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Drug-Drug Interactions in Outpatients
|上傳時間: ||2010-06-01 09:21:32 (UTC+8)|
|摘要: ||背景: 過去研究都在關注藥物交互作用之發生率及其相關因素，然而並無研究使用資料探勘技術去分析處方型態及藥物交互作用之預測模式。
研究目的: 建立心臟血管內科處方藥品關聯規則及藥物交互作用等級預測模式。研究方法: 本回溯性研究資料為2004年健保研究資料庫之門診處方治療明細檔、醫療機構檔及醫師檔。衛生署交互作用資料庫用於擷取有交互作用之處方。本研究為了建立同成份藥品之關聯規則，因此藥品代碼轉成解剖/療效/化學(ATC)代碼。資料分析工具為SPSS 12.0與Clementine 7.2之C5.0決策樹及Apriori關聯規則。研究結果: 門診處方藥物交互作用等級與性別無關，年齡會影響交互作用發生之等級，1級交互作用的病患平均年齡為64.6 ± 15.9歲、藥品品項數為6.5 ± 2.6個與給藥天數為24.3 ± 7.8天，都顯著高於其他等級的平均數(p<0.001)。就醫科別以心臟血管內科，診斷別以本態性高血壓之交互作用發生率居冠。
心臟血管內科與高血壓之門診處方都以發生2級的交互作用處方居多，分別為51.41%及57.13%。就老人門診處方與心臟血管門診處方分別分析之結果與門診處方之分析結果大致上相同，但在藥品關聯規則組合之門診處方前十排行有列入結核病藥物Isoniazid與Rifampin；類風濕性關節炎藥物Methotrexate與Sulfonamides。1級藥物交互作用之藥品組合無論在門診處方、老人門診處方或心臟血管內科門診處方都以Digitalis Glycosides與Loop Diuretics出現頻率最多(28.19~32.55%)。
心臟血管內科門診交互作用處方之C5.0決策樹結果顯示主診斷是影響交互作用等級之最重要因素。在支持度5%，信心度40%下沒有交互作用嚴重度1級之藥品關聯規則，而有一組2級交互作用藥品組合Carvedilol與Aspirin，但用於其禁忌症心臟衰竭之病人只有0.21%。結論: 處方交互作用提示系統對於Digitalis Glycosides與Loop Diuretics交互作用提示是否有其必要性。此外Carvedilol與Aspirin藥品組合用於禁忌病心臟衰竭只有0.21%。因此建議臨床專家與處方開立決策支援系統建置者應研商取得共識，建立客製化交互作用知識庫，否則提示”警訊”反成為”雜訊”。
BACKGROUND: Previous drug-drug interactions (DDIs) research has focused on the incidence and related factors of DDIs. However, few studies have examined DDIs at the patterns of medication use and even fewer studies addressed the rules for severity level prediction adopting data mining techniques. OBJECTIVES: To examine the incidence in outpatients visits involving DDIs and explore prescription drugs association rules and DDIs level prediction rules. METHODS: This retrospective research was conducted using data from the 2004 Taiwan National Health Insurance Research Database (NHRID) which contains outpatients’ prescriptions, therapeutic and registration files. DDIs prescriptions were retrieved using the DDIs database of the Department of Health. The Anatomical Therapeutic Chemical Classification System with Defined Daily Doses (ATC/DDD system) was used to identify drugs at the name of chemical substance instead of brand name. A decision tree model was built to predict the DDIs level. Apriori computational method was needed for mining the patterns of medication use. RESULTS: The sex did not differ in the level of DDIs. Outpatient’s age was associated with the level of DDIs. The mean age, number of medications and drug supply days of first level DDIs prescriptions were 64.6±15.9years, 6.5±2.6 medication and 24.3±7.8 days, and with a significantly higher in other level DDIs, respectively (P < 0.0001). Outpatients with internal cardiovascular specialty, essential hypertension and hypertensive heart disease were treated most with DDIs prescriptions. Prevalence of secondary level DDIs was 51.41% in internal cardiovascular specialty, 57.13% in essential hypertension outpatients’ prescriptions. The results of elderly and cardiovascular DDIs levels were also similar, with secondary level outnumbering other levels in DDIs. In terms of the medication association rule, isoniazid and sulfonamide (tuberculosis drugs), together with methotrxate and sulfonamide (rheumatoid arthritis drugs) were listed in top ten item sets of the outpatients’ first level DDIs prescription. The medication combination of digitalis glycosides and loop diuretics was listed as top one in spite of the data from outpatients, elderly or cardiovascular prescriptions. The first hierarchy level of C5.0 decision tree was principal diagnosis. Association rules with support and confidence threshold values of 5% and 40% generated one association rule with DDIs secondary level, carvedilol and aspirin, which was contraindication to heart failure. However, this study found that carvedilol and aspirin was only prescribed 0.21% in heart failure patients.
CONCLUSIONS: Digitalis glycosides and loop diuretics was still the most frequent DDIs medication combination. The principal diagnosis was of pivotal importance for the DDIs level. In the light of prescription diagnosis and association rules, the patterns of medication use were reasonable. The knowledge hidden behind the results uncovered the current computerized DDIs alerts are often over “alerts” because of the DDIs knowledge bases are highly inclusive, placing more emphasis on breadth of coverage than on clinical relevancy or severity of adverse events. Therefore it is essential to design a computerized prescribing decision support with customized knowledge bases.
|Appears in Collections:||[醫務管理系(所)] 博碩士論文|
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