臺灣博碩士論文加值系統

目的： 目前已有研究指出重症病患延遲入住加護病房對病患預後的影響，但仍鮮有研究以急診等待加護病房的時間，來探討其對重症病患預後結果的影響。本研究目的在於探討於急診等待加護病房的重症病患，等待時間對其預後及醫療照護資源使用的影響。

設計： 這是一個透過病例回顧的回溯型研究。於2009年7月至2010年6月，所有成人(實際年齡大於17歲) 非外傷的病患，在急診因急性呼吸衰竭而使用呼吸器支持者，皆納入分析。透過回顧病歷記載，收集了每個個案的人口統計學資料、檢傷級數、到急診時之生命徵象、等待加護病房時間、出院狀態、急性生理及慢性健康評估分數項目、72小時急診非計畫性再回診、出院14天內再住院、呼吸器使用天數、加護病房住院天數、總住院天數等項目納入統計分析。於病患存活與否的結果分組，連續變項用以 Student’s t test、Wilcoxon’s rank sum test，類別變項以 Chi-square及 Fisher’s exact test來分析組間是否有統計上的顯著差異，最後以Cox regression 及 logistic regression來做多元迴歸分析，以探討急診急性呼吸衰竭並使用呼吸器支持的重症病患，加護病房等待時間對其預後結果的影響。

背景設定： 急診部門及加護病房。

病人: 於急診因急性呼吸衰竭而使用呼吸器支持之非外傷成人病患(實際年齡大於17歲)，且成功存活入住到加護病房者。

介入： 無。

結果： 本研究計有1014名個案納入分析，並以病患的存活與否(加護病房21天死亡率、在院死亡率)、較多資源使用與否來作為研究的結果變項；而較多資源使用以呼吸器依賴 (呼吸器連續使用大於21天) 及超長住院 (住院天數大於30天) 來做評估。在Cox迴歸模式下，研究發現加護病房等待時間對病患死亡率而言是一個會質性改變 (危害、非危害) 的變項。故以擬化學滴定的方式，定位出質性改變的時間位置，是發生在急診等待加護病房的9至10小時。於等待時間的前9小時，對加護病房21天死亡率而言，等待時間(單位：小時) 的死亡危險比為1.146 (95% 信賴區間為 1.066-1.232)。而當等待時間延伸超過10小時，卻發現等待時間的死亡危險比於統計上開始呈現不顯著的結果，甚而開始質性改變而成非危害效果。對在院死亡率而言，以同樣方法定位出質性改變的時間位置，是發生在急診等待加護病房的11至12小時，於前11小時等待時間的死亡危險比為1.056 (95% 信賴區間為 1.005-1.110)。此外，急性生理與慢性健康評估分數較高者，對病患死亡率亦是風險因子。而在Cox回歸模式下分析院內死亡機率，以呼吸系統感染為參考類別，我們發現診斷類別為顱內出血、敗血症、腸胃道出血、到院前心肺停止、非冠狀動脈疾病之心血管疾病的病患有相對較高的死亡風險，鬱血性心衰竭之診斷類別會有相對較佳的存活機率。對於存活至加護病房的急診病患而言，加護病房等待時間，在呼吸器依賴上的勝算比為1.015 (95% 信賴區間為 1.008-1.022)；在超長住院上的勝算比為1.016 (95% 信賴區間為 1.008-1.024)。

結論： 急診難以暫代加護病房對重症病患照護的角色，故對急診急性呼吸衰竭而使用呼吸器支持的病患，及時入住到加護病房對病患的預後是有裨益的，亦可降低日後醫療資源的使用。因愈延時入住加護病房，那些能因及時入住加護病房而降低死亡率的病患就愈少，使得加護病房對存活率的裨益會隨之下降，並在9至10小時後消失。而那些存活於延時入住加護病房的重症病患，其對醫療資源的耗用也隨著加護病房等待時間的延長而增加。重症病患延時入住加護病房而停滯於急診，對醫療照護體系是一個惡性循環；因救治不到有迫切需求的病人，而存活者對醫療照護的依賴也較多，因而排擠系統對新病患的處理流量，更增加急診重症病患停滯。
本研究建議醫療照護系統之官方相關管理者，應探求重症病患於急診停滯的原因，並設法解決或緩解此一困境，以減少因急診留滯對病患安全及病患健康的危害。

