察组,无感染病情稳定患者20例为对照组。在OCT期间测定10min OCT、氧负荷指数(OCI);在PICCO导管插入后24 h测量比较患者外周灌注相关变量,通过ROC曲线分析PI,OCT及相关变量对脓毒症休克患者复苏后死亡率的预测能力。结果观察组APACHE II,SOFA,Ramsay评分,MAP,PaO_(2)(基础FiO_(2)),PtcO_(2),PaO_(2)(FiO_(2)1.0),10 min OCT和PI均低于对照组,差异有统计学意义(P<0.05),观察组死亡患者Lac显著高于生存患者(P<0.001),PI、10 min OCT和OCI的预测准确性明显优于PV-aCO_(2)和ScvO_(2),与Lac水平相似,OCI,10 min OCT和PI是死亡率的重要预测因子。结论低外周灌注和高代谢变量与脓毒症休克患者复苏后死亡率增加有关。当PI≤0.2,10 min OCT≤66 mm Hg,OCI≤0.55时,脓毒症休克患者复苏后死亡率较高。展开更多
We discuss the basic intrusion detection techniques, and focus on how to apply association rules to intrusion detection. Begin with analyzing some close relations between user’s behaviors, we discuss the mining algor...We discuss the basic intrusion detection techniques, and focus on how to apply association rules to intrusion detection. Begin with analyzing some close relations between user’s behaviors, we discuss the mining algorithm of association rules and apply to detect anomaly in IDS. Moreover, according to the characteristic of intrusion detection, we optimize the mining algorithm of association rules, and use fuzzy logic to improve the system performance.展开更多
文摘目的探讨外周灌注指数(perfusion index,PI)和氧负荷试验(oxygen challenge test,OCT)对脓毒症休克患者复苏后死亡率的预测作用。方法选取2018—2021年住院接受PICCO监测的脓毒症休克患者46例为观察组,无感染病情稳定患者20例为对照组。在OCT期间测定10min OCT、氧负荷指数(OCI);在PICCO导管插入后24 h测量比较患者外周灌注相关变量,通过ROC曲线分析PI,OCT及相关变量对脓毒症休克患者复苏后死亡率的预测能力。结果观察组APACHE II,SOFA,Ramsay评分,MAP,PaO_(2)(基础FiO_(2)),PtcO_(2),PaO_(2)(FiO_(2)1.0),10 min OCT和PI均低于对照组,差异有统计学意义(P<0.05),观察组死亡患者Lac显著高于生存患者(P<0.001),PI、10 min OCT和OCI的预测准确性明显优于PV-aCO_(2)和ScvO_(2),与Lac水平相似,OCI,10 min OCT和PI是死亡率的重要预测因子。结论低外周灌注和高代谢变量与脓毒症休克患者复苏后死亡率增加有关。当PI≤0.2,10 min OCT≤66 mm Hg,OCI≤0.55时,脓毒症休克患者复苏后死亡率较高。
文摘We discuss the basic intrusion detection techniques, and focus on how to apply association rules to intrusion detection. Begin with analyzing some close relations between user’s behaviors, we discuss the mining algorithm of association rules and apply to detect anomaly in IDS. Moreover, according to the characteristic of intrusion detection, we optimize the mining algorithm of association rules, and use fuzzy logic to improve the system performance.
