臺灣博碩士論文加值系統

研究背景：老年病患在中風後有一定的比例會出現幻覺、妄想、激動不安與混亂行為等精神症狀(psychosis)。依據過去文獻與治療指引，老年中風病患出現相關精神症狀時，抗精神病藥物為第一線治療藥物，而美國食品藥物管理局曾提出警告，在老年族群使用抗精神病藥物治療會增加病患的死亡風險。然而過去並沒有在老年中風病患使用抗精神病藥物治療之療效與安全性的臨床試驗，僅有極少數之觀察性研究結果報告其安全性，但這些研究受到選擇性偏差(selection bias)、不死時間偏差(immortal time bias)及未測量干擾因子(unmeasured confounders)等的影響，導致用藥的安全性仍然有爭議性。

研究目的：運用active comparator and new user design with external adjustment方法評估抗精神病藥物使用型態和死亡風險在老年中風病患族群。

研究方法：研究設計與族群：運用回溯性世代研究法，從全民健康保險資料庫中篩選65歲以上因腦中風住院之病患，並排除以下之條件：(1)出院日期之前365天內有接受任何抗精神病藥物治療的紀錄、(2)年齡和性別變項遺漏或不明、(3)住院期間發生死亡者。再以病患出院日期為起始點，追蹤病患直至開始接受精神病藥物治療止。病患開始接受抗精神病藥物治療的日期則為指標日期(index date)。再連結多中心中風登錄資料，擷取病患在出院時之抽菸史、BMI、美國國家衛生院中風量表(NIHSS)、巴氏量表(Barthel Index)和modified Rankin Scale (mRS)等中風疾病嚴重程度和失能程度分數。暴露：健保給付之抗精神病藥物。主要結果：1年內全死因死亡情況。次要結果：1年內特定死因死亡情況。統計方法：運用描述性統計方法摘要研究族群的人口學變項、基本特性和抗精神病藥物之處方型態；運用Cox proportional hazard models結合傾向分數修正法(propensity score calibration, PSC)控制未測量干擾因子之影響，並估計老年中風病患接受不同抗精神藥物治療之死亡風險差異，以及特定死因風險。為避免資料違反PSC的統計假設- surrogacy假設，本研究另外引入另一個外部資料校正方法-二階段修正法(two-stage calibration, TSC)，此方法無須符合surrogacy假設，借由TSC方法確認PSC的結果，並比較PSC與TSC之間方法差異。

結果：經納入與排除條件之篩選後，共72,441人在中風後開始接受抗精神病藥物治療，用藥的累積發生率約26.2% (2002-2015)，大部分的病患是接受單一抗精神病藥物治療 (99%)，而最常使用的抗精神病藥物為quetiapine (39.9%)。在死亡風險方面，本研究選擇過去文獻常用於治療中風後精神症狀之藥物，包括quetiapine、haloperidol和risperidone。經PSC校正後，相較於quetiapine，病患接受haloperidol [adjusted hazard ratio (aHR)=1.22; 95% confidence interval (CI) 1.18-1.27]和risperidone (aHR=1.31; 95% CI 1.24-1.38)治療則有較高的死亡風險，其中haloperidol和risperidone呈現與劑量-反應正相關之死亡風險。敏感度分析結果顯示，其結果與主要結果相似。在特定死因死亡風險方面，經PSC校正後，相較於quetiapine，病患接受risperidone (aHR=1.25; 1.14-1.38)和sulpiride (aHR=1.35; 95% CI 1.16-1.57)治療則有較高腦-心血管特定死因風險，而在haloperidol則不顯著。此外，經surrogacy假設檢定，其資料並未違反PSC的統計假設，因此PSC與TSC方法校正後之結果相似。

結論：抗精神病藥物治療的死亡風險，在老年中風病患族群中，個別藥品之間存在著差異性，這些差異性會因為選擇的藥品及劑量的不同，而影響病患存活。因此藥品選擇與劑量上應考量病患狀況與小心調整劑量，可減少死亡風險的發生。另外，運用PSC方法修正未測量干擾因子時，須檢查是否違反PSC統計假設，若違反假設，PSC得到的結果反而會造成更大的偏差。因此當資料違反PSC的統計假設時，使用TSC的方法得到的結果會比PSC更為準確。

Background: Elderly patients are at risk for developing psychosis after stroke, including delusions, hallucinations, agitation, and disorganized behavior. According to previous guidelines, antipsychotics are the first-line pharmacological intervention for psychosis, but elderly patients who are treated with antipsychotics might have an increased risk of mortality based on US FDA safety communications. However, there are limited studies examining mortality risk associated with antipsychotic use in elderly patients who have had a stroke. The major limitations of these studies include selection bias, immortal time bias, and unmeasured confounders, which can lead to bias related to the relative risks of antipsychotic treatment and result in controversial findings.

