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研究生:林佳佑
研究生(外文):Jia-You Lin
論文名稱:長期血壓量測之作用: 於台灣的血壓變異性社區型研究
論文名稱(外文):Effect of Long-term Blood Pressure Monitoring: A Community- based Blood Pressure Variability Study in Taiwan
指導教授:巫坤品郭冠良郭冠良引用關係
指導教授(外文):Kun-Pin WuKuan-Liang Kuo
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
學門:生命科學學門
學類:生物化學學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:112
中文關鍵詞:血壓變異性數位化居家血壓監測血壓量測行為高血壓
外文關鍵詞:Blood pressure variabilityDigitalized home blood pressure monitoringMeasure patternHypertension
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背景:長期的居家血壓監測能幫助治療高血壓;此外,過去的研究顯示,血壓變異性(BPV)與心血管疾病風險呈正相關。但時至今日,我們對血壓變異性與不同的居家血壓量測行為之間的相關性知之甚少。
目的:本研究是以社區為基礎的研究,旨在評估不同的血壓量測行為與血壓變異性之間的關聯。
方法:本研究共納入1128名參與者(平均年齡:77.4 ± 9.3歲,男性:51%),其中包括23278個行為測量單位。為了全面使用原始血壓量測數據,我們使用了滑動窗口(Sliding widnow)技術進行採樣,以每6個月為一個行為測量單位,將原始量測血壓數據以局部重複的方式重疊切割,直到數據不連續為止。根據所有行為測量單位的前3個月數據來定義並評估三種測量行為(穩定頻繁測量[SF]、穩定不頻繁測量[SI]和不穩定測量[US]),並使用每個行為測量單位後面3個月的數據評估該行為測量單位的血壓變異性。我們使用線性混合模型(Mixed Effect model)來校正年齡、性別、身高體重指數、教育程度、社會經濟地位、接受社會福利與否、居住地區、飲酒習慣、吸煙習慣、是否使用降血壓藥物,以及中風、高血壓、高血脂、心臟病和糖尿病等疾病史,以評估血壓量測行為與血壓變異性之間的關係。其中,我們利用居家血壓監測(Home Blood Pressure Monitoring, HBPM)所得到的血壓數值來計算血壓的平均實際變異性(Average Real Variability, ARV)和變異係數(Coefficient Variability, CV)。
結果:穩定頻繁測量行為、穩定不頻繁測量行為,以及不穩定測量行為的平均血壓並沒有顯著差異。但是,不論是收縮壓或舒張壓,穩定頻繁量測行為的血壓變異性明顯低於穩定不頻繁與不穩定測量行為的血壓變異性;並且對收縮壓來說,此血壓變異性的差距會從第一個月到第三個月(每行為量測單位的第4、第5,和第6個月)依序遞減。但是與收縮壓相反,舒張壓的血壓變異性的差距不會隨時間而遞減或遞增。而穩定不頻繁測量血壓和不穩定測量血壓的血壓變異性並沒有顯著差異。
結論:穩定且頻繁的血壓量測行為與較低的血壓變異性獨立相關,這意味著居家定期測量血壓可能與血壓變化波動較小有相關。
 
關鍵字:血壓變異性 、數位化居家血壓監測 、血壓量測行為、高血壓
Backgrounds: Long-term blood pressure (BP) measurements using home BP monitoring is a useful tool in the management of hypertension. Blood pressure variability (BPV) has been associated with an increased risk of cardiovascular events. Little is known regarding whether the BPV is associated with different BP measurement patterns of long-term home BP monitoring.
Aims: In this community-based study, we aimed to assess the associations between the dynamic BP measurement patterns and BPV.
Methods: A total of 1128 subjects with 23278 behavior measuring units were included in the present study (ages 77.4 ± 9.3, 51% male). To utilize the comprehensive home BP data, we used the sliding window sampling technique, which groups participants’ home BP data with a regular 6-month interval into units in a sliding manner until the data was not continuous. Three measurement patterns (stable frequent [SF], stable infrequent [SI], and unstable [US]) were determined based on home BP data in the first three months, and the data in the following three months were used to compute the BPV of that unit. A linear mixed-effects model was used to model the association between BP measurement patterns and BPV by adjusting age, sex, body mass index, education, social-economic status, receiving social welfare benefit, residences area, drinking habit, smoking habit, drugs, the history of stroke, hypertension, heart disease, diabetes mellitus. Average real variability and coefficient variability were calculated from Home BP monitoring (HBPM) as a measure of BPV.
Results: In general, there have no significant differences in average BP among stable frequent patterns, SI patterns, and US patterns. However, BPVs in the SF pattern were significantly lower than those in the US pattern, and the differences decreased from the first month to the third month (4th, 5th, and 6th month in a 6-month unit). BPVs in the SF pattern were also significantly lower than those in the SI pattern. However, unlike the BPVs of SBP, the differences did not attenuate with time in the BPVs of DBP. The BPVs between SI patterns and US patterns were not statistically different.
Conclusion: A stable and frequent BP measuring pattern was independently associated with a lower BPV. Measuring BP regularly at home may be associated with a lower fluctuated BP profile.

