老年患者抗胆碱能药物使用情况横断面研究
A Cross-Sectional Study on the Use of Anticholinergic Drugs in Elderly Patients

作者: 张丽娜 , 刘 璐 , 侯继文 , 张俊青 , 魏亚琳 , 郭宗君 :青岛大学附属医院老年医学科,山东 青岛;

关键词: 老年患者抗胆碱能药物认知负担Elderly Patients Anticholinergic Drugs Cognitive Burden

摘要:
目的:采用横断面研究方法,应用Beers标准及抗胆碱能负担分数(ACB分数)对老年患者抗胆碱能负担进行量化,研究65岁及以上老年住院患者抗胆碱能特性药物使用情况,提高老年人用药安全性和合理性。方法:选取2019年1月~2019年3月青岛某三甲医院老年住院患者859名(男437例,女422例),根据入院诊断将其分为认知障碍组(78例),无认知障碍组(781例)。收集所有患者住院期间抗胆碱能药物详细用药处方,并对药物进行分类,根据抗胆碱能负担量表(ACB量表)对药物进行评分,分为轻度(1分)、中度(2分)和重度(3分)三个等级。总的抗胆碱能认知负担量表评分≥3被认为与临床相关。数据使用Excel2016进行整理,采用SPSS24.0软件进行统计分析,并使用线性回归和logistic回归分析抗胆碱能负担与各相关因素之间的关系。以p < 0.05判定为有统计学意义。结果:65岁及以上的患者中12.9%的患者抗胆碱能认知负担总评分≥3分。共有33种抗胆碱能药物被使用,其中使用频率最高的是心血管疾病药物,这类药物大多属于ACB 1级药物。5-羟色胺抑制剂是使用频率最高的3级药物。认知障碍组临床相关抗胆碱能负担(ACB总分数≥3)发生率为32%,无认知障碍组发生率为11%,二者有显著性差异,其中认知障碍患者较无认知障碍患者更易发生抗胆碱能负担过重,且MMSE得分与抗胆碱能认知负担显著相关(p < 0.05)。单因素线性回归分析ACB分数和用药种类、用药时长、住院天数、住院次数、西药费之间有着显著的正相关关系,与性别、年龄则无明显相关。多分类Logistic回归分析显示用药种类数与抗胆碱能负担呈正相关。结论:老年患者抗胆能药物使用频率较高。认知障碍患者较无认知障碍患者更易暴露于高抗胆碱能药物处方。认知障碍程度与累积抗胆碱能负担之间存在显着相关性,重度认知障碍导致发生临床相关抗胆碱能认知负担的风险增加。当患者使用抗胆碱能药物总负担分数大于等于3时,临床医师应结合Beers标准及患者病情,换用其他ACB分数小于3的药物或抗胆碱能活性较低的药物,将抗胆碱能总分数降低到小于3,减轻抗胆碱能负担。

Abstract: Objective: To quantify the anticholinergic burden of elderly patients by using Beers standard and anticholinergic burden score (ACB score) by cross-sectional study, and to study the use of anticholinergic drugs in elderly inpatients aged 65 and above, so as to improve the safety and rationality of anticholinergic drugs in the elderly. Methods: A total of 859 elderly inpatients (437 males and 422 females) in a third-class hospital in Qingdao from January 2019 to March 2019 were divided into cognitive impairment group (n = 78) and non-cognitive impairment group (n = 781). The detailed prescriptions of anticholinergic drugs in all patients during hospitalization were collected and classified. The drugs were scored according to the anticholinergic burden scale (ACB scale), which were divided into three grades: mild (1), moderate (2) and severe (3). The total anticholinergic burden score of the drug was calculated from the daily mean of ACB score, and the total anticholinergic cognitive burden scale score ≥ 3 was considered to be clinically related. The data were collated by Excel2016 and statistically analyzed by SPSS24.0 software, and the relationship between anticholinergic burden and related factors was analyzed by linear regression and logistic regression. It was statistically significant when p < 0.05. Results: The total score of anticholinergic cognitive burden was ≥ 3 in 12.9% of the patients aged 65 and above. A total of 33 anticholinergic drugs are used, of which cardiovascular disease drugs are the most frequently used, and most of these drugs belong to ACB class 1 drugs. Serotonin inhibitors are the most frequently used class 3 drugs. The incidence of clinical related anticholinergic burden (ACB total score ≥ 3) was 32% in the cognitive impairment group and 11% in the non-cognitive impairment group. The patients with cognitive impairment were more likely to have excessive anticholinergic burden than those without cognitive impairment, and the MMSE score was significantly correlated with the cognitive burden of anticholinergic. Univariate linear regression analysis showed that there was a significant positive correlation between ACB score and the type of medication, the duration of medication, the days of hospitalization, the number of hospitalization and the cost of western medicine, but not with sex and age. Multiple Logistic regression analysis showed that the number of drugs used was positively correlated with anticholinergic burden. Conclusion: The frequency of anticholinergic drugs is higher in elderly patients. Patients with cognitive impairment are more likely to be exposed to high anticholinergic drugs than those without cognitive impairment. There is a significant correlation between the degree of cognitive impairment and cumulative anticholinergic burden. Severe cognitive impairment leads to an increased risk of clinically related anticholinergic cognitive burden. When the total burden score of anticholinergic drugs used by patients is greater than or equal to 3, clinicians should combine the Beers standard and the patient’s condition, switch to other drugs with ACB scores less than 3 or drugs with lower anticholinergic activity, and reduce the total anticholinergic score to less than 3 to reduce the anticholinergic burden.