Objectives: To evaluate prescription patterns and comparative mortality risk of antipsychotic use in elderly patients after a stroke by using an active comparator and new user design with an external adjustment method.

Methods, design and setting: We conducted a retrospective cohort study to identify patients aged above 65 years old admitted for stroke in the National Health Insurance Database (NHID) from 2002 to 2014. These patients were not prescribed antipsychotics before their discharge date and were followed until they started to receive antipsychotic treatment. The date of antipsychotic use was set as the index date. The covariates were retrieved from claims during the one-year look-back period prior to the index date. We then linked to multi-center stroke registry databases to retrieve additional variables, including smoking history, body mass index, National Institute of Health Stroke Scale (NHISS), the Barthel index, and the modified Rankin Scale (mRS). Exposure: Antipsychotics covered by the NHI program. Main outcome: One-year all-cause mortality. Secondary outcome: One-year cause-specific mortality. Statistical analysis: Descriptive statistics were used to characterize the baseline demographics and antipsychotic prescription patterns. To compare antipsychotics with respect to risk of all-cause and cause-specific mortality, we performed Cox proportional hazard models using the propensity score calibration (PSC) method to adjust for unmeasured confounders in order to estimate the relative risk among antipsychotics in elderly stroke patients. In addition, in order to avoid the surrogacy assumption due to the use of the PSC method, the two-stage calibration (TSC) method (without the surrogacy assumption) was used to adjust unmeasured confounders and to compare the differences between the PSC and TSC methods.

Results: There were 72,441 elderly stroke patients who initiated treatment with antipsychotics during the study period. The proportion of incident use of antipsychotics was 26.2% (2002-2015). The majority of the elderly stroke patients had received only a single antipsychotic treatment (99%), and the most commonly used antipsychotic was quetiapine (39.9%). We selected the antipsychotics, including quetiapine, haloperidol and risperidone, which were prescribed for post-stroke psychosis treatment in previous literature on this topic, and compared the mortality risk among these antipsychotics. In the PSC-adjusted intent to treat analyses, haloperidol [adjusted hazard ratio (aHR)=1.22; 95% confidence interval (CI) 1.18-1.27] and risperidone (aHR=1.31; 95% CI 1.24-1.38) users had a higher mortality risk as compared to quetiapine users. Haloperidol and risperidone exhibited a dose-response related to mortality risk after controlling for confounders. The sensitivity analyses assessing the influence of the study population showed similar patterns. In the cause-specific mortality analyses, risperidone (aHR=1.25; 95% CI 1.14-1.38) users had higher cause-specific mortality from cerebro-cardiovascular disease compared to quetiapine users, but there were no significant differences found in the haloperidol (aHR=1.04 95% CI 0.97-1.12) and quetiapine (reference) users. In addition, we found that the surrogacy assumption was not violated. PSC and TSC methods exhibited similar results in terms of mortality risk related to the use of antipsychotics.

Conclusions: The significant variations in the differences in mortality risk among antipsychotic agents suggests that antipsychotic selection and dosing may affect survival in elderly stroke patients. In addition, we also found the surrogacy assumption should be tested to determine whether the assumption is violated when the PSC method is performed to adjust for unmeasured confounders. If this assumption is violated, PSC is far less useful and may even increase bias. When the PSC assumption is violated, the TSC method can provide more precise treatment effects than PSC.

