PCA資料蒐集系統之開發及其應用：PCA藥量數學模型之歸納與分析

The Development and Application for Data Acquisition System of Patient-Controlled Analgesia

江宏倫(Hung-Lun Chiang)

指導教授 ： 吳昌暉

元智大學/工程學院/機械工程學系/碩士(2000年)

摘要

本論文係針對醫療器材PCA(Patient Controlled Analgesia, 病患自控式止痛)開發一資料蒐集系統(Data Acquisition System)，並建立癌症末期病患之PCA藥量使用模型以進行用藥量預測與PCA藥量使用類型分析。
本文根據34位(口腔癌、肺癌、肝癌等9種)癌症末期病人使用PCA之每日藥量記錄，經由AR(Auto-regression)模型分析後，依據癌末病人PCA用藥量之時間序列其增減之快慢及振盪程度等特性而區分為數種主要PCA藥量使用類型，並可評估AR模型之準確度。除此之外，尚可依據AR模型之特徵參數進行短期之用藥量預測。吾人依據個別病患20天之藥量記錄來計算其時間序列AR模型之特徵參數，再以此參數分別預測1天後、3天後及10天後之用藥量，觀察其誤差低於30%的成功率。並考慮PCA藥量使用類型之變化，此一現象可能係因病況的改變或因其他的治療所導致，觀察此一現象是否對用藥量之預測造成影響。
研究結果發現：當病人之PCA藥量使用類型穩定時， 使用AR模型預測1天後、3天後及10天後之用藥量，誤差低於30%之成功率分別為82.41%、70.29%、47.36%，而病人之類型有明顯變動時，成功率則明顯降低，因此若病人之類型無明顯之變化時，吾人所提出之方法將可有效進行預測。

關鍵字

病患自控式止痛 ； 自我迴歸模型 ； 預測 ； 分類 ； 資料蒐集系統

並列摘要

In this thesis, we developed a Data-Acquisition (DAQ) System for Patient-controlled analgesia (PCA). The advantages of the DAQ are portable, convenient, efficient and useful for user. Then we analyze the data which collected by the DAQ, we attempt to model the PCA time history with simple AR(Auto-regressive) model and classify the patients'' data for several parts.
According to 34 cancer patients'' data, we can find that the scale of oscillation and the speed of increase or decrease for PCA dose with each patient''s PCA time history were different. Then we can use the information of PCA time history for classifying the patients with several parts. And we modeled the data with AR model for each patient, we predicted the patients'' PCA dose for future with the AR model. Our rates of success for every 1 day, 3 days and 10 days were 82.41%, 70.29% and 47.36%.
In this thesis, our method was offering an information of different focus for doctor to compare with their knowledge and make a better clinical reserch.

並列關鍵字

Patient-Controlled Analgesia ； Auto-regressive model ； prediction ； classification ； Data Acquisition

參考文獻

[1] 病患自控式止痛簡易操作說明，台灣亞培大藥廠股份有限公司

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[3] Gil KM, et. al., ”Patient-controlled analgesia in postoperative pain: the relation of psychological factors to pain and analgesic use”, Clinical Journal of Pain, Vol. 6, No. 2, pp. 137-142. June 1990

[4] Lehmann KA, ”New developments in patient-controlled postoperative analgesia”, Annals of Medicine, Vol. 27, No. 2, pp. 271-282. April 1995

[5] Jacobs, O.L., et. Al., ”Modeling and estimation for patient controlled analgesia of chronic pain”, IEEE Transactions on Biomedical Engineering, Vol. 42, No. 5, pp. 477-485. May 1995