研究背景：
過往研究指出，許多慢性腎臟病（CKD）患者會被開立抗憂鬱藥品。在台灣，CKD 和末期腎臟病（ESRD）的盛行率和發生率居全球之冠，對台灣的公共衛生和醫療體系帶來了重要的挑戰。但同時，台灣擁有完善的 CKD 照護計畫提供這些患者醫療照護。然而，關於台灣 CKD 患者使用抗憂鬱藥品的處方模式資料仍相對有限。因此，為了深入探討抗憂鬱藥品使用安全性，需要更進一步了解台灣 CKD 患者的抗憂鬱藥品處方模式。此外，抗憂鬱藥品與中風風險之間的關聯性仍不確定，尤其是對於已經面臨較高中風風險的 CKD 患者。為了避免潛在風險，在為 CKD 患者開立抗憂鬱藥品之前，我們應該更加謹慎地評估使用抗憂鬱藥品的適宜性。

研究目的：
這項研究旨在探索台灣不同慢性腎臟病患者使用抗憂鬱藥品的處方模式，並評估其與中風風險之間的相關性。研究的第一個目標是評估參與慢性腎臟病照護計畫的患者中使用抗憂鬱藥品的處方模式。這項回顧性研究分析了慢性腎臟病患者中抗憂鬱藥品處方的發生率、藥物選擇和使用持續時間。研究的第二個目標是進一步評估使用抗憂鬱藥品的慢性腎臟病患者相較於未使用抗憂鬱藥品的患者的中風風險。

研究方法：
這個研究是一項採用新使用者設計的世代研究。利用台灣全民健康保險資料庫，我們篩選出在 2015 年 1 月至 2018 年 12 月間參與慢性腎臟病照護計畫或接受長期透析治療（接受透析治療連續三個月以上）的患者，隊列進入日期（cohort entry date）為加入照護計畫或開始穩定透析的日期。抗憂鬱藥品新使用者在首次處方（指標日， index date）的前一年內必須無任何抗憂鬱藥品處方記錄，並且首次處方日期應在隊列進入日期之後。我們依據患者在指標日時的腎功能狀態將患者分成三組： Early- CKD 組（CKD 第1-2 期合併蛋白尿和第 3a 期）、Pre-ESRD 組（CKD 第 3b-5 期且未接受透析治療）和 ESRD 組（末期腎臟病同時使用透析治療）。此外，在研究的第二個目標中，具有 5 年中風病史或在索引日期之前接受過腎移植的抗憂鬱藥品新使用者將被排除。抗憂鬱藥品新使用者將根據年齡、性別、腎功能、隊列進入日期之年份和索引日期與非使用者進行 1：1 的配對。為了分析抗憂鬱藥品的使用模式，抗憂鬱藥品新使用者會從指標日期開始追蹤，直到抗憂鬱藥品新使用者轉換成不同類型的抗憂鬱藥品、使用多種抗憂鬱藥品的組合、停止使用抗憂鬱藥品、死亡，或者經過了365天。為了評估抗憂鬱藥品使用對中風風險的影響，抗憂鬱藥品新使用者會從指標日起追蹤，直到發生中風、死亡或至多 180 天。

為了呈現 CKD 患者使用抗憂鬱藥品的比例，我們計算了抗憂鬱藥品的使用盛行率和新使用率，此外，我們使用桑基圖來展示抗憂鬱藥品的處方模式。我們使用 Cox 比例風險回歸模型（cox proportional hazards regression model），特別是 subdistribution hazard model 來解決競爭風險。類別變項以數字和百分比表示，連續變項則以平均值和標準差或中位數和四分位數範圍表示，並使用卡方檢定和t檢定進行組間比較。我們使用標準化平均差異（SMD）作為統計方法進行描述性分析，以檢測組間的不平衡，當SMD超過 0.10 時表示組間存在不平衡。

研究結果：
1. 抗憂鬱藥品處方型態：
觀察期間，共有 778,481 名患者參加了 CKD 照護計畫或穩定透析。其中116,315 名患者符合抗憂鬱藥品新使用者的納入條件。這些患者的平均年齡為 68.5歲，男性佔 48.7%。研究結果顯示，在CKD患者中，抗憂鬱藥品的處方比例為22.4%，新使用率則為 55.7/1000 人年。此外，隨著腎功能惡化，抗憂鬱藥品的新使用率漸漸上升，從 early-CKD 組的 53.18/1000 人年， pre-ESRD 組的 65.13/1000 人年，增加到 ESRD 組的 78.85/1000 人年。從隊列進入日期到首次處方抗憂鬱藥品的中位時間在 ESRD 組為 369 天（IQR 151-730），early-CKD 組為 514 天（IQR 233-924，SMD=0.28），pre-ESRD 組為 522 天（IQR 232-939，SMD=0.3）。

最常被處方的類別是三環抗憂鬱藥 (tricyclic antidepressants)，佔 early-CKD 組的 45.3%，pre-ESRD 組的 43.2% 和 ESRD 組的 38.8%。選擇性血清素回收抑制劑（SSRI）和選擇性5-羥色胺回收抑制劑（SARI）在三個腎功能分組中各佔約 20%。在處方觀察期間，平均處方使用時間為 63 天（±SD 93）。有 79.6% 的患者停用了抗憂鬱藥品，持續使用藥物一年的比例為 10.8%，轉換至其他抗憂鬱藥品的比例為 5.8%。另有1.3% 的患者轉換成同時使用兩種以上不同抗憂鬱藥品。

2. 抗憂鬱藥品中風風險分析：
在 CKD 病患者中，總共收錄 75,007 位新使用抗憂鬱藥品的患者與 75,007 位未使用抗憂鬱藥品的患者。在校正相關變數後存活分析的結果顯示，新使用抗憂鬱藥品的 CKD 患者發生中風的風險顯著高於未使用抗憂鬱藥品的 CKD 患者。其中，中風風險在 early-CKD 組中最高 (aHR, 1.9; 95% CI, 1.68-2.15)，其次是 pre-ESRD 組 (aHR, 1.61; 95% CI, 1.32-1.97) 和 ESRD 組 (aHR, 1.69; 95% CI, 1.09-2.62)。次群體分析的結果顯示，抗憂鬱藥品與缺血性和出血性中風風險較高有顯著關係。此外，不同種類的抗憂鬱藥品發現都會顯著增加病患中風風險。

研究結論：
在這個收錄大型 CKD病患的研究中，我們發現首次使用抗憂鬱藥品的時間距離慢性腎臟病變開始的時間超過一年，而且抗憂鬱藥品的處方盛行率很高，尤其是三環抗憂鬱藥品。此外，我們的研究結果顯示，在腎臟疾病患者中，抗憂鬱藥品的使用可能與中風風險有關。因此，在需要使用抗憂鬱藥品的 CKD 患者中，建議謹慎使用並密切監測。

BACKGROUND
Research has demonstrated that a considerable proportion of individuals diagnosed with chronic kidney disease (CKD) are commonly prescribed antidepressant medications. However, there's limited information available regarding the prescribing of antidepressants for CKD patients in Taiwan, despite Taiwan having the highest prevalence and incidence of CKD and end-stage renal disease (ESRD) globally, with dedicated CKD care programs. Therefore, in order to further explore the safety of antidepressants, it is important to have a better grasp of how antidepressants are prescribed to patients with CKD in Taiwan. Additionally, the link between antidepressants and stroke risk remains uncertain, particularly for CKD patients who are already at a higher risk for stroke. To avoid potential risks, it is essential to evaluate these risks before prescribing antidepressants to CKD patients.

OBJECTIVES
The study was aimed to investigate how antidepressants were prescribed to patients at various stages of CKD in Taiwan, and to evaluate the associated risk of stroke. The first objective was to assess the prescription pattern of antidepressants among CKD patients enrolled in the CKD care programs or were undergoing chronic dialysis. The study examined the incidence of antidepressant prescriptions, drug choice, and duration of use in CKD patients. The second objective aimed to further evaluate the stroke risk in patients using antidepressants compared to those not using antidepressants.