摘要 I
Summary IV
Chapter 1 Background 1
1.1 Global Burden of Stroke 1
1.1.1 Stroke Mortality and Disability 1
1.1.2 Medical complications of stroke 2
1.1.3 Neuropsychiatric Symptoms of Stroke 3
1.2 Post-Stroke Psychosis 5
1.2.1 Clinical description of psychosis 5
1.2.2 Epidemiology of Post-Stroke Psychosis 5
1.2.3 Mechanisms Leading to Post-Stroke Psychosis 7
1.3 Pharmacological Treatment for Patients with Post-Stroke Psychosis 9
1.3.1 Classification and Mechanism of Antipsychotics 9
1.3.2 Antipsychotic Treatment of Post-Stroke Psychosis 11
1.3.3 Dilemma of Antipsychotic Treatment in Stroke Patients 12
1.3.4 Threat to Causal Inferences in Observational Studies 13
1.4 Active Comparator and New User Design with External Adjustment 16
1.5 Knowledge Gap 19
1.6 Objectives and Aims 20
Chapter 2 Methods 21
2.1 Data Source 21
2.1.1 Health and Welfare Database 21
2.1.2 Multi-Center Stroke Registry Database 22
2.1.3 Representative of the Multi-Center Stroke Registry Database 22
2.2 Design, Setting, and Study population 24
2.3 Antipsychotics and Follow-Up 26
2.4 Outcomes 27
2.5 Statistical Analysis 28
2.5.1 Descriptive Statistics 28
2.5.2 Inferential Statistics 28
2.5.3 Propensity Score Calibration (PSC) 29
2.5.4 Sensitivity and Subgroup Analysis 31
Chapter 3 Results 33
3.1 Heterogeneity between the National Health Insurance Database and the Multi-Center Stroke Registry Database 33
3.2 Prescribing Patterns of Antipsychotics in Elderly Stroke patients 33
3.2.1 Characteristics of Stroke Patients Treated with Antipsychotics and Those Not Treated with Antipsychotics 33
3.2.2 Prescribing Patterns of Use of antipsychotics 34
3.3 Comparative One-Year Mortality Risk of the Use of antipsychotics in Elderly Stroke patients 36
3.3.1 Baseline Characteristics of the Study Population 36
3.3.2 Comparative Mortality Risk of Use of antipsychotics 37
3.3.3 Sensitivity and Subgroup Analyses 39
3.4 Cause of Death for Use of antipsychotics in Elderly Stroke patients 41
3.4.1 Proportional Mortality of Study Population 41
3.4.2 Comparative Cause-Specific Mortality Risk of Use of antipsychotics 41
3.5 Comparison of Methods for Adjusting Unmeasured Confounding Variables with Validation Data 42
Chapter 4 Discussion 43
4.1 Heterogeneity between the National Health Insurance Database and the Multi-center Stroke Registry Database 43
4.2 Prescribing Patterns of Use of Antipsychotics in Elderly Stroke patients 43
4.3 Comparative One-Year Mortality Risk of Use of antipsychotics in Elderly Stroke patients 45
4.4 Comparison of Methods for Adjusting Unmeasured Confounders with Validation Data 48
4.5 Strengths and Limitations 52
Chapter 5 Implications for Clinical Practice 53
Chapter 6 Conclusions 53
References 54
List of Tables 59
List of Figures 103
Appendix I Two-Stage Calibration (TSC) 126
Appendix II Ethical Approval Documentation for This Doctoral Dissertation. 128

