研究背景
隨著抗精神病藥物的發展，第二代抗精神病藥物逐漸取代第一代為優先選擇，尤其是在兒童及青少年族群。安全性方面，第二代抗精神病藥物大幅降低錐體外症候群的副作用。然而，愈來愈多研究指出第二代抗精神藥物會增加代謝症候群、心血管及腦血管事件的發生風險。不過這些研究大多建立於一般成人或是老年族群，兒童與青少年的安全性資料付之闕如，加上鮮少研究納入亞洲病人族群，抗精神病藥物對於不同種族族群的風險甚少被探討。資料科學日新月異，多國資料庫的藥物流行病學研究蓬勃發展，藉此能夠評估藥物在不同國家族群的風險差異。

研究目的
評估不同國家對於兒童、青少年及年輕成人開立抗精神病藥物之處方型態，以及暴露於抗精神病藥物的心血管事件和代謝異常事件之發生風險比。

研究方法
主要以台灣全民健康保險資料庫進行自我對照分析研究。輔以香港資料庫(Clinical Data Analysis Reporting System；CDARS)與英國資料庫(The Health Improvement Network；THIN)。觀察期間分別為2004年至2012年（台灣）；2001年至2014年（香港）；1997年至2016年（英國）。研究族群納入6到30歲新使用口服抗精神病藥物之精神疾患病人。排除過去診斷有癌症、先天性心臟病、第一型糖尿病，以及觀察期前一年有相關心血管及代謝事件診斷紀錄者。針對該族群首次使用之抗精神病藥物進行處方型態分析。於觀察期間曾發生過一次（或以上）心血管代謝事件之病人做自我對照分析。欲觀察的心血管事件包含：中風、缺血性心臟病、急性心肌梗塞；代謝異常事件包含：高血壓、第二型糖尿病、血脂異常。主要結果為評估病人於暴露在抗精神病物的期間（風險期）、風險前期以及風險後期之心血管代謝發生風險，以病人未暴露於抗精神病藥物的期間為對照組，建立在卜瓦松迴歸模型下計算發生風險比。次要結果為評估第一代、第二代以及個別抗精神病藥物之心血管代謝發生風險，並依據年齡進行次分組分析。

研究結果
本研究對象共納入台灣107,425人，平均年齡21.1±6.7歲，男性61.0%；香港19,034人，平均年齡23.3±5.3歲，男性51.0%；英國7,770人，平均年齡24.9±3.8歲，男性63.8%。各國開立第二代抗精神病藥比例皆逐年提升，開立比例分別為50% (2012年台灣)，61% (2014年香港)，95% (2016年英國)。最常被做為首次開立之藥物：sulpiride 30% (2012年台灣)，risperidone 38% (2014年香港)，olanzapine及risperidone 32% (2016年英國)。各國於6-12歲兒童的處方中，以risperidone最常被開立。隨著病人年齡增加所使用的抗精神病藥物種類也更多樣。台灣、香港、英國分別有9,730人，233人，120人發生過至少一次的心血管代謝事件。根據台灣研究結果，相較於未暴露藥物期間，開立處方後的前30天(IRR=1.56; 95%CI, 1.45-1.68)以及90天以上(IRR=1.42;1.32-1.53)有較高的心血管代謝事件發生風險。尤其以Haloperidol於前30天有較高的中風(IRR=10.09; 8.24-12.35)及缺血性心臟病風險(IRR=2.68; 1.96-3.66)；用藥90天以上有則有高血壓(IRR=1.58; 1.19-2.11)及第二型糖尿病風險(IRR=1.85; 1.21-2.81)。香港研究結果大致上與台灣一致，前30天有較高中風(IRR=10.09; 8.24-12.35)及高血壓風險(IRR=2.19; 1.01-4.74)。由於英國廣泛使用第二代抗精神病藥物，鮮少使用Haloperidol，並沒有觀察到類似結果。第二代抗精神病藥物以Risperidone較為顯著，台灣研究果顯示於前30天有較高的中風(IRR=1.96; 1.26-3.05)；用藥90天以上有高血壓(IRR=1.53; 1.11-2.10)及血脂異常風險(IRR=2.12; 1.54-2.91)。
台灣的次分組分析中，無論是兒童/青少年，或是年輕成人，在使用抗精神病藥品後的前30天及90天以上皆有較高心血管代謝事件發生風險。針對中風事件風險，僅有Haloperidol (兒童/青少年: IRR=9.02; 5.27-15.46; 年輕成人: IRR=9.65; 7.71-12.08)及Sulpiride (兒童/青少年: IRR=2.12; 1.07-4.18; 年輕成人: IRR=1.59; 1.13-2.23)同時在兩組有較高之風險。

研究結論
亞洲資料庫分析結果確立抗精神病藥物使用於兒童、青少年及年輕成人與心血管代謝事件風險之相關性。該研究結果提供醫療照護者應多留意可能的急性心血管事件，如使用Haloperidol後發生中風；同時在長期用藥後可能導致代謝異常，如使用Risperidone後發生高血壓及血脂異常。英國處方型態以第二代抗精神病藥物為主，且研究族群相對小，無法建立Haloperidol等藥物之風險相關性。