METHOD
The study was a cohort study with new user design. We utilized the Taiwan National Health Insurance Database (NHID) to identify patients who were enrolled in the CKD care programs or were undergoing chronic dialysis (defined as receiving dialysis treatment for at least three successive months) between January 2015 and December 2018. The cohort entry date referred to the date of enrollment in the CKD care programs or the start of stable dialysis treatment. Antidepressant new users were those who did not receive any antidepressant within a year before the first prescription (index date), and the first prescription date should be after the cohort entry date. We stratified patients into three groups based on their renal function at the index date: the early-CKD group (CKD stage 1 to 2 with proteinuria and stage 3a), the pre-ESRD group (CKD stage 3b to 5 without dialysis treatment) or the ESRD group (ESRD with dialysis treatment). In objective 2, antidepressant new users with a 5-year history of stroke or who received a kidney transplant before the index date would be excluded from the study. Antidepressant new users were matched with non-users in a 1:1 ratio based on age, sex, renal function, cohort entry year, and index date. To describe how antidepressants were used, we tracked individuals from the index date until they switched to a different class of antidepressant, used a combination of antidepressants, stopped taking antidepressants, experienced death, or until 365 days had passed. In order to investigate the association between antidepressant use and the risk of stroke, individuals were followed from the index date until they experienced a stroke, died, or until 180 days had passed.

Prevalence and incidence rates were calculated to represent the use of antidepressants in CKD patients. A Sankey diagram was utilized to display the prescription patterns of initial antidepressants. We employed a Cox proportional hazards regression model, specifically the subdistribution hazard model, to address competing risks. Categorical variables were presented as numbers with percentages, while continuous variables were presented as means with standard deviations or medians with interquartile ranges, and compared across groups using chi-squared test and t test, respectively. We used the standardized mean difference (SMD) as a statistical approach in descriptive analysis to examine imbalance between groups, with an SMD above 0.10 indicating imbalance.

RESULT
Objective 1:
Throughout the observation periods, 778,481 patients were enrolled in the CKD care programs or undergoing chronic dialysis. Of these patients, 116,315 fulfilled the inclusion criteria as antidepressant new users (mean age 68.5, 48.7% male). Prevalence of antidepressants prescribing was 22.4%, and the incidence rate was 55.7/1000 person-years in overall CKD population. Furthermore, as renal function deteriorated, the incidence rate of antidepressants increased gradually, from 53.18 per 1000 person-years in the early-CKD group to 65.13 per 1000 person-years in the pre-ESRD group, and 78.85 per 1000 person-years in the ESRD group. Median time from the cohort entry date to the first antidepressants prescription was 369 days (IQR 151-730) in the ESRD group, 514 days (IQR 233-924, SMD= 0.28) in the early-CKD group and 522 days (IQR 232-939, SMD= 0.3) in the pre-ESRD group.

The most commonly prescribed class were tricyclic antidepressants, accounting for 45.3% of the early-CKD group, 43.2% of the pre-ESRD group and 38.8% of the ESRD group. Selective serotonin reuptake inhibitors (SSRIs) and serotonin antagonist and reuptake inhibitors (SARIs) both accounted for approximately 20% of each group. During the observation period, average prescription time of the first antidepressant was 63 days (±SD 94). A significant majority (76.9%) of patients discontinued their use of antidepressant drugs. The proportion of patients who continued using the drugs for one year was 10.8%, while 5.8% switched to other antidepressant drugs. A further 1.3% became combined users.

Objective 2:
Among the CKD patients, 75,007 antidepressant new users were matched with 75,007 non-users. The results of the survival analysis indicated that patients with exposure to antidepressants had a higher probability of stroke compared to those who did not use antidepressants, after fully adjusting for confounding variables. The risk of stroke was highest in the early-CKD group (aHR, 1.9; 95% CI, 1.68-2.15), followed by the pre-ESRD group (aHR, 1.61; 95% CI, 1.32-1.97) and the ESRD group (aHR, 1.69; 95% CI, 1.09-2.62). The results of the subgroup analysis indicated that antidepressants were associated with a higher risk of both ischemic and hemorrhagic strokes. Additionally, regardless of the type of antidepressant prescribed, there was a greater likelihood of stroke.

CONCLUSION
In this large cohort of patients with CKD, we found the initiation of first antidepressants were more than a year since the onset of CKD, and the prevalence of antidepressants prescription was high, especially tricyclic antidepressants. Furthermore, our findings suggest a potential association between antidepressant use and stroke risk in patients with kidney disease. Therefore, it is recommended to use antidepressants with caution and closely monitor patients with CKD when antidepressants are indicated.

1. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709-33. Epub 20200213. doi: 10.1016/s0140-6736(20)30045-3. PubMed PMID: 32061315; PubMed Central PMCID: PMC7049905.
2. United States Renal Data System. 2022 USRDS Annual Data Report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD: , 2022. Report No.
3. 衛生福利部中央健康保險署. 全民健康保險末期腎臟病前期(Pre-ESRD)之病人照護與衛教計畫. (Accessed March 9, 2023).
4. 衛生福利部中央健康保險署. 糖尿病及初期慢性腎臟病照護整合方案. (Accessed March 9, 2023).
5. Davison SN, Levin A, Moss AH, Jha V, Brown EA, Brennan F, et al. Executive summary of the KDIGO Controversies Conference on Supportive Care in Chronic Kidney Disease: developing a roadmap to improving quality care. Kidney Int. 2015;88(3):447-59. Epub 20150429. doi: 10.1038/ki.2015.110. PubMed PMID: 25923985.
6. van Oosten MJM, Koning D, Logtenberg SJJ, Leegte MJH, Bilo HJG, Hemmelder MH, et al. Chronic prescription of antidepressant medication in patients with chronic kidney disease with and without kidney replacement therapy compared with matched controls in the Dutch general population. Clin Kidney J. 2022;15(4):778-85. Epub 20211203. doi: 10.1093/ckj/sfab242. PubMed PMID: 35371442; PubMed Central PMCID: PMC8967542.
7. Iwagami M, Tomlinson LA, Mansfield KE, McDonald HI, Smeeth L, Nitsch D. Prevalence, incidence, indication, and choice of antidepressants in patients with and without chronic kidney disease: a matched cohort study in UK Clinical Practice Research Datalink. Pharmacoepidemiol Drug Saf. 2017;26(7):792-801. Epub 20170411. doi: 10.1002/pds.4212. PubMed PMID: 28397412; PubMed Central PMCID: PMC5516188.
8. Yeh CY, Chen CK, Hsu HJ, Wu IW, Sun CY, Chou CC, et al. Prescription of psychotropic drugs in patients with chronic renal failure on hemodialysis. Ren Fail. 2014;36(10):1545-9. Epub 20140826. doi: 10.3109/0886022x.2014.949762. PubMed PMID: 25154717.
9. Fischer MJ, Xie D, Jordan N, Kop WJ, Krousel-Wood M, Kurella Tamura M, et al. Factors associated with depressive symptoms and use of antidepressant medications among participants in the Chronic Renal Insufficiency Cohort (CRIC) and Hispanic-CRIC Studies. Am J Kidney Dis. 2012;60(1):27-38. Epub 20120411. doi: 10.1053/j.ajkd.2011.12.033. PubMed PMID: 22497791; PubMed Central PMCID: PMC3378778.
10. Hung CC, Lin CH, Lan TH, Chan CH. The association of selective serotonin reuptake inhibitors use and stroke in geriatric population. Am J Geriatr Psychiatry. 2013;21(8):811-5. Epub 20130206. doi: 10.1016/j.jagp.2013.01.018. PubMed PMID: 23567390.
11. Chan CH, Huang HH, Lin CH, Kuan YC, Loh EW, Lan TH. Risk of First Onset Stroke in SSRI-Exposed Adult Subjects: Survival Analysis and Examination of Age and Time Effects. J Clin Psychiatry. 2017;78(8):e1006-e12. doi: 10.4088/JCP.16m11123. PubMed PMID: 28994901.
12. 吳律萱. 台灣抗憂鬱藥品使用與中風風險的關係 2020.
13. Biffi A, Scotti L, Corrao G. Use of antidepressants and the risk of cardiovascular and cerebrovascular disease: a meta-analysis of observational studies. Eur J Clin Pharmacol. 2017;73(4):487-97. Epub 20170109. doi: 10.1007/s00228-016-2187-x. PubMed PMID: 28070601.
14. Trajkova S, d'Errico A, Soffietti R, Sacerdote C, Ricceri F. Use of Antidepressants and Risk of Incident Stroke: A Systematic Review and Meta-Analysis. Neuroepidemiology. 2019;53(3-4):142-51. Epub 20190619. doi: 10.1159/000500686. PubMed PMID: 31216542.
15. Ramasubbu R. Cerebrovascular effects of selective serotonin reuptake inhibitors: a systematic review. J Clin Psychiatry. 2004;65(12):1642-53. doi: 10.4088/jcp.v65n1209. PubMed PMID: 15641869.
16. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney inter., Suppl. 2013; 3: 1–150.
17. Wen CP, Cheng TY, Tsai MK, Chang YC, Chan HT, Tsai SP, et al. All-cause mortality attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan. Lancet. 2008;371(9631):2173-82. doi: 10.1016/s0140-6736(08)60952-6. PubMed PMID: 18586172.
18. Tsai MH, Hsu CY, Lin MY, Yen MF, Chen HH, Chiu YH, et al. Incidence, Prevalence, and Duration of Chronic Kidney Disease in Taiwan: Results from a Community-Based Screening Program of 106,094 Individuals. Nephron. 2018;140(3):175-84. Epub 20180823. doi: 10.1159/000491708. PubMed PMID: 30138926.
19. 財團法人國家衛生研究院. 台灣慢性腎臟病臨床診療指引. 2015.
20. 衛生福利部統計處歷年死因統計。https://dep.mohw.gov.tw/dos/lp-5069-113.html.
21. Wu MY, Wu MS. Taiwan renal care system: A learning health-care system. Nephrology (Carlton). 2018;23 Suppl 4:112-5. doi: 10.1111/nep.13460. PubMed PMID: 30298659.
22. Hsieh HM, Lin MY, Chiu YW, Wu PH, Cheng LJ, Jian FS, et al. Economic evaluation of a pre-ESRD pay-for-performance programme in advanced chronic kidney disease patients. Nephrol Dial Transplant. 2017;32(7):1184-94. doi: 10.1093/ndt/gfw372. PubMed PMID: 28486670.
23. Chen YC, Weng SF, Hsu YJ, Wei CJ, Chiu CH. Continuity of care: evaluating a multidisciplinary care model for people with early CKD via a nationwide population-based longitudinal study. BMJ Open. 2020;10(12):e041149. Epub 20201229. doi: 10.1136/bmjopen-2020-041149. PubMed PMID: 33376170; PubMed Central PMCID: PMC7778764.
24. Lin MT, Hsu CN, Lee CT, Cheng SH. Effect of a Pay-for-Performance Program on Renal Outcomes Among Patients With Early-Stage Chronic Kidney Disease in Taiwan. Int J Health Policy Manag. 2021;11(8):1307-15. Epub 20210413. doi: 10.34172/ijhpm.2021.27. PubMed PMID: 33906336; PubMed Central PMCID: PMC9808322.
25. Lin MY, Chiu YW, Hsu YH, Wu MS, Chang JM, Hsu CC, et al. CKD Care Programs and Incident Kidney Failure: A Study of a National Disease Management Program in Taiwan. Kidney Med. 2022;4(7):100485. Epub 20220521. doi: 10.1016/j.xkme.2022.100485. PubMed PMID: 35812528; PubMed Central PMCID: PMC9257411.
26. Wei SY, Chang YY, Mau LW, Lin MY, Chiu HC, Tsai JC, et al. Chronic kidney disease care program improves quality of pre-end-stage renal disease care and reduces medical costs. Nephrology (Carlton). 2010;15(1):108-15. doi: 10.1111/j.1440-1797.2009.01154.x. PubMed PMID: 20377778.
27. Fraser SD, Taal MW. Multimorbidity in people with chronic kidney disease: implications for outcomes and treatment. Curr Opin Nephrol Hypertens. 2016;25(6):465-72. doi: 10.1097/mnh.0000000000000270. PubMed PMID: 27490909.
28. Tonelli M, Wiebe N, Guthrie B, James MT, Quan H, Fortin M, et al. Comorbidity as a driver of adverse outcomes in people with chronic kidney disease. Kidney International. 2015;88(4):859-66. doi: https://doi.org/10.1038/ki.2015.228.
29. Bowling CB, Plantinga L, Phillips LS, McClellan W, Echt K, Chumbler N, et al. Association of Multimorbidity with Mortality and Healthcare Utilization in Chronic Kidney Disease. J Am Geriatr Soc. 2017;65(4):704-11. Epub 20161123. doi: 10.1111/jgs.14662. PubMed PMID: 27880003.
30. Sullivan MK, Jani BD, Lees JS, Welsh CE, McConnachie A, Stanley B, et al. Multimorbidity and the risk of major adverse kidney events: findings from the UK Biobank cohort. Clin Kidney J. 2021;14(11):2409-19. Epub 20210411. doi: 10.1093/ckj/sfab079. PubMed PMID: 34754437; PubMed Central PMCID: PMC8573008.
