請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99839完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 簡國龍 | zh_TW |
| dc.contributor.advisor | Kuo-Liong Chien | en |
| dc.contributor.author | 黃琬茹 | zh_TW |
| dc.contributor.author | Wan-Ru Huang | en |
| dc.date.accessioned | 2025-09-18T16:09:45Z | - |
| dc.date.available | 2025-09-19 | - |
| dc.date.copyright | 2025-09-18 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-03-18 | - |
| dc.identifier.citation | [1] 花蓮縣民政處戶政科 (2024年9月2日)。花蓮縣各鄉鎮市人口數及原住民人口比率統計表。
[2] 客家委員會(2022年5月)。110 年全國客家人口暨語言基礎資料調查研究。 [3] Jones CA, Parker TS, Ahearn M, Mishra AK, Variyam JN. Health Status and Health Care Access of Farm and Rural Populations. Washington, DC: US Department of Agriculture, Economic Research Service (USDA), 2009. [4] 花蓮縣政府(2023年6月)。111年花蓮縣主要死因概況(含男、女性比較)統計分析。 [5] 衛生福利部(2022年12月)。中華民國110 年醫療機構現況及醫院醫療服務量統計。 [6] 基督教門諾會醫院，門諾簡介(2023年12月14日)。檢自https://www.mch.org.tw/Docs/270/Default.aspx (2024年9月17 日) [7] 中華民國內政部消防署救災救護組(2021年6月11日)。109年各直轄市、縣(市)消防機關緊急救護無生命徵象傷病患急救成功人數統計表。 [8] 中華民國內政部消防署救災救護組(2022年3月23日)。110年緊急救護無生命徵象傷病患急救成功人數統計一覽表。 [9] 中華民國內政部消防署救災救護組(2023年5月4日)。各直轄市、縣(市)消防局111年緊急救護到院前心肺功能停止傷病患急救成功人數統計表。 [10] 中華民國內政部消防署救災救護組(2024年1月26日)。各直轄市、縣(市)消防局及本署港務消防大隊112年緊急救護到院前心肺功能停止傷病患急救成功人數統計表。 [11] Berdowski J, Berg RA, Tijssen JG, Koster RW. Global incidences of out-of hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies. Resuscitation. 2010;81:1479-87. [12]Hayashi M, Shimizu W, Albert CM. The spectrum of epidemiology underlying sudden cardiac death. Circ Res. 2015;116:1887-906. [13] Myat A, Song KJ, Rea T. Out-of-hospital cardiac arrest: current concepts. Lancet. 2018;391:970-9. [14] Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL et al. American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024;149:e347-e913. [15] Milan M, Perman SM. Out of Hospital Cardiac Arrest: A Current Review of the Literature that Informed the 2015 American Heart Association Guidelines Update. Curr Emerg Hosp Med Rep. 2016;4:164-71. [16] C Holmgren, NJ Abdon, L Bergfeldt, N Edvardsson, J Herlitz, T Karlsson et al. Out-of-hospital cardiac arrest: Causes according to autopsy and electrocardiography – Analysis of 781 patients with neither hospital care nor prescribed medication during the preceding two years. Resuscitation. 2020;150:65-71. [17] Wittwer MR, Zeitz C, Beltrame JF, Arstall MA. Aetiology of resuscitated out-of-hospital cardiac arrest treated at hospital. Resuscitation. 2022;170:178-83. [18] J Engdahl, M Holmberg, BW Karlson, R Luepker, J Herlitz. The epidemiology of out-of-hospital ‘sudden’ cardiac arrest. Resuscitation. 2002;52:235-45. [19] Gräsner JT, Herlitz J, Tjelmeland IBM, Wnent J, Masterson S, Lilja G et al.European Resuscitation Council Guidelines 2021: Epidemiology of cardiac arrest in Europe. Resuscitation. 2021;161:61-79. [20] Zheng J, Lv C, Zheng W, Zhang G, Tan H, Ma Y et al. Incidence, process of care, and outcomes of out-of-hospital cardiac arrest in China: a prospective study of the BASIC-OHCA registry. The Lancet Public Health. 