Objective: Adverse outcomes related to delay intensive care unit admission for critical patients in emergency department have been recognized in previous studies. There is still no study using length of waiting time as a variable to study the associations of adverse outcomes. The study was to explore impact of the length of emergency department boarding time for critically ill patient.
Design: This was a retrospective cohort study based on charts review. From Jul 2009 to Jun 2010, all non-traumatic adult patients (age >17 years old) intubated and requiring mechanical ventilation in emergency department were included in study. Demographics, triage results, length of stay in emergency department, intensive care unit and hospital, intensive care unit waiting time, components of Acute Physiology and Chronic Health Evaluation II scores, principal diagnostic categories leading to respiratory failure, 72 hours unplanned revisits, 14 days readmission, status of hospital discharge and days of ventilator using were collected. The results of surviving and no surviving were analyzed by using Chi-square, Fisher’s exact test, Student’s t test, and Wilcoxon’s rank sum test. Models of Cox’s regression and logistic regression were used for multivariate analysis.
Setting: Emergency department and intensive care units.
Patients: All non-traumatic adult patients in emergency department, intubated and requiring mechanical ventilation owing to acute respiratory failure, were enrolled in the study.
Intervention: none
Measurements and main results: The primary outcomes were 21-day intensive care unit mortality, in-hospital mortality, continuous ventilator utilization over 21 days, and prolonged hospital stays over 30 days. There were 1014 patients included into the study. Under Cox regression model, we found that the hazard effect of intensive care unit -waiting time on patient survival probability was changing hour by hour, and then quality reversed. Using a method like titration in chemistry, we located the time of quality-reversing beginning at hour 9 to 10 for 21-day intensive care unit mortality, and hour 11 to 12 for in-hospital mortality. The hazard ratio of intensive care unit-waiting hour was 1.146 (95% confidence interval, 1.066–1.232) for 21-day intensive care unit mortality in the first 9 hours, and 1.056 (95% confidence interval, 1.005–1.110) for in-hospital mortality in the first 11 hours. When intensive care unit -waiting time extended beyond the turning points, the hazardous effect was reversing. Additionally, compared with respiratory infection, the diagnostic categories of intracranium hemorrhage, sepsis, out hospital cardiac arrest , gastrointestinal bleeding, cardiovascular disease(not coronary artery disease) were associated with higher hospital mortality; diagnostic category of congestive was associated with better survival probability. For ventilator dependence over 21 days and prolonged hospital stay over 30 days, the adjusted odds ratios of entire waiting time in emergency department was 1.015 (95% confidence interval, 1.008–1.022) and 1.016 (95% confidence interval, 1.008–1.024) respectively.
Conclusion: Critically ill patients requiring mechanical ventilation should be transferred to intensive care unit from emergency department as soon as possible, not over 9 hours. Survival benefit of intensive care unit admission faded progressively as time passed by and vanished after 10 hours boarding in emergency department, because most potential patients were expired during the waiting course. For critically ill patients, intensive care unit -waiting in emergency department has shadow-like adverse effects on extra use of healthcare resources, which were accessed via prolong length of ventilator dependence and hospital stay in the study. Delayed intensive care unit admission in emergency department is a vicious cycle, because of that we could not save the potential patients, the survivor crowd the healthcare capacity and result in more critical patients stasis in emergency department. This is a systemic healthcare problem that has threatened medical care quality and patient safety. Healthcare authority should identify and make more efforts to manage the problem.

誌 謝
中文摘要
英文摘要
目 錄
表目錄
圖目錄
附 件: 人體試驗倫理委員會同意臨床試驗證明書


CONTENT
Chapter 1. BACKGROUND 16
Chapter 2. METHODS 18
2.1 Study Population and Hospital 18
2.1.1 Patients included 18
2.1.2 Study hospital and physician staffs 18
2.1.3 Patients excluded 19
2.2 Data Collection 20
2.3 Definition of ICU-waiting time in ED 21
2.4 Outcome Measures: survival and resources utilizations 21
2.5 Statistical Analysis 23
2.5.1 Patients’ baseline characteristics and censored group 23
2.5.2 Analyzed primary outcome with Cox regression model and logistic regression model 23
Chapter 3. RESULT 25
3.1 Brief of Patients Included 25
3.2 Descriptive statistics of patients’ baseline characteristics 26
3.3 Results of Outcome Analysis 27
3.3.1 The role of intensive care was not played well by emergency department 27
3.3.2 Should some specific diseases be admitted to ICU prior to others? 28
3.3.3 Critically ill patients should be transferred to intensive care unit from emergency department as soon as possible 30
Chapter 4. DISSCUSSION 33
4.1 Not All Previous Studies Found the Associations between Delayed in ED to ICU Admission and Adverse Outcomes. 33
4.2 The Conditions of ED Was Suboptimal for Intensive Care 36
4.3 ED Overcrowding and Prolonged ICU-waiting 37
4.4 Triage Result and Short-term Hospital Returns Were Not Associated with Outcome 38
4.5 Study Limitation 39
Chapter 5. CONCLUSION 41

REFFERENCE 42


表目錄 (Table List)

Table 1. Baseline characteristics of patients 52
Table 2. Baseline characteristics of patients (diagnostic categories) …...53
Table 3. Hazard ratios of multivariate Cox regression for in-hospital mortality …………………...………………………….………54
Table 4. Hazard ratios of 21-day intensive care unit mortality of different
diagnostic category in multivariate analysis ………..………..55
Table 5. Hazard ratios of in-hospital mortality of different diagnostic
category in multivariate analysis …………..…………………56
Table 6. Quantile distribution of frequency by ICU-waiting hours ….…57
Table 7. Hazard ratios of 21-day ICU mortality of different populations, stratified by sections of ICU-waiting hours ………….....……58
Table 8. Hazard ratios of in-hospital mortality of different populations,
stratified by sections of ICU-waiting hours ………….....……59
Table 9. Hazard ratios of multivariate Cox regression for 21-day ICU
mortality …………………...……………………...….....……60
Table 10. Hazard ratios of multivariate Cox regression for in-hospital
mortality ………………….………………...………...………61
Table 11. Odds ratios of continuous ventilator utilization over twenty-one days …………………………………………………………..62
Table 12. Odds ratios of prolonged-length hospital stay over thirty days …………………………………………………………..63


圖目錄 (Figure List)

Figure 1. Brief of patients included …………………………………….64
Figure 2. Survival curves of patients cared in intensive care unit (ICU)
and emergency department (ED) …………………………….65
Figure 3. Survival curves of 21-day intensive care unit (ICU) course of
diagnostic categories …………………………………..……..66
Figure 4. Survival curves of in-hospital course of diagnostic
categories …………………………………………………….68
Figure 5. Frequency of ICU-admission events divided into groups of
specific ICU-waiting hours …………………………………..70
Figure 6. Frequency of expired events divided into groups of specific
ICU-waiting hours …………………………………..……….71
Figure 7. Hazard ratios and 95% confidence intervals of different sections of ICU-waiting hours ……………………………………..….72
Figure 8. Hazard ratios and 95% confidence intervals of different sections
of ICU-waiting hours for in-hospital mortality …………..…..73

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