The study aimed to compare the risk of cardiometabolic events among antipsychotics in psychiatric children, adolescents and young adult patients. We included databases from Taiwan, Hong Kong, and the UK. We conducted distributed network approach with common data model for the international study. We performed self-controlled case series design and included population aged 6 to 30 years receiving antipsychotics with cardiometabolic events, including stroke, ischemic heart disease (IHD), acute myocardial infarction (AMI), hypertension, type 2 diabetes mellitus (T2DM) and dyslipidemia. We defined three risk periods based on the exposure of antipsychotics, 1-30 days, 30-90 days, and 90+ days, compared to the non-exposure period and performed conditional Poisson regressions to evaluate cardiometabolic risk. We included a total of 107,425, 19,034, 7770 patients and of which 9730, 233, 120 patients had events from Taiwan, Hong Kong, and the UK, respectively. We found antipsychotics were associated with cardiometabolic risk in the window of 1-30 days (incident rate ratio [IRR], 1.56; 95% CI, 1.45-1.68) and 90+ days (IRR, 1.42; 1.32-1.53) in Taiwan. Specifically, we found haloperidol had higher risk of stroke (IRR, 10.09; 8.24-12.35) and IHD (IRR, 2.68; 1.96-3.66) in the window of 1-30 days, and higher risk of hypertension (IRR, 1.58; 1.19-2.11) and T2DM (IRR, 1.85; 1.21-2.81) in the window of 90+ days in Taiwan. Consistent findings from Hong Kong that haloperidol had higher risk of stroke (IRR, 35.29; 4.63-269.25) and hypertension (IRR, 2.19; 1.01-4.74) in the window of 1-30 days, although we did not find consistent results from the UK. We found risperidone had higher risk of stroke (IRR, 1.96; 1.26-3.05) in the window of 1-30 days and higher risk of hypertension (IRR, 1.53; 1.11-2.10) and dyslipidemia (IRR, 2.12; 1.54-2.91) in the window of 90+ days in Taiwan. The findings warrant attentions on varied cardiometabolic effects of haloperidol and risperidone, suggesting we should use the antipsychotics with cautions to avoid unintended outcomes.

1. Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®): American Psychiatric Pub; 2013.
2. Costello EJ, Egger H, Angold A. 10-year research update review: the epidemiology of child and adolescent psychiatric disorders: I. Methods and public health burden. J Am Acad Child Adolesc Psychiatry 2005; 44(10): 972-86.
3. Merikangas KR, Nakamura EF, Kessler RC. Epidemiology of mental disorders in children and adolescents. Dialogues in clinical neuroscience 2009; 11(1): 7-20.
4. De Hert M, Detraux J, van Winkel R, Yu W, Correll CU. Metabolic and cardiovascular adverse effects associated with antipsychotic drugs. Nat Rev Endocrinol 2011; 8(2): 114-26.
