呼吸器脫離是指中斷重症病人呼吸器使用的過程。臨床資料顯示，加護病房內，大約30%-40%的病人必須使用呼吸器來維持生命，這些病人必須在適當時間停止使用呼吸器以避免長期使用呼吸器所引發的感染及肺部傷害，以及降低醫療成本；因此臨床醫師必須每日評估使用呼吸器的病人是否能夠脫離呼吸器。一般專業醫師預測病人能否成功脫離呼吸器的成功率約35%-60%，臨床決策系統 (Clinical decision support systems, CDSS) 已經證實可以幫助醫師提高疾病診斷正確率和改善醫療品質。據我們所知，至目前為止，並沒有研究針對預測呼吸器脫離的臨床決策系統進行臨床實測與系統效能驗證。本研究收集使用呼吸器病人之資料，包括人口學資料、生理與疾病因素、照護和治療因素等27項變數，使用邏輯斯回歸分析(Logistic regression analysis LRA) 與遞歸特徵消除(Recursive feature elimination RFE)封裝器方法(Wrapper method) 找出顯著特徵，然後再針對這些顯著特徵，使用支持向量機(Support vector machine SVM)進行呼吸器脫離臨床決策系統之設計。本臨床決策系統經臨床證實具有預測呼吸器脫離結果與減少呼吸器使用天數的效能。
本研究首先收集348位四個不同時期的呼吸照護中心病人資料，經過邏輯斯回歸分析後找到7項具顯著意義 (p <0.05)的變數，遞歸特徵消除法則挑選出11項變數，然後再利用這些變數進行臨床決策支援系統之設計。系統經過模型訓練及交叉驗證後共徵招380位使用呼吸器的病人於呼吸照護中心進行臨床測試。進行臨床測試之前，病人被隨機分派於實驗組或對照組中；於實驗組中，醫師被要求利用臨床決策系統協助臨床判斷，而對照組病人的判斷則全賴臨床醫師的專業判斷。排除轉院、拒絕進一步治療、死亡的病人後，實驗組及對照組分別有168位及 144位病人完成臨床測試。
結果顯示，利用邏輯斯回歸(LRA)與遞歸特徵消除法(RFE)挑選參數後所建構之臨床決策系統，其交叉驗證之正確預測率分別為88.33%與92.73%；使用支持向量機所設計之臨床決策系統，其正確率為91.25%，比使用類神經網路所設計之的系統正確率(88.69%)更高。在臨床驗證方面，使用臨床決策系統的靈敏度(sensitivity)為87.7%， 顯著地比臨床醫師專業判斷的敏感度(61.4%)高(p <0.01)；實驗組平均使用呼吸器天數為38.41±3.35天，顯著地比對照組平均使用呼吸器天數43.69±14.89天減少約5.2天(p <0.001)，平均可以節省每位病人健保醫療花費約新台幣45,000元(約1,500美元)。本研究的結果顯示，呼吸器脫離臨床決策系統，可以提供臨床醫師一個有效的工具，可以提早發現病人成功脫離呼吸器的時間點，進而降低病人呼吸器使用時間，減少不必要的醫療過程與醫療費用。

Weaning is typically regarded as a process of discontinuing mechanical ventilation in the daily practice of an intensive care unit (ICU). Among the ICU patients, about 40% need mechanical ventilator for sustaining their lives. The predictive rate of successful weaning achieved only 35-60% for decisions made by physicians. Clinical decision support systems (CDSSs) are promising in enhancing diagnostic performance and improving healthcare quality in clinical setting. To the best of our knowledge, a prospective study has never been conducted to verify the effectiveness of the CDSS in ventilator weaning before. This study designed a clinical decision support system using support vector machine (SVM) to predict if a patient can be weaned from mechanical ventilator successfully. A filter method based on logistic regression analysis (LRA) and a wrapper method based on recursive feature elimination (RFE) were adopted to select salient features from 27 variables, including demographic data, physiology and disease factors, and care and treatment factors for CDSS. The CDSS capable of predicting weaning outcome and reducing duration of ventilator support for patients has been verified.
Data of 348 patients were collected at four different periods from an all-purpose respiratory care center. Seven significant variables (p <0.05) using LRA contrasted to eleven variables using RFE algorithm were selected for designing CDSSs using SVM. After the CDSS has been designed, a total of 380 patients admitted to the respiratory care center of the hospital were randomly assigned to either control or study group. In the control group, patients were weaned with traditional weaning method based on the professional judgments of physicians; while in the study group, patients were weaned with the assistance of CDSS monitored by physicians. After excluding the patients who transferred to other hospitals, refused further treatments, or expired the admission period, data of 168 and 144 patients in the study and control groups, respectively, were used for analysis.
The predictive accuracy under cross-validation is 88.33% and 92.73% by using features selected with LRA and RFE, respectively. The CDSS constructed using SVM was shown to have better accuracy (91.25%) than using neural network (88.69%). In clinical verification the results show that a sensitivity of 87.7% has been achieved, which is significantly higher (p <0.01) than the weaning determined by physicians (61.4%). Furthermore, the days using mechanical ventilator for the study group (38.41±3.35) is significantly shorter (p <0.001) than the control group (43.69±14.89), with a decrease of 5.2 days in average, resulting in a saving of healthcare cost of NT$45,000 (US$1,500) per patient in the current Taiwanese National Health Insurance setting.
The designed CDSS with a graphic user interface (GUI) provides a valuable tool to assist physicians to determine if a patient is ready to wean from the ventilator. The CDSS is demonstrated to be effective in identifying the earliest time of ventilator weaning for patients to resume and sustain spontaneous breathing, thereby avoiding unnecessary prolonged ventilator use and decreasing healthcare cost.

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