31. Rhee CM, Edwards D, Ahdoot RS, Burton JO, Conway PT, Fishbane S, et al. Living Well With Kidney Disease and Effective Symptom Management: Consensus Conference Proceedings. Kidney Int Rep. 2022;7(9):1951-63. Epub 20220630. doi: 10.1016/j.ekir.2022.06.015. PubMed PMID: 36090498; PubMed Central PMCID: PMC9459054.
32. Lui S-F. Patient engagement: what matters to patients. Published 2021. Accessed March, 2023. https://ifkf.org/wp-content/uploads/2022/04/WKD21_Webinar_Lui-SF-v210308.pdf.
33. Metzger M, Abdel-Rahman EM, Boykin H, Song MK. A Narrative Review of Management Strategies for Common Symptoms in Advanced CKD. Kidney Int Rep. 2021;6(4):894-904. Epub 20210210. doi: 10.1016/j.ekir.2021.01.038. PubMed PMID: 33912741; PubMed Central PMCID: PMC8071652.
34. Chiang HH, Guo HR, Livneh H, Lu MC, Yen ML, Tsai TY. Increased risk of progression to dialysis or death in CKD patients with depressive symptoms: A prospective 3-year follow-up cohort study. J Psychosom Res. 2015;79(3):228-32. Epub 20150128. doi: 10.1016/j.jpsychores.2015.01.009. PubMed PMID: 25659439.
35. Tsai YC, Chiu YW, Hung CC, Hwang SJ, Tsai JC, Wang SL, et al. Association of symptoms of depression with progression of CKD. Am J Kidney Dis. 2012;60(1):54-61. Epub 20120410. doi: 10.1053/j.ajkd.2012.02.325. PubMed PMID: 22495469.
36. Tu CY, Chou YH, Lin YH, Huang WL. Sleep and emotional disturbance in patients with non-dialysis chronic kidney disease. J Formos Med Assoc. 2019;118(6):986-94. Epub 20181109. doi: 10.1016/j.jfma.2018.10.016. PubMed PMID: 30416021.
37. Yu IC, Huang JY, Tsai YF. Symptom cluster among hemodialysis patients in Taiwan. Appl Nurs Res. 2012;25(3):190-6. Epub 20110126. doi: 10.1016/j.apnr.2010.11.002. PubMed PMID: 21273045.
38. Hsu HJ, Yen CH, Hsu KH, Wu IW, Lee CC, Hung MJ, et al. Factors associated with chronic musculoskeletal pain in patients with chronic kidney disease. BMC Nephrol. 2014;15:6. Epub 20140108. doi: 10.1186/1471-2369-15-6. PubMed PMID: 24400957; PubMed Central PMCID: PMC3890529.
39. Hedayati SS, Minhajuddin AT, Afshar M, Toto RD, Trivedi MH, Rush AJ. Association between major depressive episodes in patients with chronic kidney disease and initiation of dialysis, hospitalization, or death. Jama. 2010;303(19):1946-53. doi: 10.1001/jama.2010.619. PubMed PMID: 20483971; PubMed Central PMCID: PMC3217259.
40. Kimmel PL, Fwu CW, Abbott KC, Moxey-Mims MM, Mendley S, Norton JM, et al. Psychiatric Illness and Mortality in Hospitalized ESKD Dialysis Patients. Clin J Am Soc Nephrol. 2019;14(9):1363-71. Epub 20190822. doi: 10.2215/cjn.14191218. PubMed PMID: 31439538; PubMed Central PMCID: PMC6730507.
41. McPherson S, Barbosa-Leiker C, Daratha K, Short R, McDonell MG, Alicic R, et al. Association of co-occurring serious mental illness with emergency hospitalization in people with chronic kidney disease. Am J Nephrol. 2014;39(3):260-7. Epub 20140319. doi: 10.1159/000360095. PubMed PMID: 24663040.
42. Balogun RA, Abdel-Rahman EM, Balogun SA, Lott EH, Lu JL, Malakauskas SM, et al. Association of depression and antidepressant use with mortality in a large cohort of patients with nondialysis-dependent CKD. Clin J Am Soc Nephrol. 2012;7(11):1793-800. Epub 20120816. doi: 10.2215/cjn.02650312. PubMed PMID: 22904119; PubMed Central PMCID: PMC3488945.
43. Davison SN, Koncicki H, Brennan F. Pain in chronic kidney disease: a scoping review. Semin Dial. 2014;27(2):188-204. Epub 20140212. doi: 10.1111/sdi.12196. PubMed PMID: 24517512.
44. 早期慢性腎臟病照護手冊。台灣腎臟醫學會, 衛生福利部國民健康署編撰. -- 第一版. -- 臺北市 : 衛生福利部國民健康署, 民 111.01.
45. Wong J, Motulsky A, Abrahamowicz M, Eguale T, Buckeridge DL, Tamblyn R. Off-label indications for antidepressants in primary care: descriptive study of prescriptions from an indication based electronic prescribing system. Bmj. 2017;356:j603. Epub 20170221. doi: 10.1136/bmj.j603. PubMed PMID: 28228380; PubMed Central PMCID: PMC5320934.
46. 衛生福利部食品藥物管理署。西藥、醫療器材、化粧品許可證查詢。Duloxetine 30 mg. Accessed April 14, 2023. https://info.fda.gov.tw/MLMS/H0001D.aspx?Type=Lic&LicId=02024240.
47. United States Food and Drug Administration. Drug Approvals and Databases. Duloxetine, CYMBALTA®. Accessed April 14, 2023. https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/021427s056lbl.pdf.
48. Electronic Medicines Compendium. Amitriptyline 25mg. Accessed April 14, 2023. https://www.medicines.org.uk/emc/product/14335/smpc.
49. Electronic Medicines Compendium. Cymbalta 30mg hard gastro- resistant capsules. Accessed “April 14, 2023. https://www.medicines.org.uk/emc/product/3880/smpc.
50. United States Food and Drug Administration. Drug Approvals and Databases. Doxepin, SILENOR®. Accessed April 17, 2023. https://www.accessdata.fda.gov/drugsatfda_docs/label/2020/022036s006lbl.pdf.
51. Gelenberg AJ, Freeman M, Markowitz J, Rosenbaum J, Thase M, Trivedi M, et al. American Psychiatric Association practice guidelines for the treatment of patients with major depressive disorder. Am J Psychiatry. 2010;167(Suppl 10):9-118.
52. Carvalho AF, Sharma MS, Brunoni AR, Vieta E, Fava GA. The Safety, Tolerability and Risks Associated with the Use of Newer Generation Antidepressant Drugs: A Critical Review of the Literature. Psychother Psychosom. 2016;85(5):270-88. Epub 20160811. doi: 10.1159/000447034. PubMed PMID: 27508501.
53. Dragioti E, Solmi M, Favaro A, Fusar-Poli P, Dazzan P, Thompson T, et al. Association of Antidepressant Use With Adverse Health Outcomes: A Systematic Umbrella Review. JAMA Psychiatry. 2019;76(12):1241-55. doi: 10.1001/jamapsychiatry.2019.2859. PubMed PMID: 31577342; PubMed Central PMCID: PMC6777224.
54. Voican CS, Corruble E, Naveau S, Perlemuter G. Antidepressant-induced liver injury: a review for clinicians. Am J Psychiatry. 2014;171(4):404-15. doi: 10.1176/appi.ajp.2013.13050709. PubMed PMID: 24362450.
55. Chen HY, Lin CL, Lai SW, Kao CH. Association of Selective Serotonin Reuptake Inhibitor Use and Acute Angle-Closure Glaucoma. J Clin Psychiatry. 2016;77(6):e692-6. doi: 10.4088/JCP.15m10038. PubMed PMID: 27135704.
56. Assimon MM, Brookhart MA, Flythe JE. Comparative Cardiac Safety of Selective Serotonin Reuptake Inhibitors among Individuals Receiving Maintenance Hemodialysis. J Am Soc Nephrol. 2019;30(4):611-23. Epub 20190318. doi: 10.1681/asn.2018101032. PubMed PMID: 30885935; PubMed Central PMCID: PMC6442344.
57. Hackam DG, Mrkobrada M. Selective serotonin reuptake inhibitors and brain hemorrhage: a meta-analysis. Neurology. 2012;79(18):1862-5. Epub 20121017. doi: 10.1212/WNL.0b013e318271f848. PubMed PMID: 23077009.
58. Shin D, Oh YH, Eom CS, Park SM. Use of selective serotonin reuptake inhibitors and risk of stroke: a systematic review and meta-analysis. J Neurol. 2014;261(4):686-95. Epub 20140130. doi: 10.1007/s00415-014-7251-9. PubMed PMID: 24477492.