5. Katzung BG, Masters SB, Trevor AJ. Basic & clinical pharmacology. 2018: 517.
6. Stahl SM. Stahl's essential psychopharmacology: neuroscientific basis and practical applications: Cambridge university press; 2013.
7. Nasrallah HA. Atypical antipsychotic-induced metabolic side effects: insights from receptor-binding profiles. Mol Psychiatry 2008; 13(1): 27-35.
8. Kusumi I, Boku S, Takahashi Y. Psychopharmacology of atypical antipsychotic drugs: From the receptor binding profile to neuroprotection and neurogenesis. Psychiatry Clin Neurosci 2015; 69(5): 243-58.
9. Mulder H, Cohen D, Scheffer H, et al. HTR2C gene polymorphisms and the metabolic syndrome in patients with schizophrenia: a replication study. J Clin Psychopharmacol 2009; 29(1): 16-20.
10. Ma X, Maimaitirexiati T, Zhang R, et al. HTR2C polymorphisms, olanzapine-induced weight gain and antipsychotic-induced metabolic syndrome in schizophrenia patients: a meta-analysis. Int J Psychiatry Clin Pract 2014; 18(4): 229-42.
11. M DEH, Schreurs V, Vancampfort D, R VANW. Metabolic syndrome in people with schizophrenia: a review. World psychiatry : official journal of the World Psychiatric Association (WPA) 2009; 8(1): 15-22.
12. Shin JY, Choi NK, Lee J, Park MJ, Lee SH, Park BJ. A comparison of risperidone and haloperidol for the risk of ischemic stroke in the elderly: a propensity score-matched cohort analysis. J Psychopharmacol 2015; 29(8): 903-9.
13. Shin JY, Choi NK, Lee J, et al. Risk of ischemic stroke associated with the use of antipsychotic drugs in elderly patients: a retrospective cohort study in Korea. PloS one 2015; 10(3): e0119931.
14. Lin ST, Chen CC, Tsang HY, et al. Association between antipsychotic use and risk of acute myocardial infarction: a nationwide case-crossover study. Circulation 2014; 130(3): 235-43.
15. Park Y, Bateman BT, Kim DH, et al. Use of haloperidol versus atypical antipsychotics and risk of in-hospital death in patients with acute myocardial infarction: cohort study. BMJ (Clinical research ed) 2018; 360: k1218.
16. Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009; 120(16): 1640-5.
17. Alberti KGMM, Zimmet P, Shaw J. The metabolic syndrome—a new worldwide definition. The Lancet 2005; 366(9491): 1059-62.
18. Khasawneh FT, Shankar GS. Minimizing cardiovascular adverse effects of atypical antipsychotic drugs in patients with schizophrenia. Cardiol Res Pract 2014; 2014: 273060.
19. Aguilar M, Bhuket T, Torres S, Liu B, Wong RJ. Prevalence of the metabolic syndrome in the united states, 2003-2012. Jama 2015; 313(19): 1973-4.
20. Hwang LC, Bai CH, Chen CJ. Prevalence of obesity and metabolic syndrome in Taiwan. Journal of the Formosan Medical Association = Taiwan yi zhi 2006; 105(8): 626-35.
21. Kalverdijk LJ, Bachmann CJ, Aagaard L, et al. A multi-national comparison of antipsychotic drug use in children and adolescents, 2005-2012. Child Adolesc Psychiatry Ment Health 2017; 11: 55.
22. Halfdanarson O, Zoega H, Aagaard L, et al. International trends in antipsychotic use: A study in 16 countries, 2005-2014. Eur Neuropsychopharmacol 2017; 27(10): 1064-76.
23. Lao KSJ, Tam AWY, Wong ICK, et al. Prescribing trends and indications of antipsychotic medication in Hong Kong from 2004 to 2014: General and vulnerable patient groups. Pharmacoepidemiol Drug Saf 2017; 26(11): 1387-94.
24. 鄭淑文, 詹宏裕. Antipsychotic Prescription Pattern among Child and Adolescent Patients with Psychiatric Illnesses in Taiwan. 臺灣精神醫學 2017; 31(3): 222-31+iv.
