臨床呼吸器脫離之快速評估模型研究

To study a fast screening model on the clinical ventilator weaning

10.29423/JSRR.202303_17(3).01

徐桂芳(Kuei-Fang Hsu)；賴君瑋(Chun-Wei Lai)；賴苑惠(Yuan-Hui Lai)；林雅云(Ya-Yun Lin)；張炳華(Bin-Wha Chang)

次級資料分析法 ； 呼吸器脫離 ； 每日篩選參數 ； 疾病嚴重度APACH ΙΙ ； Secondary data analysis ； clinical ventilator weaning (CVW) ； daily screening parameters (DSPs) ； disease severity APACH II

運動與遊憩研究

17卷3期（2023 / 03 / 01）

1 - 16

繁體中文;英文

目的：利用次級資料分析法，分析某醫學中心呼吸照護資料，進行臨床呼吸器脫離之簡易評估模型建立。方法：採回溯性斷面研究，資料來源為某醫學中心呼吸照護資料庫。合格樣本為呼吸器使用大於24小時且年齡大於18歲之患者。資料收集期間為2016至2018年。統計評估模型採用每日篩選參數、脫離總分與最終呼吸器脫離結果。研究結果:合格樣本359名，男性241人（67.1%）、女性118人（32.9%），年齡為66.5±16.98歲，疾病嚴重度APACH ΙΙ分數為22.10±7.17，成功脫離呼吸器者235人（65.5%）、失敗者124人（34.5%）。經由邏輯斯迴歸建立每日篩選參數（呼吸衰竭原因解決否、使用鎮靜劑、使用升壓劑、吐氣末陽壓、咳嗽能力、吸入氧濃度及快淺呼吸）與脫離總分評估模型。在模型準確性分析，每日篩選參數之咳嗽能力、快淺呼吸指標及吐氣末陽壓三項指標為關鍵指標，當指標達標時，其呼吸器脫離成功率分別為失敗組的39.045、5.184及2.819倍。而脫離總分之預測模式，其預測敏感性為94.89%、特異性為74.19%、陽性預測值為87.5%、陰性預測值為88.5%、準確率為87.7%。每日篩選參數與脫離總分兩模型均可有效預測呼吸器脫離之成功率。結論：本研究之脫離總分、咳嗽能力與快淺呼吸指標及吐氣末陽壓等自變項，顯著對臨床呼吸器脫離具有高度關聯性（p<.05），可做為臨床快速判別或有效預測呼吸衰竭患者呼吸器脫離之關鍵指標。

Purposes: To study the respiratory care data of a medical center by using the secondary data analysis, and to establish a simple evaluation model for clinical ventilator weaning (CVW). Methods: A retrospective cross-sectional study was conducted, and the data source was from the respiratory care database of a medical center. Eligible samples were patients over 18 years of age with ventilators used for more than 24 hours. The data collection period was from the year 2016 to 2018. The model of statistical evaluation used daily screening parameters (DSPs), total weaning scores (TWS), and the final ventilator weaning result. Results: 359 qualified samples, 241 males (67.1%) and 118 females (32.9%), aged 66.5 ± 16.98 years old, disease severity APACH ΙΙ score was 22.10 ± 7.17, to the end, 235 cases were successfully weaning from the ventilator (65.5%) and 124 failures (34.5%). The DSPs (resolved on respiratory failure, sedatives, vasopressors, positive end-expiratory pressure, cough ability, inspired oxygen concentration, and rapid shallow breathing) and the TWS were established by logistic regression. In the model accuracy analysis, the DSPs of cough ability(CA), fast and shallow breathing index (FSBI), and end-expiratory positive pressure (EPP) were the key indicators. When the indicators reach the standard, the success rate of CVW was 39.045, 5.184, and 2.819 times than in the failure group, respectively. For the prediction mode of TWS, the predictive sensitivity was 94.89%, the specificity was 74.19%, the positive predictive value was 87.5%, the negative predictive value was 88.5%, and the accuracy was 87.7%. Both the DSPs and the TWS can effectively predict the success rate of CVW. Conclusions: In this study, the independent variables such as the TWS, CA, FSBI, and EEP were significantly correlated with CVW (p<.05), which can be used as a fast clinical screening, or to be a key indicator for effectively predicting CVW in patients with respiratory failure.