Abstract:

1. 引言

老年人通常开具抗胆碱能活性的药物,这些药物用于治疗过敏,抑郁,高血压,帕金森氏病,眩晕,哮喘,心血管疾病,失禁,精神病症状和行为问题 [1] [2]。老年人特别容易受到抗胆碱能相关的认知作用的影响,其主要原因有两个:一是由于大部分老年人同时合并多种疾病以及使用多种处方药和非处方药,老年人很可能接触抗胆碱药 [3]。二是由于年龄较大,衰老伴随着肝和肾药物代谢的下降,血脑屏障通透性的增加和中枢胆碱能活性的降低 [4] [5],老年人更容易产生与抗胆碱能有关的严重认知不良反应 [4]。根据治疗效果,我们大致可以将AC药物分为两类:一是抗胆碱能活性作为治疗目的的药物(如抗帕金森药物、抗痉挛药及抗毒蕈碱药物如阿托品等),二是具有抗胆碱能活性,但不为其主要治疗目的的药物(如抗组胺药、抗精神病药和抗抑郁药等),该类药物的抗胆碱能作用在临床中常被忽视。

抗胆碱能药物的非预期抗胆碱能效应会导致许多副作用,其中包括跌倒、认知和功能障碍、住院时间延长以及各种原因的痴呆和死亡的风险增加。由于它们的累计抗胆碱能副作用,所以在某些情况下AC药物的处方可能是不合适的。但迄今为止,国内尚无研究调查该人群中累积抗胆碱能药物暴露量和高抗胆碱能负担及其与人口、社会及临床相关因素之间的关联。我们决定使用更新后的2012抗胆碱认知负担(ACB)量表 [6],对65岁以上老年患者的药物进行统计分析报告。为老年人用药提供实践建议,提高老年人用药安全性和合理性。

2. 研究与方法

2.1. 研究对象

选取2019年1月~2019年3月青岛某三甲医院老年住院患者859名(男437例,女422例)。我们评估了研究期间抗胆碱能药物的暴露情况,并用ACB分数来量化抗胆碱能负担。纳入标准:① 住院患者;② 年龄≥65岁;③ 住院时间≥7天;④ 住院期间至少使用一种具有全身作用的常规药物;⑤ 同意接受评估的患者。排除标准:① 非住院患者;② 年龄<65岁;③ 住院时间<7天;④ 住院期间未使用任何一种抗胆碱能药物或使用的抗胆碱能药物为无全身作用的局部用药;⑤ 拒绝接受评估的患者。

本研究通过青岛大学附属医院伦理委员会审批。

2.2. 研究方法

2.2.1. 收集研究对象一般情况及处方情况

包括性别、年龄、诊断、住院科室、住院天数、住院次数及住院期间详细用药处方情况。

2.2.2. 评估抗胆碱能负担

提取所有患者住院期间抗胆碱能药物详细用药处方,并根据ACB量表对药物进行分类,分为轻度(1分)、中度(2分)和重度(3分)三个等级。在ACB量表上,每种药物的评分等级为1~3。ACB等级为1的药物具有血清抗胆碱能活性,但其对认知的临床影响尚不确定。ACB等级为2或3的药物已产生临床相关的抗胆碱能作用。我们计算了研究期间每位患者每日ACB日均值,作为抗胆碱药的累积暴露量 [7],