59. Alqdwah-Fattouh R, Rodríguez-Martín S, Barreira-Hernández D, Izquierdo-Esteban L, Gil M, González-Bermejo D, et al. Selective Serotonin Reuptake Inhibitors and Risk of Noncardioembolic Ischemic Stroke: A Nested Case-Control Study. Stroke. 2022;53(5):1560-9. Epub 20220203. doi: 10.1161/strokeaha.121.036661. PubMed PMID: 35109681.
60. Chu CS, Chou PH, Lin CH, Cheng C, Tsai CJ, Lan TH, et al. Use of Selective Serotonin Reuptake Inhibitors and Risks of Stroke in Patients with Obsessive Compulsive Disorder: A Population-Based Study. PLoS One. 2016;11(9):e0162239. Epub 20160909. doi: 10.1371/journal.pone.0162239. PubMed PMID: 27612144; PubMed Central PMCID: PMC5017574.
61. Hoirisch-Clapauch S, Nardi AE. Antidepressants: bleeding or thrombosis? Thromb Res. 2019;181 Suppl 1:S23-s8. doi: 10.1016/s0049-3848(19)30362-7. PubMed PMID: 31477223.
62. Maclean JA, Schoenwaelder SM. Chapter 5 - Serotonin in Platelets. In: Pilowsky PM, editor. Serotonin. Boston: Academic Press; 2019. p. 91-119.
63. de Abajo FJ. Effects of selective serotonin reuptake inhibitors on platelet function: mechanisms, clinical outcomes and implications for use in elderly patients. Drugs Aging. 2011;28(5):345-67. doi: 10.2165/11589340-000000000-00000. PubMed PMID: 21542658.
64. Hergovich N, Aigner M, Eichler HG, Entlicher J, Drucker C, Jilma B. Paroxetine decreases platelet serotonin storage and platelet function in human beings. Clin Pharmacol Ther. 2000;68(4):435-42. doi: 10.1067/mcp.2000.110456. PubMed PMID: 11061584.
65. Wgner A, Montero D, Mårtensson B, Siwers B, Asberg M. Effects of fluoxetine treatment of platelet 3H-imipramine binding, 5-HT uptake and 5-HT content in major depressive disorder. J Affect Disord. 1990;20(2):101-13. doi: 10.1016/0165-0327(90)90123-p. PubMed PMID: 2176228.
66. Mårtensson B, Wägner A, Beck O, Brodin K, Montero D, Asberg M. Effects of clomipramine treatment on cerebrospinal fluid monoamine metabolites and platelet 3H-imipramine binding and serotonin uptake and concentration in major depressive disorder. Acta Psychiatr Scand. 1991;83(2):125-33. doi: 10.1111/j.1600-0447.1991.tb07377.x. PubMed PMID: 1708190.
67. Vanhoutte PM, Shimokawa H, Feletou M, Tang EH. Endothelial dysfunction and vascular disease - a 30th anniversary update. Acta Physiol (Oxf). 2017;219(1):22-96. Epub 20160125. doi: 10.1111/apha.12646. PubMed PMID: 26706498.
68. Bonvento G, MacKenzie ET, Edvinsson L. Serotonergic innervation of the cerebral vasculature: relevance to migraine and ischaemia. Brain Res Brain Res Rev. 1991;16(3):257-63. doi: 10.1016/0165-0173(91)90009-w. PubMed PMID: 1790433.
69. Muhonen MG, Robertson SC, Gerdes JS, Loftus CM. Effects of serotonin on cerebral circulation after middle cerebral artery occlusion. J Neurosurg. 1997;87(2):301-6. doi: 10.3171/jns.1997.87.2.0301. PubMed PMID: 9254097.
70. Wu CS, Wang SC, Cheng YC, Gau SS. Association of cerebrovascular events with antidepressant use: a case-crossover study. Am J Psychiatry. 2011;168(5):511-21. Epub 20110315. doi: 10.1176/appi.ajp.2010.10071064. PubMed PMID: 21406464.
71. Renoux C, Vahey S, Dell'Aniello S, Boivin JF. Association of Selective Serotonin Reuptake Inhibitors With the Risk for Spontaneous Intracranial Hemorrhage. JAMA Neurol. 2017;74(2):173-80. doi: 10.1001/jamaneurol.2016.4529. PubMed PMID: 27918771.
72. Ön BI, Vidal X, Berger U, Sabaté M, Ballarín E, Maisterra O, et al. Antidepressant use and stroke or mortality risk in the elderly. Eur J Neurol. 2022;29(2):469-77. Epub 20211025. doi: 10.1111/ene.15137. PubMed PMID: 34632668.
73. Jensen MP, Ziff OJ, Banerjee G, Ambler G, Werring DJ. The impact of selective serotonin reuptake inhibitors on the risk of intracranial haemorrhage: A systematic review and meta-analysis. Eur Stroke J. 2019;4(2):144-52. Epub 20190125. doi: 10.1177/2396987319827211. PubMed PMID: 31259262; PubMed Central PMCID: PMC6591760.
74. Lee YC, Lin CH, Lin MS, Lin JW, Chang CH, Lai MS. Effects of selective serotonin reuptake inhibitors versus tricyclic antidepressants on cerebrovascular events: a nationwide population-based cohort study. J Clin Psychopharmacol. 2013;33(6):782-9. doi: 10.1097/JCP.0b013e31829c970e. PubMed PMID: 24091857.
75. Koncicki HM, Unruh M, Schell JO. Pain Management in CKD: A Guide for Nephrology Providers. Am J Kidney Dis. 2017;69(3):451-60. Epub 20161120. doi: 10.1053/j.ajkd.2016.08.039. PubMed PMID: 27881247.
76. Pham PC, Khaing K, Sievers TM, Pham PM, Miller JM, Pham SV, et al. 2017 update on pain management in patients with chronic kidney disease. Clin Kidney J. 2017;10(5):688-97. Epub 20170818. doi: 10.1093/ckj/sfx080. PubMed PMID: 28979781; PubMed Central PMCID: PMC5622905.
77. Nagler EV, Webster AC, Vanholder R, Zoccali C. Antidepressants for depression in stage 3-5 chronic kidney disease: a systematic review of pharmacokinetics, efficacy and safety with recommendations by European Renal Best Practice (ERBP). Nephrol Dial Transplant. 2012;27(10):3736-45. Epub 20120801. doi: 10.1093/ndt/gfs295. PubMed PMID: 22859791.
78. Lee M, Saver JL, Chang KH, Liao HW, Chang SC, Ovbiagele B. Low glomerular filtration rate and risk of stroke: meta-analysis. Bmj. 2010;341:c4249. Epub 20100930. doi: 10.1136/bmj.c4249. PubMed PMID: 20884696; PubMed Central PMCID: PMC2948650.
79. Ninomiya T, Perkovic V, Verdon C, Barzi F, Cass A, Gallagher M, et al. Proteinuria and stroke: a meta-analysis of cohort studies. Am J Kidney Dis. 2009;53(3):417-25. Epub 20081213. doi: 10.1053/j.ajkd.2008.08.032. PubMed PMID: 19070947.
80. Chen YC, Su YC, Lee CC, Huang YS, Hwang SJ. Chronic kidney disease itself is a causal risk factor for stroke beyond traditional cardiovascular risk factors: a nationwide cohort study in Taiwan. PLoS One. 2012;7(4):e36332. Epub 20120430. doi: 10.1371/journal.pone.0036332. PubMed PMID: 22558437; PubMed Central PMCID: PMC3340358.
81. Cherng YG, Lin CS, Shih CC, Hsu YH, Yeh CC, Hu CJ, et al. Stroke risk and outcomes in patients with chronic kidney disease or end-stage renal disease: Two nationwide studies. PLoS One. 2018;13(1):e0191155. Epub 20180112. doi: 10.1371/journal.pone.0191155. PubMed PMID: 29329323; PubMed Central PMCID: PMC5766135.
82. Toyoda K, Ninomiya T. Stroke and cerebrovascular diseases in patients with chronic kidney disease. Lancet Neurol. 2014;13(8):823-33. doi: 10.1016/s1474-4422(14)70026-2. PubMed PMID: 25030514.
83. Ueki K, Matsuo R, Kuwashiro T, Irie F, Wakisaka Y, Ago T, et al. Decreased Estimated Glomerular Filtration Rate and Proteinuria and Long-Term Outcomes After Ischemic Stroke: A Longitudinal Observational Cohort Study. Stroke. 2023;54(5):1268-77. Epub 20230406. doi: 10.1161/strokeaha.122.040958. PubMed PMID: 37021567.
84. Kumai Y, Kamouchi M, Hata J, Ago T, Kitayama J, Nakane H, et al. Proteinuria and clinical outcomes after ischemic stroke. Neurology. 2012;78(24):1909-15. Epub 20120516. doi: 10.1212/WNL.0b013e318259e110. PubMed PMID: 22592359.
