臺灣博碩士論文加值系統

急診室的急性冠狀動脈症候群（ACS）患者管理是一項具有挑戰性的任務，因為大多數患者沒有提供這種病症的明確證據。這導致難以識別在觀察單位（OU）中應該優先進行徹底診斷並且能夠安全地在常規ED隊列中等待的患者。本研究旨在驗證和使用邏輯回歸作為開發一套分類標準的方法，該標準可以識別出現ACS時風險相當大或可忽略不計的患者。同時，評估為通過救護車（119）或自我到達患者到達的患者提供單獨的分診模型的有效性。邏輯回歸的驗證是通過先前由Tsai等人開發的ACS分類工具的實施來完成的。 Tsai, et al.（2018)在新數據集上並將其一致性與先前公佈的結果進行比較。由於ACS分診工具以前使用的患者數據並不真正代表急診室人群，因此使用邏輯回歸法開發了一個使用更有效數據集的新模型，同時醫生決定將分類作為預測的依據。通過將其與使用較大訓練數據集制定的其他邏輯回歸模型進行比較以及基於ACS放電診斷的預測來驗證該新開發的模型。還開發了通過救護車和自我運輸到達的患者的個體邏輯回歸模型，並與一般模型進行了比較。事實證明，ACS分類工具具有一致的結果，其公佈的結果表明邏輯回歸是模型開發的可靠方法。使用更具包容性的患者數據集的新邏輯回歸模型由6個重要預測因子組成，表示為：Odds Ratio = - 4.566+ 1.056#westeur024#Age + 0.778#westeur024#Male + 2.24#westeur024#Chest Discomfort + 1.365#westeur024#Shock + 0.805#westeur024#Proximal Radiation Pain -1.308#westeur024#Arrhythmia 概率（ACS懷疑）的閾值為0.13。對於分類ACS病例，這表現出95％靈敏度，13％特異性和65％以下面積的表現，優於其他6個測試模型(Chest Pain Strategy, Triage Flowchart, Zarich’s Strategy, HBI Checklist and Modified HBI Checklist 1&2)。此外，邏輯回歸模型證明足夠隨著訓練數據集增加的開發模型和基於ACS放電診斷的模型沒有顯著改善（p> 0.05）。同時，針對通過特定到達模式（自我到達和救護車/ 119）到達的患者的專用分診模型預計在性能改善方面具有良好潛力。目前的結果必須謹慎進行，因為通過救護車到達的患者現有少量數據（119）。因此，對於119名患者進行模型開發的進一步數據收集被認為是有益的。預計這將提高模型的可靠性，並開發具有增強的分類功能的專用119分類模型。

Acute Coronary Syndrome (ACS) patient management in the Emergency Department (ED) is known to be a challenging task as majority of patients do not present clear-cut evidence of this condition. This leads to difficulty in identifying patients who should be prioritized for thorough diagnoses in the Observation Unit (OU) and who can safely wait in the regular ED queue. This study aimed to validate and use Logistic Regression as the method for developing a set of triaging criteria which can identify patients with considerable or negligible risk of ACS upon presentation. At the same time, evaluate the effectiveness of having a separate triage model for patients arriving via ambulance (119) or self-arrival patients. Validation for Logistic Regression was done through the implementation of the ACS Triage Tool, previously developed by Tsai et al. (2018), on a new dataset and compared its consistency with the previously published result. Since the ACS Triage Tool previously utilized patient data which was not truly representative of the ED population, a new model using a more valid dataset was developed using Logistic Regression along with physician’s decision for triaging as basis for prediction. Validation of this newly developed model was done through comparing it with other Logistic Regression models formulated with a larger training dataset as well as prediction based on ACS discharge diagnosis. Individual Logistic Regression models for patients arriving via ambulance and self-transportation were also developed and compared with the general model. The ACS Triage Tool proved to have consistent results with its published results indicating that Logistic Regression is a reliable method for model development. The new Logistic Regression model which used a more inclusive set of patient data was comprised of 6 significant predictors and expressed as: Odds Ratio = - 4.566+ 1.056#westeur024#Age + 0.778#westeur024#Male + 2.24#westeur024#Chest Discomfort + 1.365#westeur024#Shock + 0.805#westeur024#Proximal Radiation Pain -1.308#westeur024#Arrhythmia with threshold value of 0.13 for Probability (ACS suspicion). This yielded a performance of 95% sensitivity, 13% specificity, and Area Under Curve of 65% for triaging ACS cases which were superior than other 6 models tested (Chest Pain Strategy, Triage Flowchart, Zarich’s Strategy, HBI Checklist and Modified HBI Checklist 1&2). Furthermore, the logistic regression model proved to be sufficient as developed models with increased training dataset and ACS discharge diagnosis-based model did not result to significant improvements (p>0.05). Meanwhile, a dedicated triage model for patients arriving via a specific mode-of-arrival (self-arrival and ambulance/119) is projected to have good potential in performance improvement. Current results have to be proceeded with caution due to the small amount of data available on-hand for patients arriving via ambulance (119). Therefore, further collection of data for 119 patients for model development is seen to be beneficial. This is foreseen to improve the reliability of the model and develop a dedicated 119 triage model with enhanced triaging capabilities.