2023;8:e923-32. [21] Lazzarin T, Tonon CR, Martins D, Fávero EL Jr, Baumgratz TD, Pereira FWL, et al. Post-Cardiac Arrest: Mechanisms, Management, and Future Perspectives. J Clin Med. 2022;12:259. [22] Krnjević K. Early effects of hypoxia on brain cell function. Croat Med J. 1999;40:375-80. [23] Buunk G, van der Hoeven JG, Meinders AE. Cerebral blood flow after cardiac arrest. Neth J Med. 2000;57:106-12. [24] Sandroni C, Cronberg T, Sekhon M. Brain injury after cardiac arrest: pathophysiology, treatment, and prognosis. Intensive Care Med. 2021;47:1393-414. [25] Daniele SG, Trummer G, Hossmann KA, Vrselja Z, Benk C, Gobeske KT et al. Brain vulnerability and viability after ischaemia. Nat Rev Neurosci. 2021;22:553–72. [26] Panchal AR, Bartos JA, Cabañas JG, et al. Part 3: adult basic and advanced life support: 2020 American heart association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation. 2020;142:S366-468. [27] Chan PS, Krumholz HM, Nichol G, Nallamothu BK; American Heart Association National Registry of Cardiopulmonary Resuscitation Investigators. Delayed time to defibrillation after in-hospital cardiac arrest. N Engl J Med. 2008;358:9-17. [28] Berg, KM, Soar J, Andersen LW, Böttiger BW, Cacciola S, Callaway CW, et al. Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations. Circulation 2020;142:S92-S139. [29] Belur AD, Sedhai YR, Truesdell AG, Khanna AK, Mishkin JD, Belford PM et al. Targeted Temperature Management in Cardiac Arrest: An Updated Narrative Review. Cardiol Ther. 2023;12:65-84. [30] Nolan JP, Sandroni C, Bo¨ttiger BW, Cariou A, Cronberg T, Friberg H et al. European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care. Intensive Care Med. 2021;47:369-421. [31] Chiu WT, Lin KC, Tsai MS, Hsu CH, Wang CH, Kuo LK et al. Post-cardiac arrest care and targeted temperature management: A consensus of scientific statement from the Taiwan Society of Emergency & Critical Care Medicine, Taiwan Society of Critical Care Medicine and Taiwan Society of Emergency Medicine. J Formos Med Assoc. 2021;120:569-87. [32] Bisht A, Gopinath A, Cheema AH, Chaludiya K, Khalid M, Nwosu M, Agyeman WY, Arcia Franchini AP. Targeted Temperature Management After Cardiac Arrest: A Systematic Review. Cureus. 2022;14:e29016. [33] Buick, JE, Drennan IR, Scales DC, Brooks SC, Byers A, Cheskes S et al. Improving Temporal Trends in Survival and Neurological Outcomes After Out-of-Hospital Cardiac Arrest. Circ. Cardiovasc. Qual. Outcomes 2018;11:e003561. [34] Yan S, Gan Y, Jiang N, Wang R, Chen Y, Luo Z et al. The global survival rate among adult out-of-hospital cardiac arrest patients who received cardiopulmonary resuscitation: a systematic review and meta-analysis. Crit Care. 2020;24:61. [35] Ong ME, Shin SD, De Souza NN, Tanaka H, Nishiuchi T, Song KJ et al. Outcomes for out-of-hospital cardiac arrests across 7 countries in Asia: The Pan Asian Resuscitation Outcomes Study (PAROS). Resuscitation. 2015;96:100-8. [36] Gowens P, Smith K, Clegg G, Williams B, Nehme Z. Global variation in the incidence and outcome of emergency medical services witnessed out-of-hospital cardiac arrest: A systematic review and meta-analysis. Resuscitation. 2022;175:120-32. [37] Geri G, Scales DC, Koh M, Wijeysundera HC, Lin S, Feldman M et al. Healthcare costs and resource utilization associated with treatment of out-of-hospital cardiac arrest. Resuscitation. 2020;153:234-42. [38] Adrie C, Cariou A, Mourvillier B, Laurent I, Dabbane H, Hantala F et al. Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score. Eur Heart J. 2006;27:2840-5. [39] Guy A, Kawano T, Besserer F, Scheuermeyer F, Kanji HD, Christenson J et al. The relationship between no-flow interval and survival with favourable neurological outcome in out-of-hospital cardiac arrest: Implications for outcomes and ECPR eligibility. Resuscitation. 2020;155:219-225. [40] Feinstein AR. The pre-therapeutic classification of co-morbidity in chronic disease. J Chronic Dis. 1970;23:455-68. [41] Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining. comorbidity: implications for understanding health and health services. Ann Fam Med. 2009;7:357-63. [42] Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373-83. [43] Qu WF, Zhou PY, Liu WR, Tian MX, Jin L, Jiang XF et al. Age-adjusted Charlson Comorbidity Index predicts survival in intrahepatic cholangiocarcinoma patients after curative resection. Ann Transl Med. 2020;8:487. [44] Koseki Y, Hikage M, Fujiya K, Kamiya S, Tanizawa Y, Bando E et al. Utility of a modified age-adjusted Charlson Comorbidity Index in predicting cause-specific survival among patients with gastric cancer. Eur J Surg Oncol. 2021;47:2010-5. [45] Zhou S, Zhang XH, Zhang Y, Gong G, Yang X, Wan WH. The Age-Adjusted Charlson Comorbidity Index Predicts Prognosis in Elderly Cancer Patients. Cancer Manag Res. 2022;14:1683-91. [46] Hallstrom AP, Cobb LA, Yu BH. Influence of comorbidity on the outcome of patients treated for out-of-hospital ventricular fibrillation. Circulation. 1996;93:2019-22. [47] Carew HT, Zhang W, Rea TD. Chronic health conditions and survival after out-of-hospital ventricular fibrillation cardiac arrest. Heart. 2007;93:728-31. [48] Hirlekar G, Jonsson M, Karlsson T, Hollenberg J, Albertsson P, Herlitz J. Comorbidity and survival in out-of-hospital cardiac arrest. Resuscitation. 2018;133:118-23. [49] Winther-Jensen M, Kjaergaard J, Nielsen N, Kuiper M, Friberg H, Søholm H et al. Comorbidity burden is not associated with higher mortality after out-of-hospital cardiac arrest*. Scandinavian Cardiovascular Journal. 2016;50:305-10. [50] Terman SW, Shields TA, Hume B, Silbergleit R. The influence of age and chronic medical conditions on neurological outcomes in out of hospital cardiac arrest. Resuscitation. 2015;89:169-76. [51] Huang W, Teo GKW, Tan JW, Ahmad NS, Koh HH, Ong MEH. Influence of comorbidities and clinical prediction model on neurological prognostication post out-of-hospital cardiac arrest. Heart Asia. 2018;10:e011016. [52] Zhang N, Lin Q, Jiang H, Zhu H. Age-adjusted Charlson Comorbidity Index as. effective predictor for in-hospital mortality of patients with cardiac arrest: a retrospective study. BMC Emerg Med. 2023;23:7. [53] Møller SG, Rajan S, Folke F, Hansen CM, Hansen SM, Kragholm K et al. Temporal trends in survival after out-of-hospital cardiac arrest in patients with and without underlying chronic obstructive pulmonary disease. Resuscitation. 2016;104:76-82. [54] Pak JE, Kim KH, Shin SD, Song KJ, Hong KJ, Ro YS et al. Association between chronic liver disease and clinical outcomes in out-of-hospital cardiac arrest. Resuscitation. 