Objective To design fast cluster indicators (FCIs) and compare it with the acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) scoring to judge the value and prognosis of sepsis patients.Methods Based on 33 cases of severe infected patients as samples for this study from September 2013 to July 2014 in Intensive Care Unit (ICU) in the Second People's Hospital of YiYang,and with the Procalcitonin (PCT),lactic acid (LAC),D-Dimer (DD) and base excess (BE) as the indicators,as well as drawing on the APACHE Ⅱ scoring system ideological,as is to design the FCIs to evaluate the prognosis of patients with severe infection.Results After treatment,the survivals were 17 cases,16 patients were died out of 33,and the mortality rate was 48.5％.The sensitivity and specificity of fast cluster indicator that predict patient mortality rates were 75％ and 71％,respectively.The area under the receiver operating characteristic curve (ROC) of FCIs assessing the prognosis of patients with severe infections was 0.895,which was higher than the value 0.721 of APACHE Ⅱ scoring.Conclusions FCIs scoring has a certain significance and guiding role in clinical practice.

OBJECTIVE: To establish a clinically applicable model of rapid identification of adverse drug reaction program (RiADP) for risk management and decision-making of clinical drug use. METHODS: Based on the theory of disproportion analysis, frequency method and Bayes method, a clinically applicable RiADP model in R language background was established, and the parameters of the model were interpreted by MedDRA coding. Based on the actual monitoring data of FDA, the model was validated by the assessing hepatotoxicity of lopinavir/ritonavir (LPV/r). RESULTS: The established RiADP model included four parameters: standard value of adverse drug reaction signal information, empirical Bayesian geometric mean value, ratio of reporting ratio and number of adverse drug reaction cases. Through the application of R language parameter package "phViD", the model parameters could be output quickly. After being encoded by MedDRA, it was converted into clinical terms to form a clinical interpretation report of adverse drug reactions. In addition, the evaluation results of LPV/r hepatotoxicity by the model were matched with the results reported in latest literature, which also proved the reliability of the model results. CONCLUSIONS In this study, a rapid identification method of adverse reactions based on post marketing drug monitoring data was established in R language environment, which is capable of sending rapid warning of adverse reactions of target drugs in public health emergencies, and providing intuitive evidence for risk management and decision-making of clinical drugs.
Databases, Pharmaceutical ; Decision Making, Computer-Assisted ; Drug Monitoring ; Drug-Related Side Effects and Adverse Reactions ; HIV Protease Inhibitors ; adverse effects ; pharmacology ; Humans ; Liver ; drug effects ; Lopinavir ; adverse effects ; toxicity ; Models, Statistical ; Reproducibility of Results ; Software ; standards