85. Liu L, Fuller M, Behymer TP, Ng Y, Christianson T, Shah S, et al. Selective Serotonin Reuptake Inhibitors and Intracerebral Hemorrhage Risk and Outcome. Stroke. 2020;51(4):1135-41. Epub 20200304. doi: 10.1161/strokeaha.119.028406. PubMed PMID: 32126942; PubMed Central PMCID: PMC7147963.
86. Vanent KN, Leasure AC, Acosta JN, Kuohn LR, Woo D, Murthy SB, et al. Association of Chronic Kidney Disease With Risk of Intracerebral Hemorrhage. JAMA Neurol. 2022;79(9):911-8. doi: 10.1001/jamaneurol.2022.2299. PubMed PMID: 35969388; PubMed Central PMCID: PMC9379821.
87. Cheng TM. Taiwan's new national health insurance program: genesis and experience so far. Health Aff (Millwood). 2003;22(3):61-76. doi: 10.1377/hlthaff.22.3.61. PubMed PMID: 12757273.
88. National Health Insurance Research Database, Taiwan. http://nhird.nhri.org.tw/en/index.htm (accessed May, 2023).
89. WHO Collaborating Centre for Drug Statistics Methodology. Guidelines for ATC Classification and DDD Assignment. 2023 https://www.whocc.no/atc_ddd_index/ (accessed May 2023).
90. Charlson ME, Wells MT. Comorbidity: From a Confounder in Longitudinal Clinical Research to the Main Issue in Population Management. Psychother Psychosom. 2022;91(3):145-51. Epub 20220223. doi: 10.1159/000521952. PubMed PMID: 35196663; PubMed Central PMCID: PMC9064932.
91. Ording AG, Sørensen HT. Concepts of comorbidities, multiple morbidities, complications, and their clinical epidemiologic analogs. Clin Epidemiol. 2013;5:199-203. Epub 20130701. doi: 10.2147/clep.S45305. PubMed PMID: 23861599; PubMed Central PMCID: PMC3704301.
92. Hemmelgarn BR, Manns BJ, Quan H, Ghali WA. Adapting the Charlson Comorbidity Index for use in patients with ESRD. Am J Kidney Dis. 2003;42(1):125-32. doi: 10.1016/s0272-6386(03)00415-3. PubMed PMID: 12830464.
93. Schneeweiss S, Maclure M. Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol. 2000;29(5):891-8. doi: 10.1093/ije/29.5.891. PubMed PMID: 11034974.
94. Noordzij M, Dekker FW, Zoccali C, Jager KJ. Measures of disease frequency: prevalence and incidence. Nephron Clin Pract. 2010;115(1):c17-20. Epub 20100219. doi: 10.1159/000286345. PubMed PMID: 20173345.
95. Wu CS, Shau WY, Chan HY, Lee YC, Lai YJ, Lai MS. Utilization of antidepressants in Taiwan: a nationwide population-based survey from 2000 to 2009. Pharmacoepidemiol Drug Saf. 2012;21(9):980-8. Epub 20120417. doi: 10.1002/pds.3255. PubMed PMID: 22511574.
96. 黃尚志. 2021台灣腎病年報. 財團法人國家衛生研究院、台灣腎臟醫學會2022/6/1.
97. Shirazian S, Grant CD, Aina O, Mattana J, Khorassani F, Ricardo AC. Depression in Chronic Kidney Disease and End-Stage Renal Disease: Similarities and Differences in Diagnosis, Epidemiology, and Management. Kidney Int Rep. 2017;2(1):94-107. Epub 20160920. doi: 10.1016/j.ekir.2016.09.005. PubMed PMID: 29318209; PubMed Central PMCID: PMC5720531.
98. Cukor D, Ver Halen N, Fruchter Y. Anxiety and quality of life in ESRD. Semin Dial. 2013;26(3):265-8. Epub 20130222. doi: 10.1111/sdi.12065. PubMed PMID: 23432416.
99. Cheng TM. Reflections on the 20th anniversary of Taiwan's single-payer National Health Insurance System. Health Aff (Millwood). 2015;34(3):502-10. doi: 10.1377/hlthaff.2014.1332. PubMed PMID: 25732502.
100. Ferreira GE, Abdel-Shaheed C, Underwood M, Finnerup NB, Day RO, McLachlan A, et al. Efficacy, safety, and tolerability of antidepressants for pain in adults: overview of systematic reviews. Bmj. 2023;380:e072415. Epub 20230201. doi: 10.1136/bmj-2022-072415. PubMed PMID: 36725015; PubMed Central PMCID: PMC9887507.
101. Bauer M, Severus E, Köhler S, Whybrow PC, Angst J, Möller HJ. World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for biological treatment of unipolar depressive disorders. part 2: maintenance treatment of major depressive disorder-update 2015. World J Biol Psychiatry. 2015;16(2):76-95. doi: 10.3109/15622975.2014.1001786. PubMed PMID: 25677972.
102. Gadzhanova S, Roughead EE, Pont LG. Antidepressant switching patterns in the elderly. Int Psychogeriatr. 2018;30(9):1365-74. Epub 20180130. doi: 10.1017/s1041610217002964. PubMed PMID: 29380718.
103. Mars B, Heron J, Gunnell D, Martin RM, Thomas KH, Kessler D. Prevalence and patterns of antidepressant switching amongst primary care patients in the UK. J Psychopharmacol. 2017;31(5):553-60. Epub 20170201. doi: 10.1177/0269881117693748. PubMed PMID: 28460603.
104. Tamblyn R, Bates DW, Buckeridge DL, Dixon W, Forster AJ, Girard N, et al. Multinational comparison of new antidepressant use in older adults: a cohort study. BMJ Open. 2019;9(5):e027663. Epub 20190514. doi: 10.1136/bmjopen-2018-027663. PubMed PMID: 31092665; PubMed Central PMCID: PMC6530307.
105. Saragoussi D, Chollet J, Bineau S, Chalem Y, Milea D. Antidepressant switching patterns in the treatment of major depressive disorder: a General Practice Research Database (GPRD) Study. Int J Clin Pract. 2012;66(11):1079-87. doi: 10.1111/j.1742-1241.2012.03015.x. PubMed PMID: 23067031.
106. Milea D, Guelfucci F, Bent-Ennakhil N, Toumi M, Auray JP. Antidepressant monotherapy: A claims database analysis of treatment changes and treatment duration. Clin Ther. 2010;32(12):2057-72. doi: 10.1016/j.clinthera.2010.11.011. PubMed PMID: 21118742.
107. Mullins CD, Shaya FT, Meng F, Wang J, Harrison D. Persistence, switching, and discontinuation rates among patients receiving sertraline, paroxetine, and citalopram. Pharmacotherapy. 2005;25(5):660-7. doi: 10.1592/phco.25.5.660.63590. PubMed PMID: 15899727.
108. Wu CS, Shau WY, Chan HY, Lai MS. Persistence of antidepressant treatment for depressive disorder in Taiwan. Gen Hosp Psychiatry. 2013;35(3):279-85. Epub 20130212. doi: 10.1016/j.genhosppsych.2012.12.003. PubMed PMID: 23415578.
109. Sheehan DV, Keene MS, Eaddy M, Krulewicz S, Kraus JE, Carpenter DJ. Differences in medication adherence and healthcare resource utilization patterns: older versus newer antidepressant agents in patients with depression and/or anxiety disorders. CNS Drugs. 2008;22(11):963-73. doi: 10.2165/00023210-200822110-00005. PubMed PMID: 18840035.
110. Bauer M, Pfennig A, Severus E, Whybrow PC, Angst J, Möller HJ. World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for biological treatment of unipolar depressive disorders, part 1: update 2013 on the acute and continuation treatment of unipolar depressive disorders. World J Biol Psychiatry. 2013;14(5):334-85. Epub 20130703. doi: 10.3109/15622975.2013.804195. PubMed PMID: 23879318.
111. Worthington JM, Gattellari M, Goumas C, Jalaludin B. Differentiating Incident from Recurrent Stroke Using Administrative Data: The Impact of Varying Lengths of Look-Back Periods on the Risk of Misclassification. Neuroepidemiology. 2017;48(3-4):111-8. Epub 20170622. doi: 10.1159/000478016. PubMed PMID: 28637036.
112. Amarenco P. Five-Year Risk of Stroke after TIA or Minor Ischemic Stroke. N Engl J Med. 2018;379(16):1580-1. doi: 10.1056/NEJMc1808913. PubMed PMID: 30332572.