Acknowledgements ii
Abstract iii
List of Figures ix
List of Tables xi
Chapter 1: Introduction 1
1.1 Background and Motivation of the Study 1
1.2 Research Objectives 5
1.3 Research Limitations 7
1.4 Research Structure 8
Chapter 2: Review of Related Literature 10
2.1 Introduction of Acute Coronary Syndrome (ACS) 10
2.2 Medical Diagnosis of ACS 11
2.2.1 Current Practice 11
2.2.2 Limitations of current medical evaluation 13
2.3 Current Risk Scoring Systems 13
2.3.1 HEART Risk Score 14
2.3.2 North American Chest Pain Rule (NACPR) 15
2.3.3 Emergency Department Assessment of Chest Pain Score - Accelerated Diagnostic Protocol (EDACS - ADP) 16
2.3.4 Limitation of Current Risk Scoring Systems 17
2.4 Symptoms and Risk Factors of ACS 18
2.4.1 Symptoms of ACS 18
2.4.2 Risk Factors of ACS 19
2.4.3 Studies for predicting ACS occurrence 22
2.4.4 Synthesis and Limitations of current studies 24
2.5 Current Triage Models 27
2.5.1 Chest Pain Strategy 27
2.5.2 Zarich’s strategy 28
2.5.3 Triage Flowchart 28
2.5.4 Heart Broken Index (HBI) 29
2.5.5 ACS Triage Model 30
2.5.6 Modified HBI Checklist 31
2.5.7 Comparison of current triage models 32
2.6 Methods for Model Development 35
2.6.1 Logistic Regression 35
2.6.2 Signal Detection Theory 36
2.6.3 Receiver Operating Curve (ROC) 37
Chapter 3: Methodology 39
3.1 Determination of Conditions for Data Collection 40
3.2 Data Collection and Cleaning 43
3.3 Validation of ACS Triage Tool 45
3.4 Logistic Regression Model Development 45
3.4.1 Split Data 48
3.4.3 Adjust Threshold 50
3.5 Testing of Other Models 52
3.5.1 Chest Pain Strategy 52
3.5.2 Zarich’s Strategy 52
3.5.3 Triage Flowchart 52
3.5.4 Heart Broken Index (HBI) 53
3.5.5 Modified HBI Checklist 54
3.6 Performance Evaluation on Corresponding Test Data 55
3.6.1 Proportions of Test Data Used 56
3.6.2 Models with Corresponding Test Data 56
3.6.3 Performance Measures 57
3.7 Comparison and Analysis 58
3.7.1 Model Validation of Existing Model (ACS Triage Tool) 61
3.7.2 Logistic Regression Models’ Performance Validation 62
3.7.3 Increase in Logistic Regression Model’s Training Dataset Validation 64
3.7.4 Mode of Arrival-specific Logistic Regression Model Validation 64
3.7.5 Discharge Diagnosis based Training Model 65
Chapter 4: Results 67
4.1 Data Characteristics 67
4.1.1 Overall Data Characteristics 67
4.1.2 Data Characteristics Based on Admission to Observation Unit (OU) and ACS Diagnosis 68
4.1.3 Current Performance of ED Triage 69
4.1.4 Data Characteristics based on Patient’s Mode of Arrival 70
4.2 Validation of Existing Model (ACS Triage Tool) 72
4.2.1 Comparison of Published Result and Newly Tested Data 72
4.2.2 Consistent Prediction of ACS and OU Cases 74
4.3.1 Building the Logistic Regression Models 75
4.3.2 Threshold Adjustment 82
4.4 Logistic Regression Model (Stage 2, OU) Performance Validation 85
4.4.1 LR2 (OU) Model Comparison with ACS Triage Tool 85
4.4.2 Consistent Prediction of ACS and OU cases 88
4.4.3 Stage 2 Logistic Regression Model Performance Comparison with Other Models 89
4.5 Increased Training Dataset for Logistic Regression Model Validation 95
4.5.1 ACS Prediction 96
4.5.2 OU Prediction 97
4.6 Mode of Arrival-specific Logistic Regression Model Validation 98
4.6.1 Self-arrival and General Logistic Regression Model Comparison 98
4.6.2 Ambulance (119) and General Logistic Regression Model Comparison 101
4.7 Discharge Diagnosis based Logistic Regression Model 104
Chapter 5: Discussion 108
5.1 Validation of Existing Model (ACS Triage Tool) 108
5.2 Evaluation of the Logistic Regression Model 109
5.2.1 Predictors in the Newly Developed Model 110
5.2.2 Evaluation of Logistic Regression Model’s Performance with Other Triage Models 111
5.3 Validation of the Logistic Regression Model’s Sufficiency 114
5.3.1 Effect of Increasing Data for Model Development 114
5.3.2 Comparison of Discharge Diagnosis-based and Physician’s Judgement-based Model 115
5.4 Assessment of Patient’s Mode of Arrival-specific Models 116
Chapter 6: Conclusion 118
References 119
Appendix 127
Appendix A. Existing Models’ Performance on Stage 3 (ACS), 5 Replications 127
Appendix B. Logistic Regression Models’ Performance on Stage 3 (ACS), 5 Replications 127
Appendix C. Other Models’ Performance on Stage 3 (ACS), 5 Replications 128
Appendix D. Logistic Regression Models’ Performance on Stage 3 119 (ACS), 3 Replications 129
Appendix E. Logistic Regression Models’ Performance on Stage 3 self-arrival (ACS), 5 Replications 129
Appendix F. Existing Models’ Performance on Stage 3 (OU), 5 Replications 130
Appendix G. Logistic Regression Models’ Performance on Stage 3 (OU), 5 Replications 130
Appendix H. Other Models’ Performance on Stage 3 (OU), 5 Replications 131
Appendix I. Logistic Regression Models’ Performance on Stage 3 119 (OU), 3 Replications 132
Appendix J. Logistic Regression Models’ Performance on Stage 3 self-arrival (OU), 5 Replications 132

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