2021;158:1-7. [55] Hsieh MS, Chattopadhyay A, Lu TP, Liao SH, Chang CM, Lee YC et al. Effect of end-stage kidney disease on the return of spontaneous circulation in Taiwanese adults with out-of-hospital cardiac arrest. Sci Rep. 2023;13:7905. [56] Huang TL, Lee AF, Chien YC, Lin CH, Lee BC, Chung YT et al. Emergency Medical Services in Taiwan: Past, Present, and Future. J Acute Med. 2023;13:91-103. [57] Chien YS, Tsai MS, Huang CH, Lai CH, Huang WC, Chan L et al. Outcomes of Targeted Temperature Management for In-Hospital and Out-Of-Hospital Cardiac Arrest: A Matched Case-Control Study Using the National Database of Taiwan Network of Targeted Temperature Management for Cardiac Arrest (TIMECARD) Registry. Med Sci Monit. 2021;27:e931203. [58] Chang HC, Tsai MS, Kuo LK, Hsu HH, Huang WC, Lai CH et al. Factors affecting outcomes in patients with cardiac arrest who receive target temperature management: The multi-center TIMECARD registry. J Formos Med Assoc. 2022;121:294-303. [59] Chi CY, Tsai MS, Kuo LK, Hsu HH, Huang WC, Lai CH et al. Post-resuscitation diastolic blood pressure is a prognostic factor for outcomes of cardiac arrest patients: a multicenter retrospective registry-based analysis. J Intensive Care. 2022;10:39. [60] Liou FY, Tsai MS, Kuo LK, Hsu HH, Lai CH, Lin KC et al. A Study on the Outcome of Targeted Temperature Management Comparing Cardiac Arrest Patients Who Received Bystander Cardiopulmonary Resuscitation With Those Who Did Not, Using the Nationwide TIMECARD Multicenter Registry. Front Med (Lausanne). 2022;9:779781. [61] Chiu WT, Chan L, Masud JHB, Hong CT, Chien YS, Hsu Ch et al. Identifying Risk Factors for Prolonged Length of Stay in Hospital and Developing Prediction Models for Patients with Cardiac Arrest Receiving Targeted Temperature Management. Rev Cardiovasc Med. 2023;24:55. [62] Lin JJ, Huang CH, Chien YS, Hsu CH, Chiu WT, Wu Ch et al. TIMECARD score: An easily operated prediction model of unfavorable neurological outcomes in out-of-hospital cardiac arrest patients with targeted temperature management. J Formos Med Assoc. 2023;122:317-27. [63] Brain Resuscitation Clinical Trial I Study Group (BRCT-I). Randomized clinical study of thiopental loading in comatose survivors of cardiac arrest. N Engl J Med, 1986;314:397-403. [64] Brain Resuscitation Clinical Trial II Study Group (BRCT-II). A randomized clinical trial of calcium entry blocker administration to comatose survivors of cardiac arrest: design, methods and patient characteristics. Control Clin Trials, 1992;12:525-45. [65] Yule GU. On the methods of measuring association between two attributes. J. R. Stat. Soc. 1912;75: 579-652. [66] Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008; 27:157-172. [67] Leening MJ, Vedder MM, Witteman JC, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide. Ann Intern Med. 2014;160:122-31. [68] Alexandre Prehn Zavascki, Sandra Costa Fuchs. The need for reappraisal of AIDS score weight of Charlson comorbidity index. Journal of Clinical Epidemiology. 2007;60:867-68. [69] 衛生福利部統計處(2022年7月)。110年我國愛滋感染者之相關性別統計分析。 [70] Berbudi A, Rahmadika N, Tjahjadi AI, Ruslami R. Type 2 Diabetes and its Impact on the Immune System. Curr Diabetes Rev. 2020;16:442-449. [71] Tuchendler E, Tuchendler PK, Madej G. Immunodeficiency caused by cirrhosis. Clin Exp Hepatol. 2018;4:158-164. [72] Syed-Ahmed M, Narayanan M. Immune Dysfunction and Risk of Infection in Chronic Kidney Disease. Adv Chronic Kidney Dis. 2019;26:8-15. [73] Cunningham CA, Coppler PJ, Skolnik AB. The immunology of the post-cardiac arrest syndrome. Resuscitation. 