25. Correll CU, Manu P, Olshanskiy V, Napolitano B, Kane JM, Malhotra AK. Cardiometabolic risk of second-generation antipsychotic medications during first-time use in children and adolescents. Jama 2009; 302(16): 1765-73.
26. Cohen D, Bonnot O, Bodeau N, Consoli A, Laurent C. Adverse effects of second-generation antipsychotics in children and adolescents: a Bayesian meta-analysis. J Clin Psychopharmacol 2012; 32(3): 309-16.
27. Bachmann CJ, Lempp T, Glaeske G, Hoffmann F. Antipsychotic prescription in children and adolescents: an analysis of data from a German statutory health insurance company from 2005 to 2012. Dtsch Arztebl Int 2014; 111(3): 25-34.
28. Lai EC-C, Stang P, Yang Y-HK, Kubota K, Wong ICK, Setoguchi S. International Multi-database Pharmacoepidemiology: Potentials and Pitfalls. Current Epidemiology Reports 2015; 2(4): 229-38.
29. Hallas J, Pottegard A. Use of self-controlled designs in pharmacoepidemiology. J Intern Med 2014; 275(6): 581-9.
30. Farrington P, Rush M, Miller E, et al. A new method for active surveillance of adverse events from diphtheria/tetanus/pertussis and measles/mumps/rubella vaccines. The Lancet 1995; 345(8949): 567-9.
31. Petersen I, Douglas I, Whitaker H. Self controlled case series methods: an alternative to standard epidemiological study designs. BMJ (Clinical research ed) 2016; 354: i4515.
32. Douglas IJ, Smeeth L. Exposure to antipsychotics and risk of stroke: self controlled case series study. BMJ (Clinical research ed) 2008; 337: a1227.
33. Pratt NL, Roughead EE, Ramsay E, Salter A, Ryan P. Risk of hospitalization for stroke associated with antipsychotic use in the elderly: a self-controlled case series. Drugs Aging 2010; 27(11): 885-93.
34. Brauer R, Smeeth L, Anaya-Izquierdo K, et al. Antipsychotic drugs and risks of myocardial infarction: a self-controlled case series study. European heart journal 2015; 36(16): 984-92.
35. 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.
36. Cheng C-L, Lee C-H, Chen P-S, Li Y-H, Lin S-J, Yang Y-HK. Validation of Acute Myocardial Infarction Cases in the National Health Insurance Research Database in Taiwan. Journal of Epidemiology 2014; 24(6): 500-7.
37. Carbonari DM, Saine ME, Newcomb CW, et al. Use of demographic and pharmacy data to identify patients included within both the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN). Pharmacoepidemiol Drug Saf 2015; 24(9): 999-1003.
38. Marston L, Nazareth I, Petersen I, Walters K, Osborn DP. Prescribing of antipsychotics in UK primary care: a cohort study. BMJ open 2014; 4(12): e006135.
39. Danielsson B, Collin J, Jonasdottir Bergman G, Borg N, Salmi P, Fastbom J. Antidepressants and antipsychotics classified with torsades de pointes arrhythmia risk and mortality in older adults - a Swedish nationwide study. Br J Clin Pharmacol 2016; 81(4): 773-83.
40. 鐘盈姍. 台灣兒童、青少年及年輕成人使用抗精神藥物之心血管代謝作用風險比較: 成功大學; 2017.
41. Lai EC, Hsieh CY, Kao Yang YH, Lin SJ. Detecting potential adverse reactions of sulpiride in schizophrenic patients by prescription sequence symmetry analysis. PloS one 2014; 9(2): e89795.
42. Wu CS, Tsai YT, Tsai HJ. Antipsychotic drugs and the risk of ventricular arrhythmia and/or sudden cardiac death: a nation-wide case-crossover study. J Am Heart Assoc 2015; 4(2).