113. Mohan KM, Wolfe CD, Rudd AG, Heuschmann PU, Kolominsky-Rabas PL, Grieve AP. Risk and cumulative risk of stroke recurrence: a systematic review and meta-analysis. Stroke. 2011;42(5):1489-94. Epub 20110331. doi: 10.1161/strokeaha.110.602615. PubMed PMID: 21454819.
114. Liu SW, Huang LC, Chung WF, Chang HK, Wu JC, Chen LF, et al. Increased Risk of Stroke in Patients of Concussion: A Nationwide Cohort Study. Int J Environ Res Public Health. 2017;14(3). Epub 20170225. doi: 10.3390/ijerph14030230. PubMed PMID: 28245607; PubMed Central PMCID: PMC5369066.
115. Chen YH, Kang JH, Lin HC. Patients with traumatic brain injury: population-based study suggests increased risk of stroke. Stroke. 2011;42(10):2733-9. Epub 20110728. doi: 10.1161/strokeaha.111.620112. PubMed PMID: 21799162.
116. Cheng CL, Kao YH, Lin SJ, Lee CH, Lai ML. Validation of the National Health Insurance Research Database with ischemic stroke cases in Taiwan. Pharmacoepidemiol Drug Saf. 2011;20(3):236-42. Epub 20101229. doi: 10.1002/pds.2087. PubMed PMID: 21351304.
117. Hsieh CY, Chen CH, Li CY, Lai ML. Validating the diagnosis of acute ischemic stroke in a National Health Insurance claims database. J Formos Med Assoc. 2015;114(3):254-9. Epub 20131018. doi: 10.1016/j.jfma.2013.09.009. PubMed PMID: 24140108.
118. Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association. 1999;94(446):496-509. doi: 10.2307/2670170.
119. Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. Am J Epidemiol. 2009;170(2):244-56. Epub 20090603. doi: 10.1093/aje/kwp107. PubMed PMID: 19494242; PubMed Central PMCID: PMC2732996.
120. Austin PC, Lee DS, Fine JP. Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation. 2016;133(6):601-9. doi: 10.1161/circulationaha.115.017719. PubMed PMID: 26858290; PubMed Central PMCID: PMC4741409.
121. Lee YC, Lin CH, Lin MS, Lu Y, Chang CH, Lin JW. Comparison of the effects of serotonin-norepinephrine reuptake inhibitors versus selective serotonin reuptake inhibitors on cerebrovascular events. J Clin Psychiatry. 2016;77(1):e1-7. doi: 10.4088/JCP.14m09394. PubMed PMID: 26845272.
122. Bang OY, Chung JW, Lee MJ, Seo WK, Kim GM, Ahn MJ. Cancer-Related Stroke: An Emerging Subtype of Ischemic Stroke with Unique Pathomechanisms. J Stroke. 2020;22(1):1-10. Epub 20200131. doi: 10.5853/jos.2019.02278. PubMed PMID: 32027788; PubMed Central PMCID: PMC7005348.
123. Dearborn JL, Urrutia VC, Zeiler SR. Stroke and Cancer- A Complicated Relationship. J Neurol Transl Neurosci. 2014;2(1):1039. PubMed PMID: 26322334; PubMed Central PMCID: PMC4550304.
124. Schwarzbach CJ, Schaefer A, Ebert A, Held V, Bolognese M, Kablau M, et al. Stroke and cancer: the importance of cancer-associated hypercoagulation as a possible stroke etiology. Stroke. 2012;43(11):3029-34. Epub 20120920. doi: 10.1161/strokeaha.112.658625. PubMed PMID: 22996958.
125. Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. 2004;44(1):1-12. doi: 10.1021/ci0342472. PubMed PMID: 14741005.
126. Ying X, editor An overview of overfitting and its solutions. Journal of physics: Conference series; 2019: IOP Publishing.
127. Korayem GB, Alshaya OA, Kurdi SM, Alnajjar LI, Badr AF, Alfahed A, et al. Simulation-Based Education Implementation in Pharmacy Curriculum: A Review of the Current Status. Adv Med Educ Pract. 2022;13:649-60. Epub 20220701. doi: 10.2147/amep.S366724. PubMed PMID: 35801134; PubMed Central PMCID: PMC9255713.
128. Coyne L, Merritt TA, Parmentier BL, Sharpton RA, Takemoto JK. The Past, Present, and Future of Virtual Reality in Pharmacy Education. Am J Pharm Educ. 2019;83(3):7456. doi: 10.5688/ajpe7456. PubMed PMID: 31065173; PubMed Central PMCID: PMC6498191.
129. Education ACfP. Accreditation standards and key elements for the professional program in pharmacy leading to the doctor of pharmacy degree. https://www.acpe-accredit.org/pdf/Standards2016FINAL.pdf. Published February 2015. Accessed 13 Aug 2022.: Accreditation Council for Pharmacy Education Chicago, IL; 2015.
130. Education ACfP. Policies And Procedures For ACPE Accreditation Of Professional Degree Programs. https://www.acpeaccredit.org/pdf/CSPoliciesandProceduresJune2022.pdf Published June 2022. Accessed 14 Aug 2022.: Accreditation Council for Pharmacy Education Chicago, IL; 2022.
131. Oman SP, Magdi Y, Simon LV. Past Present and Future of Simulation in Internal Medicine. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright 2022, StatPearls Publishing LLC.; 2022.
132. Bui I, Bhattacharya A, Wong SH, Singh HR, Agarwal A. Role of Three-Dimensional Visualization Modalities in Medical Education. Front Pediatr. 2021;9:760363. Epub 20211207. doi: 10.3389/fped.2021.760363. PubMed PMID: 34950617; PubMed Central PMCID: PMC8691210.
133. Sherman WR, Craig AB. Understanding virtual reality: Interface, application, and design: Morgan Kaufmann; 2018.
134. Ventola CL. Virtual Reality in Pharmacy: Opportunities for Clinical, Research, and Educational Applications. P t. 2019;44(5):267-76. PubMed PMID: 31080335; PubMed Central PMCID: PMC6487969.
135. Jabbur-Lopes MO, Mesquita AR, Silva LM, De Almeida Neto A, Lyra DP, Jr. Virtual patients in pharmacy education. Am J Pharm Educ. 2012;76(5):92. doi: 10.5688/ajpe76592. PubMed PMID: 22761533; PubMed Central PMCID: PMC3386043.
136. Fuhrman Jr LC, Buff WE, Eaddy M, Dollar M. Utilization of an integrated interactive virtual patient database in a web-based environment for teaching continuity of care. American Journal of Pharmaceutical Education. 2001;65(3):271.
137. Ambroziak K, Ibrahim N, Marshall VD, Kelling SE. Virtual simulation to personalize student learning in a required pharmacy course. Curr Pharm Teach Learn. 2018;10(6):750-6. Epub 20180406. doi: 10.1016/j.cptl.2018.03.017. PubMed PMID: 30025776.
138. Patel S, Vincent AH, Abel SR, Jacobs CM, Dunlop SR, Seibert M. A virtual clean room to teach USP 797 regulations for intravenous medications. Am J Pharm Educ. 2011;75(1):7. doi: 10.5688/ajpe7517. PubMed PMID: 21451759; PubMed Central PMCID: PMC3049666.
139. Gustafsson M, Englund C, Gallego G. The description and evaluation of virtual worlds in clinical pharmacy education in Northern Sweden. Curr Pharm Teach Learn. 2017;9(5):887-92. Epub 20170821. doi: 10.1016/j.cptl.2017.06.002. PubMed PMID: 29233320.
140. Kuehn BM. Virtual and Augmented Reality Put a Twist on Medical Education. Jama. 2018;319(8):756-8. doi: 10.1001/jama.2017.20800. PubMed PMID: 29417140.
141. Fox BI, Felkey BG. Virtual Reality and Pharmacy: Opportunities and Challenges. Hosp Pharm. 2017;52(2):160-1. doi: 10.1310/hpj5202-160. PubMed PMID: 28321146; PubMed Central PMCID: PMC5345917.
142. Borja-Hart NL, Spivey CA, George CM. Use of virtual patient software to assess student confidence and ability in communication skills and virtual patient impression: A mixed-methods approach. Curr Pharm Teach Learn. 2019;11(7):710-8. Epub 20190323. doi: 10.1016/j.cptl.2019.03.009. PubMed PMID: 31227094.