1. 廖怡君,李淑秋,邱慧玲,王雅韻(2021)。亞急性呼吸照護病人呼吸器脫離之相關影響因素－回溯性病歷查閱研究。護理雜誌，68(6)，53-61。
   連結：
2. 鏡週刊（2021）。給摯愛的生死課，無效醫療被濫用，健保光呼吸器就年花 122 億。2022/02/11，資料取自 https://www.mirrormedia.mg/story/20210324cul005/
3. Boles, J. M.,Bion, J.,Connors, A.,Herridge, M.,Marsh, B.,Melot, C.,Welte, T.(2007).Weaning from mechanical ventilation.European Respiratory Journal,29(5),1033-1056.
4. Danckers, M.,Grosu, H.,Jean, R.,Cruz, R. B.,Fidellaga, A.,Han, Q.,Khouli, H.(2013).Nurse-driven, protocol-directed weaning from mechanical ventilation improves clinical outcomes and is well accepted by intensive care unit physicians.Journal of Critical Care,28(4),433-441.
5. Ely, E. W.,Baker, A. M.,Evans, G. W.,Haponik, E. F.(1999).The prognostic significance of passing a daily screen of weaning parameters.Intensive Care Medicine,25(6),581-587.
6. Esteban, A.,Alia, I.,Ibañez, J.,Benito, S.,Tobin, M. J.,Group, S. L. F. C.(1994).Modes of mechanical ventilation and weaning: A national survey of Spanish hospitals.Chest,106(4),1188-1193.
7. Foronda, F.,Troster, E. J.,Farias, J. A.,Barbas, C. S. V.,Ferraro, A. A.,Faria, L. S.,Delgado, A. F.(2011).Impact of daily evaluation and spontaneous breathing test on the duration of pediatric mechanical ventilation: A randomized controlled trial.Critical Care,15(2),1-31.
8. Lin, F. C.,Kuo, Y. W.,Jerng, J. S.,Wu, H. D.(2020).Association of weaning preparedness with extubation outcome of mechanically ventilated patients in medical intensive care units: A retrospective analysis.PeerJ,8,e8973.
9. MacIntyre, N. R.(2012).Evidence-based assessments in the ventilator discontinuation process.Respiratory Care,57(10),1611-1618.
10. Mussa, C. C.,Gomaa, D.,Rowley, D. D.,Schmidt, U.,Ginier, E.,Strickland, S. L.(2021).AARC clinical practice guideline: Management of adult patients with tracheostomy in the acute care setting.Respiratory Care,66(1),156-169.
11. Navalesi, P.,Frigerio, P.,Moretti, M. P.,Sommariva, M.,Vesconi, S.,Baiardi, P.,Levati, A.(2008).Rate of reintubation in mechanically ventilated neurosurgical and neurologic patients: Evaluation of a systematic approach to weaning and extubation.Critical Care Medicine,36(11),2986-2992.
12. Yang, K. L.,Tobin, M. J.(1991).A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation.New England Journal of Medicine,324(21),1445-1450.
13. 吳清平,楊式興(2012)。臺灣呼吸器長期使用概況。全民健康保險爭議審議委員會爭議審議報導，51，1-8。
14. 陳敏惠,許弘毅,鍾美勇,張惠娟,蔡玉琴(2020)。早產兒呼吸窘迫使用高流量鼻導管與鼻腔持續氣道正壓通氣之醫療成本與臨床效益－系統性回顧與統合分析。呼吸治療，19，12-13。
15. 陳欽明,宋美儀,鄭高珍,謝俊民(2005)。呼吸照護中心病人之身體質量指數與預後之關係。中華民國急救加護醫學會雜誌，16(1)，9-17。
16. 黃燕秋,吳泰璉,楊惠惠,吳莒瑛,徐紫娟,林淑芬(2021)。運用跨團隊合作提升加護病房病人呼吸器脫離率。臺灣醫學，25(1)，128-135。
17. 楊秀葳,邱芸貞(2021)。探討以傳統測量方式及呼吸器動態性測量呼吸快淺指數預測拔管成功之差異性。呼吸治療，20(2)，74-75。
18. 楊惠喬,劉士嘉,蔡耀德,翁紹仁,黃世哲,詹明澄(2021)。資訊系統應用於呼吸器脫離評估效益之探討。呼吸治療，20(2)，1-9。
19. 廖秀華,吳玉萍,曾瓊誼(2019)。一位肺炎併發呼吸衰竭成功脫離呼吸器病患之護理經驗。安泰醫護雜誌，25(1)，47-60。
20. 趙克耘(2021)。回朔性研究分析－加護病房內拔管後的呼吸支持。呼吸治療，20(2)，85-86。
21. 謝佩吟,賴美玉(2021)。一位多發性硬化症病人成功脫離呼吸器護理經驗。高雄護理雜誌，38(2)，80-92。
22. 羅莉琳(2019)。臺中市，東海大學工業工程與經營資訊學系。

1. (2023)。探討一位直腸癌合併呼吸衰竭病人脫離呼吸機之護理經驗。嘉基護理，23(2)，43-51。