2022;179:116-123. [74] Norhammar A, Malmberg K, Diderholm E, Lagerqvist B, Lindahl B, Rydén L, Wallentin L. Diabetes mellitus: the major risk factor in unstable coronary artery disease even after consideration of the extent of coronary artery disease and benefits of revascularization. J Am Coll Cardiol. 2004;43:585-91. [75] Einav S, Cortegiani A, Marcus EL. Cardiac arrest in older adult patients. Curr Opin Anaesthesiol. 2021;34:40-47. [76] Iris Oving, Laura H.P.I van Dongen, Suzanne C. Deurholt, Amal Ramdani, Stefanie G. Beesems, Hanno L. Tan et al. Comorbidity and survival in the pre-hospital and in-hospital phase after out-of-hospital cardiac arrest. Resuscitation. 2020;153:58-64. [77] Hong SI, Kim YJ, Kim YJ, Kim WY. Pre-arrest comorbidity burden and the future risk of out-of-hospital cardiac arrest in Korean adults. Heart. 2023;109:542-7. [78] National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes. 1979; 28:1039-57. [79] International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009; 32:1327-34. [80] Hjalmarsson A, Rawshani A, Råmunddal T, Rawshani A, Hjalmarsson C, Myredal A et al. No obesity paradox in out-of-hospital cardiac arrest: Data from the Swedish registry of cardiopulmonary resuscitation. Resusc Plus. 2023;15:100446. [81] Aoki M, Aso S, Suzuki M, Tagami T, Sawada Y, Yasunaga H et al. Association between obesity and neurological outcomes among out-of-hospital cardiac arrest patients: The SOS-KANTO 2017 study. Resusc Plus. 2023;17:100513. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99839 | - |
| dc.description.abstract | 背景
全球到院前心肺功能停止的死亡率跟不良神經學預後普遍高，識別具有良好出院預後潛力的患者對於優化臨床決策和資源分配相當重要。本研究旨在探討共病症對到院前心肺功能停止預後的影響，並評估其預測潛力。 研究材料與方法 本研究為多中心的回溯性世代研究，資料採用2014年到2019年間進行的 Taiwan Network of Targeted Temperature Management for Cardiac Arrest (TIMECARD) registry，受試者為經歷非外傷所致到院前心肺功能停止後，接受目標體溫管理的成人患者。本研究發展修訂版年齡調整後查爾森共病症指數量化受試者共病症負擔，並依此分成四組。使用多變量羅吉斯迴歸分析單一共病症、修訂版年齡調整後查爾森共病症指數與院內死亡率、出院時不良神經學預後的關聯。此外，透過比較基本模型跟納入修訂版年齡調整後查爾森共病症指數模型間的ROC曲線下面積，以評估修訂版年齡調整後查爾森共病症指數的預測力增益。 結果 本研究共分析375名受試者。多變量羅吉斯迴歸分析顯示糖尿病(調整後勝算比，1.67，95%信賴區間，1.01-2.76)、心衰竭(調整後勝算比，2.85，95%信賴區間，1.36-5.95)、需透析之末期腎病(調整後勝算比，2.47，95%信賴區間，1.03-5.92)均與院內死亡顯著相關。較高的修訂版年齡調整後查爾森共病症指數與院內死亡率增加相關 [修訂版年齡調整後查爾森共病症指數 0-1組相對於修訂版年齡調整後查爾森共病症指數≥6組(調整後勝算比，4.93，95%信賴區間，1.62-14.94，趨勢P值=0.003)]。此外心衰竭(調整後勝算比，2.78， 95%信賴區間，1.01-7.64)亦與出院時不良神經學預後相關。較高的修訂版年齡調整後查爾森共病症指數和出院時不良神經學預後風險增加相關[修訂版年齡調整後查爾森共病症指數 0-1組相對於修訂版年齡調整後查爾森共病症指數≥6組(調整後勝算比，4.78，95%信賴區間，1.08-21.12，趨勢P值= 0.02)。基本模型與納入修訂版年齡調整後查爾森共病症指數的模型在院內死亡的AUC差異為0.01 (95%信賴區間，-0.004-0.03，P=0.16)，而在出院時不良神經學預後的AUC差異為0.008 (95%信賴區間，-0.004-0.02，P=0.21)，顯示加入修訂版年齡調整後查爾森共病症指數未能顯著提升預測效能。 結論 特定共病症、修訂版年齡調整後查爾森共病症指數與接受目標體溫管理的成人非外傷所致到院前心肺功能停止後患者的院內死亡率及出院時不良神經學預後相關。然而將修訂版年齡調整後查爾森共病症指數納入預測模型未能提高預測效力，有待進一步研究以闡明共病症對此族群之風險分層。 | zh_TW |
| dc.description.abstract | Background: Mortality and unfavorable neurological outcomes following out-of-hospital cardiac arrest (OHCA) remain high worldwide. Identifying patients with a favorable prognosis is crucial for optimizing clinical decision-making and resource allocation. This study aimed to investigate the effect of comorbidity on post-OHCA outcomes and evaluate its prognostic potential.