1.Hackett ML, Kohler S, O'Brien JT, Mead GE. Neuropsychiatric outcomes of stroke. The Lancet Neurology. 2014;13(5):525-534.
2.Strong K, Mathers C, Bonita R. Preventing stroke: saving lives around the world. The Lancet Neurology. 2007;6(2):182-187.
3.Sidney S, Quesenberry CP, Jr, Jaffe MG, et al. Recent trends in cardiovascular mortality in the united states and public health goals. JAMA Cardiology. 2016;1(5):594-599.
4.衛生福利部統計處. 民國105年死因統計年報. 2016.
5.Ferro JM, Caeiro L, Figueira ML. Neuropsychiatric sequelae of stroke. Nature reviews Neurology. 2016;12(5):269-280.
6.Bejot Y, Daubail B, Giroud M. Epidemiology of stroke and transient ischemic attacks: Current knowledge and perspectives. Revue neurologique. 2016;172(1):59-68.
7.Feigin VL, Norrving B, Mensah GA. Global Burden of Stroke. Circulation research. 2017;120(3):439-448.
8.Feigin VL, Roth GA, Naghavi M, et al. Global burden of stroke and risk factors in 188 countries, during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet Neurology. 2016;15(9):913-924.
9.Kumar S, Selim MH, Caplan LR. Medical complications after stroke. The Lancet Neurology. 2010;9(1):105-118.
10.Davenport RJ, Dennis MS, Wellwood I, Warlow CP. Complications after acute stroke. Stroke; a journal of cerebral circulation. 1996;27(3):415-420.
11.Langhorne P, Stott DJ, Robertson L, et al. Medical complications after stroke: a multicenter study. Stroke; a journal of cerebral circulation. 2000;31(6):1223-1229.
12.Leys D, Henon H, Mackowiak-Cordoliani MA, Pasquier F. Poststroke dementia. The Lancet Neurology. 2005;4(11):752-759.
13.Brainin M, Tuomilehto J, Heiss WD, et al. Post-stroke cognitive decline: an update and perspectives for clinical research. European journal of neurology. 2015;22(2):229-238, e213-226.
14.McKevitt C, Fudge N, Redfern J, et al. Self-reported long-term needs after stroke. Stroke; a journal of cerebral circulation. 2011;42(5):1398-1403.
15.McManus J, Pathansali R, Stewart R, Macdonald A, Jackson S. Delirium post-stroke. Age and ageing. 2007;36(6):613-618.
16.Sun JH, Tan L, Yu JT. Post-stroke cognitive impairment: epidemiology, mechanisms and management. Annals of translational medicine. 2014;2(8):80.
17.Robinson RG. Neuropsychiatric consequences of stroke. Annual review of medicine. 1997;48:217-229.
18.Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub; 2013.
19.Charlton BG, Kavanau JL. Delirium and psychotic symptoms--an integrative model. Medical hypotheses. 2002;58(1):24-27.
20.Rabins PV, Starkstein SE, Robinson RG. Risk factors for developing atypical (schizophreniform) psychosis following stroke. The Journal of neuropsychiatry and clinical neurosciences. 1991;3(1):6-9.
21.Almeida OP, Xiao J. Mortality associated with incident mental health disorders after stroke. The Australian and New Zealand journal of psychiatry. 2007;41(3):274-281.
22.Francis J. Delirium: Acute Confusional States. JAMA. 1991;265(5):653-653.
23.Shi Q, Presutti R, Selchen D, Saposnik G. Delirium in acute stroke: a systematic review and meta-analysis. Stroke; a journal of cerebral circulation. 2012;43(3):645-649.
24.Stahl SM. Stahl's essential psychopharmacology: neuroscientific basis and practical applications. Cambridge university press; 2013.
25.Gupta M, Dasgupta A, Khwaja GA, Chowdhury D, Patidar Y, Batra A. Behavioural and psychological symptoms in poststroke vascular cognitive impairment. Behavioural neurology. 2014;2014:430128.
26.Cerejeira J, Lagarto L, Mukaetova-Ladinska EB. Behavioral and psychological symptoms of dementia. Front Neurol. 2012;3:73.
27.Stangeland H, Orgeta V, Bell V. Poststroke psychosis: a systematic review. Journal of Neurology, Neurosurgery ＆ Psychiatry. 2018.
28.Kumral E, Ozturk O. Delusional state following acute stroke. Neurology. 2004;62(1):110-113.
29.Reeves MJ, Bushnell CD, Howard G, et al. Sex differences in stroke: epidemiology, clinical presentation, medical care, and outcomes. The Lancet Neurology. 2008;7(10):915-926.
30.Graff-Radford J, Whitwell JL, Geda YE, Josephs KA. Clinical and imaging features of Othello's syndrome. European journal of neurology. 2012;19(1):38-46.
31.Devine MJ, Bentley P, Jones B, et al. The role of the right inferior frontal gyrus in the pathogenesis of post-stroke psychosis. Journal of neurology. 2014;261(3):600-603.
32.Levine DN, Grek A. The anatomic basis of delusions after right cerebral infarction. Neurology. 1984;34(5):577-577.
33.Darby RR, Laganiere S, Pascual-Leone A, Prasad S, Fox MD. Finding the imposter: brain connectivity of lesions causing delusional misidentifications. Brain : a journal of neurology. 2017;140(2):497-507.
34.Riordan HJ, Antonini P, Murphy MF. Atypical antipsychotics and metabolic syndrome in patients with schizophrenia: risk factors, monitoring, and healthcare implications. American health ＆ drug benefits. 2011;4(5):292-302.
35.Herrmann N, Mamdani M, Lanctot KL. Atypical antipsychotics and risk of cerebrovascular accidents. The American journal of psychiatry. 2004;161(6):1113-1115.
