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目的建立主成分分析(PCA)模型对我院麻醉药品的用量进行监控,探讨医院麻醉药品监控的新模式。方法提取我院2013年全年和2014年上半年共6个季度14种麻醉药品的用量数据,建立PCA模型。通过构建主成分得分图,结合主成分载荷图,对麻醉药品的用量进行整体评价,筛选变化较显著的品种。当某一季度的统计值异常时,构建变量贡献图分析原因。结果我院急诊麻醉药品的用量较稳定,住院麻醉药品的用量变化较大,其中羟考酮缓释片40 mg的用量增加和芬太尼透皮贴剂8.4 mg的用量减少最为显著。结论本研究证明了PCA算法在麻醉药品监控中的有效性,为医院麻醉药品监控提供新的方法。
Objective To establish a principal component analysis (PCA) model to monitor the dosage of narcotic drugs in our hospital and to explore a new mode of monitoring narcotic drugs in hospitals. Methods The dosage data of 14 kinds of narcotic drugs in our hospital for 6 quarters in 2013 and the first half of 2014 were extracted to establish PCA model. By constructing the score chart of the principal components and combining with the load chart of the principal components, the amount of narcotic drugs was evaluated as a whole, and the varieties with significant changes were screened. When the statistical value of a quarter of an exception, the construction of variable contribution graph analysis of the reasons. Results The dosage of narcotic drugs in our hospital was stable, and the dosage of narcotic drugs in hospital changed greatly. The dosage of oxycodone extended release tablets and the dosage of 8.4 mg of transdermal patch of fentanyl decreased most significantly. Conclusion This study demonstrates the effectiveness of PCA in the monitoring of narcotic drugs and provides a new method for the monitoring of narcotic drugs in hospitals.