Methods: This multicenter retrospective cohort study enrolled adult non-traumatic OHCA patients receiving targeted temperature management (TTM) through the Taiwan Network of Targeted Temperature Management for Cardiac Arrest (TIMECARD) registry from 2014 to 2019. Patients were grouped into 4 categories based on the modified Age-adjusted Charlson Comorbidity Index (mACCI), which quantifies comorbid burden. Multivariable logistic regression was employed to evaluate the associations between individual comorbidities, as well as the mACCI, and both in-hospital mortality and unfavorable neurological outcomes at hospital discharge. The incremental prognostic value of the mACCI was assessed by comparing the area under the receiver operating characteristic curve (AUC) between the basic model and the mACCI-incorporated model. Results: A total of 375 patients were analyzed. Multivariable logistic regression identified diabetes mellitus (adjusted odds ratio [aOR], 1.67, 95% confidence interval [CI], 1.01-2.76), heart failure (aOR, 2.85, 95% CI, 1.36-5.95) and end stage renal disease under dialysis (aOR, 2.47, 95% CI, 1.03-5.92) were consistently associated with in-hospital mortality. Additionally, a higher mACCI was linked to an increased risk of in-hospital mortality, with an aOR of 4.93 (95% CI, 1.62-14.94, P for trend=0.003) for the mACCI ≥6 group compared to the mACCI 0-1 group. Moreover, heart failure (aOR, 2.78, 95% CI, 1.01-7.64) was consistently associated with unfavorable neurological outcomes at hospital discharge. Similarly, a higher mACCI was associated with an increased risk of unfavorable neurological outcomes at hospital discharge, with an aOR of 4.78 (95% CI, 1.08-21.12, P for trend=0.020) for the mACCI ≥6 group compared to the mACCI 0-1 group. The AUC difference between the basic model and mACCI-incorporated model was 0.01 (95% CI, -0.004-0.03, P=0.16) for in-hospital mortality and 0.008 (95% CI, -0.004-0.02, P=0.21) for unfavorable neurological outcomes at hospital discharge, indicating that adding the mACCI to the prognostic mode did not significantly improve predictive performance. Conclusion: Specific comorbidities and the mACCI were related to in-hospital mortality and unfavorable neurologic outcomes at hospital discharge in adult non-traumatic OHCA patients treated with TTM. However, the addition of the mACCI did not enhance prognostic model performance, highlighting the need for further research to elucidate the risk stratification of comorbidity in this population. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-18T16:09:45Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-18T16:09:45Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
中文摘要……………………………………………………………………………i Abstract.….……………………………………………………………………iii 目次…………………………………………………………………………………v 圖次………………………………………………………………………………viii 表次…………………………………………………………………………………ix 第一章 前言………………………………………………………………………1 1.1 花蓮環境…………………………………………………………………1 1.2 花蓮醫療現況……………………………………………………………1 1.3 臺灣基督教門諾會醫療財團法人門諾醫院暨急診醫學科介紹.………2 1.4 花蓮縣到院前心肺功能停止救護的挑戰………………………………2 第二章 背景介紹……………………………………………………………………4 2.1 到院前心肺功能停止……………………………………………………4 2.1.1 流行病學概況……………………………………4 2.1.2 病生理機轉…………………………………………………………4 2.1.3 生存之鏈……………………………………………………………5 2.1.4 目標體溫管理以及急救復甦決策…………………………………6 2.1.5 預後因素以及其影響…..