來源資料
相關文獻
- Predicting Blood Pressure Change Caused by Rapid Injection of Propofol during Anesthesia Induction with a Logistic Regression Model
- 運動後低血壓反應與血壓控制之探討
- 番茄撞擊之黏彈模型的研究
- 地圖水系網簡化類型之研究--以臺灣地區河川為例
- 迴歸分析之運用
- 編製我國小學六年級女生健康體適能效標參照的組合測驗
- 煉鋼製程提高銻金屬回收率之技術
- 森林遊樂區遊客遊憩體驗及遊憩效益影響因素之探討--以棲蘭森林遊樂區為例
- Lofexidine對海洛因戒斷症狀之治療效果: 一開放性試驗
- 編製國小六年級男生健康體適能效標參照的組合測驗
題 名 | Predicting Blood Pressure Change Caused by Rapid Injection of Propofol during Anesthesia Induction with a Logistic Regression Model=預測使用Propofol做麻醉誘導時的血壓變化--使用Logistic Regression模型 |
---|---|
作 者 | 饒瑞泓; 李友專; 鄭仁坤; 陳建全; 柯元弼; 黃俊仁; |
書刊名 | 麻醉學雜誌 |
卷 期 | 42:2 2004.06[民93.06] |
頁 次 | 頁81-86 |
分類號 | 416.52 |
關鍵詞 | 低血壓; 邏輯式模式; 迴歸分析; Hypotension; Propofol; Logistic models; Regression analysis; |
語 文 | 英文(English) |
中文摘要 | 背景: 建構一個以logistic regression 為基礎的預測模型, 用以預測病人在全身麻醉誘導時使用 propofol 的血壓變化。方法: 在兩百個使用propofol 做全身麻醉的病人記錄中, 每個病人的17 個變數皆詳細紀錄。在麻醉誘導前血壓至少測量3 –5 次, 麻醉誘導後血壓在10 分鐘內至少測量十次。如果麻醉前後血壓的變化超過30%, 則被預測的變數其內容被設定為1, 如果麻醉前後的血壓變化未超過 30%, 則被預測的變數內容設為0。Logistic regression 模型使用此17 個參數來建構, 並以sensitivity 、specificity 及area under ROC curve 來評估此模型的效益。結果: 在兩百個病人之中如果以此logistic regression 模型來做預測其血壓變化, 在取臨界值為 0.17 的情況下,sensitivity 可以達到90%,specificity 可以達到67.3%, 而area under ROC curve 則為 0.855。結論: 在以17 個取自病人的變數( 包括年齡、性別、體重、麻醉前的心跳、血壓以及病人的過去病史等) 為基礎建構而成的logistic regression 模型, 可以相當準確的預測病人在使用propofol 做麻醉誘導時的血壓變化。此模型可以很簡單的和醫院資訊系統整合, 以做為一個容易使用的決策支援系統。 |
英文摘要 | Background : Propofol is a common intravenous agent for induction and maintenance of anesthesia. The advantage of propofol is rapid recovery of consciousness when the continuous infusion is stopped. Additionally, it has antiemetic effect of reducing postoperative nausea and vomiting. On the other hand, rapid infusion of propofol is painful and may cause hypotension. In this study, we aimed to develop a logistic regression model to accurately predict blood pressure change caused by rapid infusion of propofol. Methods : Seventeen variables (including demographic data, past medical history, laboratory data, and blood pressure before induction)were assessed in 200 patients who received propofol for induction of anesthesia for routine surgery. A logistic regression model was derived using these values as independent variables to predict whether a patient would suffer a significant blood pressure change (> 30% decrease from baseline) Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of our predictionmodel. Results : A cut-off value of 0.17 in the logistic regression model predicted decreased blood pressure with 90.0% sensitivity and, 67.3% specificity. The area under the receiver operating characteristic curve was 0.855. Conclusions : Our predictionmodel predicts propofol-induced hypotension with acceptable accuracy. Because of the straightforward mathematic formula used, our model can be integrated effortlessly into a hospital information system, providing a reliable and useful decision support for clinical anesthesia staff. |