计算公式如下: A B C = n = A x ( X × X A C B ) ,当ACB分数日均值在0~0.49

时药物抗胆碱能总负担分数为0,在0.50~1.49时药物抗胆碱能总负担分数为1,在1.50~2.49时药物抗胆碱能总负担分数为2,在大于2.5时药物抗胆碱能总负担分数为≥3分。计算出所有患者ACB分数日均值,并对转化为相应抗胆碱能总负担分数,总的抗胆碱能认知负担量表评分≥3被认为与临床相关 [6]。

2.3. 统计分析

数据使用Excel2016进行整理,采用SPSS24.0软件进行统计分析。以年龄、性别、用药种数、用药时长、住院天数、西药费用为自变量,ACB分数为因变量,使用线性回归分析其相关性。以ACB分数为自变量,使用logistic回归分析,对年龄、性别、用药种数、用药时长、住院天数、西药费用与ACB分数之间关系进行分析。以p < 0.05判定为有统计学意义。

3. 结果

3.1. 研究对象的一般情况

总共纳入859例住院患者,其中男性437人,女性422人,平均年龄为74.51 ± 7.45岁。其中明确诊断为认知障碍(包括痴呆和MCI)的患者78人,无明确认知障碍诊断的患者781人。统计结果显示使用频率最高的是心血管系统药物,使用频率前五的药物分别是茶碱,美托洛尔,卡托普利,硝苯地平,阿普唑仑,详见表1。住院科室排名前五的分别是心血管内科(n = 101),急诊内科(n = 95),呼吸内科(n = 87),老年医学科(n = 77),神经内科(n = 70),50%的研究对象来自这五大科室。其中认知障碍患者绝大部分来自康复科(n = 52/78)。

Table 1. Use of anticholinergic drugs

表1. 抗胆碱能药物使用情况表

3.1.1. 认知障碍组与无认知障碍组临床资料比较

认知障碍组与无认知障碍组各临床资料统计结果比较详见表2

Table 2. Comparison of clinical data between cognitive impairment group and non-cognitive impairment group

表2. 认知障碍组与无认知障碍组临床资料比较

3.1.2. 认知障碍组与无认知障碍组临床相关抗胆碱能负担发生率比较

认知障碍组研究对象临床相关抗胆碱能负担发生率为32%,无认知障碍组为11%,差异有统计学意义(p < 0.05),详见表3

Table 3. Comparison of the incidence of clinically related anticholinergic burden between cognitive impairment group and non-cognitive impairment group

表3. 认知障碍组与无认知障碍组临床相关抗胆碱能负担发生率比较

3.2. 无认知障碍组分析结果

3.2.1. 无认知障碍组临床资料与ACB分数相关性分析结果

K-S检验显示ACB分数,ln年龄,用药种类,用药总时长,住院天数,总住院次数,ln西药费均不具有正态性特质,Spearman相关系数结果显示ACB分数和用药种类、用药时长、住院天数、住院次数、ln西药费之间有着显著的正相关关系(p < 0.01),相关系数r分别为0.62,0.20,0.14,0.13,0.14。ACB分数与性别、ln年龄之间的相关关系无显著性(p > 0.05),ACB分数与性别、ln年龄之间并没有相关关系。

3.2.2. 无认知障碍组临床资料与ACB分数单因素线性回归分析结果

以ACB分数为因变量对数据进行单因素线性回归分析得知,年龄、用药种类、用药总时长、住院天数、总住院次数、西药费会对ACB分数产生显著的正向影响关系(p < 0.01)。分析结果显示性别不会对ACB分数产生影响(p > 0.05)。详见表4

Table 4. Results of univariate linear regression analysis of clinical data and ACB scores of patients without cognitive impairment

表4. 无认知障碍组患者临床资料与ACB分数单因素线性回归分析结果

3.2.3. 无认知障碍组多因素线性回归结果

以ACB分数为因变量,以为年龄、用药种类、用药时长、住院天数、住院次数、西药费为自变量进行多元回归分析,结果显示,老年患者ACB分数与性别、年龄、用药种类、总时长、住院天数、住院次数、西药费无明显相关(p > 0.05),见表5

Table 5. Results of multivariate linear regression in patients without cognitive impairment