…………………………………………8 2.2 共病症……………………………………………………………………9 2.2.1 共病症定義以及查爾森共病症指數介紹…………………………9 2.2.2 共病症與到院前心肺功能停止預後之相關的研究成就………11 第三章 知識缺口、研究假設以及研究目標……………………………………15 第四章 研究材料以及方法………………………………………………………17 4.1 研究設計、受試者以及研究資料………………………………………17 4.1.1 實驗設計以及環境………………………………………………17 4.1.2 受試者……………………………………………………………17 4.1.3 資料來源…………………………………………………………18 4.1.4 變項以及定義……………………………………………………19 4.2 納入條件以及排除條件…………………………………………………21 4.3 暴露因子…………………………………………………………………22 4.3.1 共病症……………………………………………………………22 4.3.2 修訂版年齡調整後查爾森共病症指數…………………………22 4.4 主要結果以及次要結果…………………………………………………23 4.5 統計方法…………………………………………………………………24 4.5.1 實驗變項以及干擾因子…………………………………………24 4.5.2 描述性統計………………………………………………………24 4.5.3 分析性統計以及子群分析………………………………………25 4.5.4 相關性分析、ROC曲線下面積、累積判別改善指數、淨重分類改進指數以及敏感度測試………………………………………26 4.5.5 檢定力、樣本數推估以及統計軟體……………………………28 第五章 結果………………………………………………………………………30 5.1 受試者篩選以及分組、mACCI分布……………………………………30 5.2 受試者整體基本特性……………………………………………………30 5.3 受試者分組基本特性……………………………………………………31 5.4 90天Kaplan-Meier存活曲線…..………………………………………31 5.5 共病症之間的Phi係數分析……………………………………………32 5.6 共病症與院內死亡、出院時不良神經學預後之關聯…………………32 5.7 修訂版年齡調整後查爾森共病症指數與院內死亡、出院時不良神經學 預後之關聯……………………………………………………………33 5.8 預測模型的ROC曲線下面積、累積判別改善指數以及淨重分類改進指 數 ………………………………………………………………………34 5.9 子群分析…………………………………………………………………35 5.10 敏感度測試………………………………………………………………36 第六章 討論……………………………………………………………………37 6.1 主要發現…………………………………………………………………37 6.2 與先前研究的比較…………………………………………………37 6.2.1 特定共病症………………………………………………………37 6.2.2 共病症指數以及預後預測………………………………………38 6.3 生物機轉…………………………………………………………………41 6.4 臨床運用以及公共衛生貢獻……………………………………………42 6.5 優勢與限制………………………………………………………………43 6.6 結論………………………………………………………………………45 參考文獻……………………………………………………………………………46 圖次…………………………………………………………………………………57 圖1. 受試者篩選以及分類之流程圖…………………………………………57 圖2. 修訂版年齡調整後查爾森共病症指數於所有受試者之分布…………58 圖3. 4組受試者的90天內Kaplan-Meier存活曲線……………………59 圖4. 兩預測模型於院內死亡的ROC曲線下面積之比較………………60 圖5. 兩預測模型於出院時不良神經學預後的ROC曲線下面積之比較…61 圖6. 院內死亡之子群比較(以死亡人數/子群總人數呈現)…………………62 圖7. 出院時不良神經學預後之子群比較(以死亡人數/子群總人數呈現)…63 表次…………………………………………………………………………………64 表1. 共病症與預後之相關文獻回顧…………………………………………64 表2-A. 年齡調整後查爾森共病症指數………………………………………66 表2-B. 修訂版年齡調整後查爾森共病症指數………………………………66 表3. 受試者基本特性：整體及分組比較……………………………………67 表4. 90天Kaplan-Meier存活曲線的組間比較………………………70 表5. 共病症間的相關性：Phi coefficient……………………………………71 表6. 共病症與院內死亡的相關性……………………………………………72 表7. 共病症與出院時不良神經學預後的相關性……………………………74 表8. 修訂版年齡調整後共病症指數與院內死亡、出院時不良神經學預後的相關性…………………………………………………………………76 表9. 扣除年齡在mACCI當中之權重後分析與預後相關性之敏感度測試…77 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 共病症 | zh_TW |
| dc.subject | 到院前心肺功能停止 | zh_TW |
| dc.subject | 不良神經學預後 | zh_TW |
| dc.subject | 院內死亡 | zh_TW |
| dc.subject | 修訂版年齡調整後查爾森共病症指數 | zh_TW |
| dc.subject | modified Age-adjusted Charlson Comorbidity Index | en |
| dc.subject | unfavorable neurological outcomes | en |
| dc.subject | comorbidity | en |
| dc.subject | out-of-hospital cardiac arrest | en |
| dc.subject | in-hospital mortality | en |
| dc.title | 共病症對接受目標體溫管理的到院前心肺功能停止成人患者之院內死亡及出院時神經學預後的影響 | zh_TW |
| dc.title | The prognostic effects of comorbidity on in-hospital mortality and neurological outcomes after adult out-of-hospital cardiac arrest with targeted temperature management | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 張慶國;王睿;鄧學儒 | zh_TW |
| dc.contributor.oralexamcommittee | Chin-Kuo Chang;Jui Wang;Syue-Ru Deng | en |
| dc.subject.keyword | 到院前心肺功能停止,共病症,修訂版年齡調整後查爾森共病症指數,院內死亡,不良神經學預後, | zh_TW |
| dc.subject.keyword | out-of-hospital cardiac arrest,comorbidity,modified Age-adjusted Charlson Comorbidity Index,in-hospital mortality,unfavorable neurological outcomes, | en |
| dc.relation.page | 77 | - |
| dc.identifier.doi | 10.6342/NTU202500770 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-03-19 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 公共衛生碩士學位學程 | - |
| dc.date.embargo-lift | 2030-03-14 | - |
| 顯示於系所單位： | 公共衛生碩士學位學程 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 未授權公開取用 | 1.22 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。