143. Mak V, Krishnan S, Chuang S. Students' and Examiners' Experiences of Their First Virtual Pharmacy Objective Structured Clinical Examination (OSCE) in Australia during the COVID-19 Pandemic. Healthcare (Basel). 2022;10(2). Epub 20220209. doi: 10.3390/healthcare10020328. PubMed PMID: 35206942; PubMed Central PMCID: PMC8871798.
144. Douglass MA, Casale JP, Skirvin JA, DiVall MV. A virtual patient software program to improve pharmacy student learning in a comprehensive disease management course. Am J Pharm Educ. 2013;77(8):172. doi: 10.5688/ajpe778172. PubMed PMID: 24159213; PubMed Central PMCID: PMC3806956.
145. 宋曜廷, 潘佩妤. 混合研究在教育研究的應用. 教育科學研究期刊. 2010;55(4):97-130.
146. Elrod S, Bullock K. Assessing the quality of Objective Structured Clinical Examination (OSCE) reports in pharmacy education: a review of the literature. MedEdPublish. 2018;7(257):257.
147. Barman A. Critiques on the objective structured clinical examination. Annals-Academy of Medicine Singapore. 2005;34(8):478.
148. ROBERTS J, NORMAN G. Reliability and learning from the objective structured clinical examination. Medical Education. 1990;24(3):219-23. doi: https://doi.org/10.1111/j.1365-2923.1990.tb00004.x.
149. Stowe CD, Gardner SF. Real-time standardized participant grading of an objective structured clinical examination. American Journal of Pharmaceutical Education. 2005;69(1-5):272.
150. Sturpe DA, Huynh D, Haines ST. Scoring objective structured clinical examinations using video monitors or video recordings. Am J Pharm Educ. 2010;74(3):44. doi: 10.5688/aj740344. PubMed PMID: 20498737; PubMed Central PMCID: PMC2865410.
151. Chenot J-F, Simmenroth-Nayda A, Koch A, Fischer T, Scherer M, Emmert B, et al. Can student tutors act as examiners in an objective structured clinical examination? Medical Education. 2007;41(11):1032-8. doi: https://doi.org/10.1111/j.1365-2923.2007.02895.x.
152. Berger AJ, Gillespie CC, Tewksbury LR, Overstreet IM, Tsai MC, Kalet AL, et al. Assessment of medical student clinical reasoning by “lay” vs physician raters: inter-rater reliability using a scoring guide in a multidisciplinary objective structured clinical examination. The American Journal of Surgery. 2012;203(1):81-6. doi: https://doi.org/10.1016/j.amjsurg.2011.08.003.
153. Schleicher I, Leitner K, Juenger J, Moeltner A, Ruesseler M, Bender B, et al. Examiner effect on the objective structured clinical exam – a study at five medical schools. BMC Medical Education. 2017;17(1):71. doi: 10.1186/s12909-017-0908-1.
154. Baniasadi T, Ayyoubzadeh SM, Mohammadzadeh N. Challenges and Practical Considerations in Applying Virtual Reality in Medical Education and Treatment. Oman Med J. 2020;35(3):e125. Epub 20200518. doi: 10.5001/omj.2020.43. PubMed PMID: 32489677; PubMed Central PMCID: PMC7232669.
155. Chan SCC, Choa G, Kelly J, Maru D, Rashid MA. Implementation of virtual OSCE in health professions education: A systematic review. Med Educ. 2023. Epub 20230420. doi: 10.1111/medu.15089. PubMed PMID: 37080907.
156. Garnier A, Vanherp R, Bonnabry P, Bouchoud L. Use of simulation for education in hospital pharmaceutical technologies: a systematic review. Eur J Hosp Pharm. 2023;30(2):70-6. Epub 20211223. doi: 10.1136/ejhpharm-2021-003034. PubMed PMID: 34949651.
157. Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107-15. doi: 10.1111/j.1365-2648.2007.04569.x. PubMed PMID: 18352969.
158. Stemler S. An overview of content analysis. Practical assessment, research, and evaluation. 2000;7(1):17.
159. Kelly KM, Dhumal T. Qualitative Research in Pharmacy Settings. In: Aparasu RR, Bentley JP, Pate AN, editors. Student Handbook for Pharmacy Practice Research: A Companion Book to Conduct Practice-Based Research in Pharmacy. New York, NY: McGraw Hill LLC; 2022.
160. Bengtsson M. How to plan and perform a qualitative study using content analysis. NursingPlus Open. 2016;2:8-14. doi: https://doi.org/10.1016/j.npls.2016.01.001.
161. Holdford D. Content analysis methods for conducting research in social and administrative pharmacy. Research in Social and Administrative Pharmacy. 2008;4(2):173-81. doi: https://doi.org/10.1016/j.sapharm.2007.03.003.
162. 梁淑媛, 莊宇慧, 吳淑芳. 內容分析技巧在護理質性資料之初步應用. 護理雜誌. 2012;59(5):84-90. doi: 10.6224/jn.59.5.84.
163. Karl A, Wisnowski J, Rushing WH. A practical guide to text mining with topic extraction. Wiley Interdisciplinary Reviews: Computational Statistics. 2015;7(5):326-40.
164. Lindgren BM, Lundman B, Graneheim UH. Abstraction and interpretation during the qualitative content analysis process. Int J Nurs Stud. 2020;108:103632. Epub 20200515. doi: 10.1016/j.ijnurstu.2020.103632. PubMed PMID: 32505813.
165. Lai L, To WM. Content analysis of social media: A grounded theory approach. Journal of Electronic Commerce Research. 2015;16:138-52.
166. SAS® Visual Text Analytics: User’s Guide [Internet]. 2023 [cited May 26, 2023]. Available from: https://documentation.sas.com/doc/en/ctxtcdc/v_015/ctxtug/titlepage.htm.
167. Anandarajan M, Hill C, Nolan T. SAS Visual Text Analytics. In: Anandarajan M, Hill C, Nolan T, editors. Practical Text Analytics: Maximizing the Value of Text Data. Cham: Springer International Publishing; 2019. p. 263-82.
168. Hallgren KA. Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. Tutor Quant Methods Psychol. 2012;8(1):23-34. doi: 10.20982/tqmp.08.1.p023. PubMed PMID: 22833776; PubMed Central PMCID: PMC3402032.
169. Cohen J. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement. 1960;20(1):37-46. doi: 10.1177/001316446002000104.
170. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-74. PubMed PMID: 843571.
171. 許惠琪. Kappa統計量於量測一致性之應用與使用限制: 國立臺灣大學; 2005.
172. Gwet K. Inter-rater reliability: dependency on trait prevalence and marginal homogeneity. 2002.
173. Feinstein AR, Cicchetti DV. High agreement but low kappa: I. The problems of two paradoxes. J Clin Epidemiol. 1990;43(6):543-9. doi: 10.1016/0895-4356(90)90158-l. PubMed PMID: 2348207.
174. Chong L, Taylor S, Haywood M, Adelstein BA, Shulruf B. The sights and insights of examiners in objective structured clinical examinations. J Educ Eval Health Prof. 2017;14:34. Epub 20171227. doi: 10.3352/jeehp.2017.14.34. PubMed PMID: 29278906; PubMed Central PMCID: PMC5801428.
175. Mortsiefer A, Karger A, Rotthoff T, Raski B, Pentzek M. Examiner characteristics and interrater reliability in a communication OSCE. Patient Education and Counseling. 2017;100(6):1230-4. doi: https://doi.org/10.1016/j.pec.2017.01.013.
176. Wilkinson TJ, Frampton CM, Thompson-Fawcett M, Egan T. Objectivity in objective structured clinical examinations: checklists are no substitute for examiner commitment. Acad Med. 2003;78(2):219-23. doi: 10.1097/00001888-200302000-00021. PubMed PMID: 12584104.