36.Kuehn BM. FDA warns antipsychotic drugs may be risky for elderly. Jama. 2005;293(20):2462.
37.Setoguchi S, Wang PS, Alan Brookhart M, Canning CF, Kaci L, Schneeweiss S. Potential causes of higher mortality in elderly users of conventional and atypical antipsychotic medications. Journal of the American Geriatrics Society. 2008;56(9):1644-1650.
38.Wang JY, Wang CY, Tan CH, Chao TT, Huang YS, Lee CC. Effect of different antipsychotic drugs on short-term mortality in stroke patients. Medicine. 2014;93(25):e170.
39.Jennum P, Baandrup L, Iversen HK, Ibsen R, Kjellberg J. Mortality and use of psychotropic medication in patients with stroke: a population-wide, register-based study. BMJ Open. 2016;6(3):e010662.
40.Salas M, Hofman A, Stricker BH. Confounding by indication: an example of variation in the use of epidemiologic terminology. Am J Epidemiol. 1999;149(11):981-983.
41.Levesque LE, Hanley JA, Kezouh A, Suissa S. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ (Clinical research ed). 2010;340:b5087.
42.Sturmer T, Glynn RJ, Rothman KJ, Avorn J, Schneeweiss S. Adjustments for unmeasured confounders in pharmacoepidemiologic database studies using external information. Med Care. 2007;45(10 Supl 2):S158-165.
43.Schneeweiss S, Setoguchi S, Brookhart MA, Kaci L, Wang PS. Assessing residual confounding of the association between antipsychotic medications and risk of death using survey data. CNS drugs. 2009;23(2):171-180.
44.Yoshida K, Solomon DH, Kim SC. Active-comparator design and new-user design in observational studies. Nature reviews Rheumatology. 2015;11(7):437-441.
45.Stürmer T, Schneeweiss S, Avorn J, Glynn RJ. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. American journal of epidemiology. 2005;162(3):279-289.
46.Lin HW, Chen YH. Adjustment for missing confounders in studies based on observational databases: 2-stage calibration combining propensity scores from primary and validation data. Am J Epidemiol. 2014;180(3):308-317.
47.Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiology and drug safety. 2006;15(5):291-303.
48.Sung SF, Hsieh CY, Kao Yang YH, et al. Developing a stroke severity index based on administrative data was feasible using data mining techniques. Journal of clinical epidemiology. 2015;68(11):1292-1300.
49.MOHW. Health and Welfare Data Science Center. https://dep.mohw.gov.tw/DOS/np-2497-113.html, 2018.
50.Hsieh FI, Lien LM, Chen ST, et al. Get With the Guidelines-Stroke performance indicators: surveillance of stroke care in the Taiwan Stroke Registry: Get With the Guidelines-Stroke in Taiwan. Circulation. 2010;122(11):1116-1123.
51.Cheng CL, Kao YH, Lin SJ, Lee CH, Lai ML. Validation of the National Health Insurance Research Database with ischemic stroke cases in Taiwan. Pharmacoepidemiology and drug safety. 2011;20(3):236-242.
52.Leucht S, Samara M, Heres S, Davis JM. Dose Equivalents for Antipsychotic Drugs: The DDD Method. Schizophr Bull. 2016;42 Suppl 1:S90-94.
53.Gerhard T, Huybrechts K, Olfson M, et al. Comparative mortality risks of antipsychotic medications in community-dwelling older adults. The British journal of psychiatry : the journal of mental science. 2014;205(1):44-51.
54.Kales HC, Kim HM, Zivin K, et al. Risk of mortality among individual antipsychotics in patients with dementia. The American journal of psychiatry. 2012;169(1):71-79.
55.衛生福利部統計處. 死因統計. 2018; https://dep.mohw.gov.tw/DOS/np-1776-113.html.
56.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.
57.So Y, Lin G, Johnston G. Using the PHREG procedure to analyze competing-risks data. Paper presented at: SAS Global Forum2014.
58.De Sousa A. Psychiatric issues in renal failure and dialysis. Indian journal of nephrology. 2008;18(2):47-50.
59.Kester R, Unutzer J, Hogan D, Huang H. Antipsychotic prescribing patterns in a Medicare Advantage population of older individuals with dementia. Journal of mental health (Abingdon, England). 2017;26(2):167-171.
60.Targum SD. Treating Psychotic Symptoms in Elderly Patients. Primary care companion to the Journal of clinical psychiatry. 2001;3(4):156-163.
61.Maust DT, Kim HM, Seyfried LS, et al. Antipsychotics, other psychotropics, and the risk of death in patients with dementia: number needed to harm. JAMA psychiatry. 2015;72(5):438-445.
62.Faurot KR, Jonsson Funk M, Pate V, et al. Using claims data to predict dependency in activities of daily living as a proxy for frailty. Pharmacoepidemiology and drug safety. 2015;24(1):59-66.
63.Osborn D, Marston L, Nazareth I, King MB, Petersen I, Walters K. Relative risks of cardiovascular disease in people prescribed olanzapine, risperidone and quetiapine. Schizophrenia research. 2017;183:116-123.
64.Sturmer T, Schneeweiss S, Rothman KJ, Avorn J, Glynn RJ. Performance of propensity score calibration--a simulation study. Am J Epidemiol. 2007;165(10):1110-1118.
65.Steinberg M, Lyketsos CG. Atypical antipsychotic use in patients with dementia: managing safety concerns. The American journal of psychiatry. 2012;169(9):900-906.