43. Krogh J, Selmer C, Torp-Pedersen C, Gislason GH, Kistorp C. Hyperprolactinemia and the Association with All-Cause Mortality and Cardiovascular Mortality. Horm Metab Res 2017; 49(6): 411-7.
44. Zeng C, Jose PA. Dopamine receptors: important antihypertensive counterbalance against hypertensive factors. Hypertension 2011; 57(1): 11-7.
45. Osborn D, Marston L, Nazareth I, King MB, Petersen I, Walters K. Relative risks of cardiovascular disease in people prescribed olanzapine, risperidone and quetiapine. Schizophr Res 2017; 183: 116-23.
46. Pasternak B, Svanstrom H, Ranthe MF, Melbye M, Hviid A. Atypical antipsychotics olanzapine, quetiapine, and risperidone and risk of acute major cardiovascular events in young and middle-aged adults: a nationwide register-based cohort study in Denmark. CNS Drugs 2014; 28(10): 963-73.
47. Liao CH, Chang CS, Wei WC, et al. Schizophrenia patients at higher risk of diabetes, hypertension and hyperlipidemia: a population-based study. Schizophr Res 2011; 126(1-3): 110-6.
48. Kessing LV, Thomsen AF, Mogensen UB, Andersen PK. Treatment with antipsychotics and the risk of diabetes in clinical practice. Br J Psychiatry 2010; 197(4): 266-71.
49. Baker RA, Pikalov A, Tran QV, Kremenets T, Arani RB, Doraiswamy PM. Atypical antipsychotic drugs and diabetes mellitus in the US Food and Drug Administration Adverse Event database: a systematic Bayesian signal detection analysis. Psychopharmacology bulletin 2009; 42(1): 11-31.
50. Mondelli V, Anacker C, Vernon AC, et al. Haloperidol and olanzapine mediate metabolic abnormalities through different molecular pathways. Transl Psychiatry 2013; 3: e208.
51. 杜彗寧, 張家銘, 周玟觀, 葉鳳英. 老年人之用藥問題. 台灣老年醫學暨老年學雜誌 2017; 12(1): 1-19.
52. 楊淑瑜, 詹鼎正, 張勤斌, et al. 臺灣老人潛在性不適當用藥評估工具的發展. 長期照護雜誌 2011; 15(3): 223-36.
53. Maher RL, Hanlon J, Hajjar ER. Clinical consequences of polypharmacy in elderly. Expert opinion on drug safety 2014; 13(1): 57-65.
54. Chan DC, Hao YT, Wu SC. Characteristics of outpatient prescriptions for frail Taiwanese elders with long-term care needs. Pharmacoepidemiol Drug Saf 2009; 18(4): 327-34.
55. Qato DM, Alexander GC, Conti RM, Johnson M, Schumm P, Lindau ST. Use of prescription and over-the-counter medications and dietary supplements among older adults in the United States. Jama 2008; 300(24): 2867-78.
56. Hajjar ER, Hanlon JT, Sloane RJ, et al. Unnecessary drug use in frail older people at hospital discharge. J Am Geriatr Soc 2005; 53(9): 1518-23.
57. Bourgeois FT, Shannon MW, Valim C, Mandl KD. Adverse drug events in the outpatient setting: an 11-year national analysis. Pharmacoepidemiol Drug Saf 2010; 19(9): 901-10.
58. Hohl CM, Dankoff J, Colacone A, Afilalo M. Polypharmacy, adverse drug-related events, and potential adverse drug interactions in elderly patients presenting to an emergency department. Annals of emergency medicine 2001; 38(6): 666-71.
59. O'Connor MN, Gallagher P, O'Mahony D. Inappropriate prescribing: criteria, detection and prevention. Drugs Aging 2012; 29(6): 437-52.
60. van Mil JW, Westerlund LO, Hersberger KE, Schaefer MA. Drug-related problem classification systems. Ann Pharmacother 2004; 38(5): 859-67.
61. Gurwitz JH, Field TS, Judge J, et al. The incidence of adverse drug events in two large academic long-term care facilities. Am J Med 2005; 118(3): 251-8.