Keywords: Blood pressure variability, Digitalized home blood pressure monitoring, Measure pattern, Hypertension
論文電子檔著作權授權書 i
論文審定同意書 ii
中文摘要 iii
英文摘要 iv
目錄 vi
圖目錄 viii
表目錄 ix
第一章 緒論 1
1.1 緒論 1
1.2 血壓變異性(Blood Pressure Variability) 2
1.3 滑動窗口(Sliding Window) 3
1.4 目標 4
第二章 文獻回顧 5
2.1 血壓 5
2.2 血壓變異性 7
2.3 滑動窗口 8
2.4 統計模型 9
第三章 材料與方法 11
3.1 個案來源 11
3.2 血壓測量 12
3.3 滑動窗口(Sliding Window) 12
3.4 研究人群 14
3.5 血壓量測行為的分類 16
3.6 居家血壓和血壓變異性 (Home Blood Pressure and BPV) 18
3.7 模擬實驗:不同採樣率與血壓變異性之間的關聯研究 19
3.8 潛在的干擾因子 19
3.9 統計分析 20
3.10 敏感度分析(SensitivityAnalyses) 20
3.11 滑動窗口穩定性分析 21
第四章 結果 22
4.1 結果 22
4.2 模擬實驗:不同採樣頻率與血壓變異性之間的關聯研究 24
4.3 敏感度分析(Sensitivity Analysis) 25
4.4 滑動窗口穩定性分析 25
第五章 討論和觀點 27
5.1 討論 27
5.2 機制 28
5.3 優勢和局限性 30
5.4 觀點 30
第六章 新穎性與重要性 31
6.1 新穎性 31
6.2 相關性 31
6.3 結論 31
參考文獻 32
附錄 40
附錄 1、模擬實驗:不同採樣頻率與血壓變異性之間的關聯研究 40
附錄 2、用於執行 Mixed Effect Model 的 SAS 代碼 45
附錄 3、用於執行測量次數和血壓變異性關係的 SAS 代碼 47
附錄 4、用於執行表一獨立分析的 SAS 代碼 48
附錄 5、名詞對照表 50
附錄 6、分析結果表 52

圖目錄
圖 1、研究參與者納入與排除流程圖。 12
圖 2、滑動窗口技術(sliding window technique)。 13
圖 3、13週每週測量天數標準差 (standard deviation)的累積分佈與機率分佈 16
圖 4、穩定測量行為(穩定頻繁測量行為(SF)+穩定不頻繁測量行為(SI))每週平均測量天數的分佈。 17
圖 5、不穩定測量行為每週平均測量天數的分佈。 17
圖 6、不同血壓量測行為(SF、SI和US)之間的換組頻率。 18
圖 7、行為累積分佈:參與者最常見的血壓量測行為的百分比分佈。 23
圖 8、真實世界血壓變異性和測量次數關係 23
圖 9、模擬分析:在參與完整12個月(52週)的血壓測量者中,根據不同採樣比率,血壓變異性的變化。 25
圖10、比較兩種生成數據的方法(傳統方法和滑動窗口技術) 26
附圖 1、模擬分析:在參與完整1個月(4週)的血壓測量者中,根據不同採樣比率,血壓變異性的變化。 41
附圖 2、模擬分析:在參與完整3個月(12週)的血壓測量者中,根據不同採樣比率,血壓變異性的變化。 41
附圖 3、模擬分析:在參與完整6個月(26週)的血壓測量者中,根據不同採樣比率,血壓變異性的變化。 42
附圖 4、模擬分析:在參與完整9個月(39週)的血壓測量者中,根據不同採樣比率,血壓變異性的變化。 42

表目錄
表 1、參與者的基礎資料 14
表 2、血壓變異性公式表 19
表 3、測量次數與真實世界血壓變異性、模擬實驗血壓變異性結果摘要 24
附表 1、模擬分析結果摘要 43
附表 2、根據三種不同的測量行為比較平均血壓和血壓變異性(閾值= 4) 52
附表 3、根據三種不同的測量行為比較平均血壓和血壓變異性(閾值= 3) 54
附表 4、根據三種不同的測量行為比較平均血壓和血壓變異性(閾值= 5) 56
附表 5、比較不同閾值區分穩定測量組別與不穩定測量組別(閾值= 1.17) 59
附表 6、比較不同測量頻率結果摘要 61
附表 7、比較不穩定測量與穩定測量行為中不同測量頻率結果摘要 80
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