| 研究生: |
蔡昇翰 Tsai, Sheng-Han |
|---|---|
| 論文名稱: |
建立慢性阻塞性病人發生一年內死亡的預測指標 Development of predicting indexes for 1-year mortality in chronic obstructive pulmonary disease patients |
| 指導教授: |
李中一
Li, Chung-Yi |
| 學位類別: |
碩士 Master |
| 系所名稱: |
醫學院 - 公共衛生研究所碩士在職專班 Graduate Institute of Public Health(on the job class) |
| 論文出版年: | 2021 |
| 畢業學年度: | 109 |
| 語文別: | 中文 |
| 論文頁數: | 62 |
| 中文關鍵詞: | 慢性阻塞性肺疾病 、死亡 、預測 、預後 |
| 外文關鍵詞: | COPD, mortality, prediction, prognosis, validation |
| 相關次數: | 點閱:90 下載:32 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
背景:慢性阻塞性肺疾病(COPD)是一種不全可逆的呼吸道氣流阻塞疾病,對患者的健康及醫療照護系統皆造成很大的負擔,在台灣之盛行率估計約6.1%,到目前為止,除了戒菸,並沒有證據證實治療可改善COPD病人的存活。在慢性惡化的病程末期,病人常常遭遇反覆急性惡化及呼吸衰竭,最後邁入死亡,即使使用呼吸器存活,生活品質極差,對照顧者及病人都很重要的問題是,什麼時候會死亡?何時是採行緩和醫療的時機?若能正確預測病人在未來一段時間內使否死亡,對照顧病患的末期生活及品質是很重要的。
目的:本研究便是希望利用健保資料庫,尋找與死亡相關的危險因子,並建構一年內死亡的預測指標。
方法:利用健保資料庫的百萬歸人檔,並串聯承保檔、醫事機構基本資料檔、醫事人員基本資料檔、專科醫師證書主檔、門診處方治療明細檔、住院醫療費用清單明細檔,利用一年內至少有三次門診診斷有COPD,或是一次以上的住院診斷有COPD者,來獲得COPD世代,並串聯以上各檔案得到相關的人口學變項、共病症診斷、醫療利用相關資訊。區分一年內死亡的各個相關危險因子,依據迴歸模型選取與死亡相關之危險因子,並建構出加總的指標分數,來評估預測指標能力。
結果:總共的COPD世代共有34446人,將新診斷COPD的12455人作為本研究主要對象。將世代中新發個案依據COPD診斷日期分成兩組,模型建立組為2005至2007年之診斷個案,驗證組為2008年以後之診斷個案。研究發現主要跟死亡相關的人口學變項為性別、年齡以及投保薪資。多數共病症包括心血管疾病、失智症、糖尿病、腎臟病與癌症皆與一年內死亡相關。在醫療利用率方面,與死亡相關的為門診次數與住院次數。利用以上因素的羅吉斯迴歸模型,建立出總合的風險指標並畫出操作特徵曲線(ROC curve),在模型建立組及驗證組的AUC(area under ROC curve)分別為0.82及0.81。而利用最佳切點的陽性預測率(PPV)則約為25%。
結論:利用健保資料庫所擁有的資料,可以建立出一個具有中上預測能力的指標,並且擁有高度陰性預測率,不過若利用指標挑出真正會在一年內死亡的病人,正確率仍待改進,可能需要挑選適合的病人使用。
Current recommendations for palliative care in COPD patients are not perfect nor precise, most because prediction of death is difficult. We conducted a retrospective cohort study to develop a predictive index for 1-year mortality in COPD patients. From Longitudinal Health Insurance Database 2005, we selected COPD cohort which included 21991 prevalent cases and 12452 incident cases. All patients had complete-1 year follow up and the end point was mortality. By applying logistic regression model, we found risk factor for mortality were male gender, old age, lower income, more hospitalization, higher comorbidity score and lower numbers of outpatient department visits. We developed prognostic index based on these variables and the c-statistics was 0.82. When applying the index in validation group, the model has nearly the same discrimination power (c-statistics = 0.81). Best cut-off point based on Youden index was calculated to identify patients with high risk for death. When comparing with actual mortality status, out predictive index showed high negative predictive value (95%) but relatively low positive predictive value (25%).
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