62. Chang CB, Chen JH, Wen CJ, et al. Potentially inappropriate medications in geriatric outpatients with polypharmacy: application of six sets of published explicit criteria. Br J Clin Pharmacol 2011; 72(3): 482-9.
63. O'Connor MN, O'Sullivan D, Gallagher PF, Eustace J, Byrne S, O'Mahony D. Prevention of Hospital-Acquired Adverse Drug Reactions in Older People Using Screening Tool of Older Persons' Prescriptions and Screening Tool to Alert to Right Treatment Criteria: A Cluster Randomized Controlled Trial. J Am Geriatr Soc 2016; 64(8): 1558-66.
64. Chang CB, Chan DC. Comparison of published explicit criteria for potentially inappropriate medications in older adults. Drugs Aging 2010; 27(12): 947-57.
65. Kaufmann CP, Tremp R, Hersberger KE, Lampert ML. Inappropriate prescribing: a systematic overview of published assessment tools. Eur J Clin Pharmacol 2014; 70(1): 1-11.
66. Mann E, Bohmdorfer B, Fruhwald T, et al. Potentially inappropriate medication in geriatric patients: the Austrian consensus panel list. Wiener klinische Wochenschrift 2012; 124(5-6): 160-9.
67. By the American Geriatrics Society Beers Criteria Update Expert P. American Geriatrics Society 2015 Updated Beers Criteria for Potentially Inappropriate Medication Use in Older Adults. J Am Geriatr Soc 2015; 63(11): 2227-46.
68. Laroche ML, Charmes JP, Merle L. Potentially inappropriate medications in the elderly: a French consensus panel list. Eur J Clin Pharmacol 2007; 63(8): 725-31.
69. Maio V, Del Canale S, Abouzaid S, Investigators GAP. Using explicit criteria to evaluate the quality of prescribing in elderly Italian outpatients: a cohort study. J Clin Pharm Ther 2010; 35(2): 219-29.
70. Rognstad S, Brekke M, Fetveit A, Spigset O, Wyller TB, Straand J. The Norwegian General Practice (NORGEP) criteria for assessing potentially inappropriate prescriptions to elderly patients. A modified Delphi study. Scand J Prim Health Care 2009; 27(3): 153-9.
71. Chang CB, Yang SY, Lai HY, et al. Using published criteria to develop a list of potentially inappropriate medications for elderly patients in Taiwan. Pharmacoepidemiol Drug Saf 2012; 21(12): 1269-79.
72. Holt S, Schmiedl S, Thurmann PA. Potentially inappropriate medications in the elderly: the PRISCUS list. Dtsch Arztebl Int 2010; 107(31-32): 543-51.
73. Rancourt C, Moisan J, Baillargeon L, Verreault R, Laurin D, Gregoire JP. Potentially inappropriate prescriptions for older patients in long-term care. BMC Geriatr 2004; 4: 9.
74. O'Mahony D, O'Sullivan D, Byrne S, O'Connor MN, Ryan C, Gallagher P. STOPP/START criteria for potentially inappropriate prescribing in older people: version 2. Age Ageing 2015; 44(2): 213-8.
75. Renom-Guiteras A, Meyer G, Thurmann PA. The EU(7)-PIM list: a list of potentially inappropriate medications for older people consented by experts from seven European countries. Eur J Clin Pharmacol 2015; 71(7): 861-75.
76. Winit-Watjana W, Sakulrat P, Kespichayawattana J. Criteria for high-risk medication use in Thai older patients. Arch Gerontol Geriatr 2008; 47(1): 35-51.
77. Lin YJ, Peng LN, Chen LK, Lin MH, Hwang SJ. Risk factors of potentially inappropriate medications among older patients visiting the community health center in rural Taiwan. Arch Gerontol Geriatr 2011; 53(2): 225-8.
78. Chang CB, Yang SY, Lai HY, et al. Application of three different sets of explicit criteria for assessing inappropriate prescribing in older patients: a nationwide prevalence study of ambulatory care visits in Taiwan. BMJ open 2015; 5(11): e008214.