表5. 无认知障碍组多因素线性回归结果

3.2.4. 无认知障碍组方差分析及非参数检验结果

差异性分析结果提示不同ACB分数对于性别不会表现差异性(c2 = 1.23, p > 0.05)。方差齐性检验结果示ACB分数对于ln年龄,用药种类,总时长,住院天数,总住院次数,ln西药费呈现出显著性(p < 0.05),即方差不齐,因此使用非参数检验(K-W检验)。结果示ACB分数对于ln年龄,用药种类,总时长,住院天数,总住院次数,ln西药费全部均呈现出显著性(p < 0.05),意味着有明显差异性。结果如表6

Table 6. Results of Kmurw-Wallis test in the group without cognitive impairment

表6. 无认知障碍组K-W检验结果

3.2.5. 无认知障碍组多元Logistic回归分析

我们以ln年龄、用药种类、总时长、住院天数、总住院次数、ln西药费为自变量,ACB分数为因变量进行多元Logistic回归分析,结果如下,详见表7

Table 7. Multiple Logistic regression analysis of patients without cognitive impairment

表7. 无认知障碍组多元Logistic回归分析

从上表可知,将ln年龄,用药种类,总时长,住院天数,总住院次数,ln西药费为自变量,而将ACB分数作为因变量进行多分类Logistic回归分析,Y一共有4项,并且以0作为参照对比项。ACB分数由0变到1时,总时长会对ACB分数产生显著的负向影响关系,OR值为0.838,意味着总时长增加一个单位时,ACB分数减少幅度为0.838倍。ACB分数由0变到2时,用药种类会对ACB分数产生显著的正向影响关系,OR值为14.138,意味着用药种类增加一个单位时,ACB分数增加幅度为14.138倍。ACB分数由0变到2时,总时长会对ACB分数产生显著的负向影响关系,OR值为0.841,意味着总时长增加一个单位时,ACB分数减少幅度为0.841倍。ACB分数由0变到3时,用药种类会对ACB分数产生显著的正向影响关系,OR值为22.202,意味着用药种类增加一个单位时,ACB分数增加幅度为22.202倍。ACB分数由0变到3时,总时长会对ACB分数产生显著的负向影响关系,OR值为0.808,意味着总时长增加一个单位时,ACB分数减少幅度为0.808倍。

3.3. 认知障碍组分析结果

3.3.1. 认知障碍组相关性分析结果

K-S检验显示ACB分数,ln年龄,用药种类,用药总时长,住院天数,总住院次数,ln西药费均不具有正态性,故对认知障碍组患者ACB分数与各因素之间进行相关性分析。结果示ACB分数与用药种类之间有着正相关关系,Spearman相关系数为0.6。同时,ACB分数与性别,ln年龄,总时长,住院天数,住院次数,ln西药费之间没有相关关系,Spearman相关系数接近于0。

3.3.2. 认知障碍组方差分析结果

差异性分析显示用药总时长、住院天数、住院次数、ln西药费方差齐,进一步行方差分析可知ACB分数与以上六种因素均表现一致性,并无差异性。因此行方差分析,如表8

Table 8. Results of analysis of variance in cognitive impairment group

表8. 认知障碍组方差分析结果

Ln年龄、用药种类方差不齐,行非参数检验(K-W检验),结果如表9,并行Dunn’s t检验两两比较,结果示不同ACB分数对于ln年龄不会表现出显著性差异(p > 0.05),ACB分数对于用药种类呈现出显著性差异(p < 0.01)。

Table 9. Results of Kmurw-Wallis test in cognitive impairment group

表9. 认知障碍组K-W检验结果

3.3.3. 逻辑回归分析

只有用药种类显著,进一步使用单因素逻辑回归结果如表10

Table 10. Results of single factor logical regression analysis in cognitive impairment group

表10. 认知障碍组单因素逻辑回归分析结果

从上表可知,将用药种类作为自变量,将ACB分数作为因变量进行多分类Logistic回归分析,Y一共有3项,并且以1作为参照对比项。ACB分数由1变到2时,用药种类会对ACB分数产生显著的正向影响关系,OR值为7.009,意味着用药种类增加一个单位时,ACB分数为增加幅度为7.009倍。ACB分数由1变到3时,用药种类会对ACB分数产生显著的正向影响关系,OR值为8.987,意味着用药种类增加一个单位时,ACB分数增加幅度为8.987倍。

4. 讨论

许多常见慢性病的药物都导致高累积抗胆碱能暴露,认知能力受损且患有常见慢性病的人易受抗胆碱能副作用影响,且更容易暴露于高抗胆碱能负荷,加强这些患者对抗胆碱能药物负担的关注会改善认知障碍患者的健康状况。

通常用于治疗心血管疾病的药物(如美托洛尔、卡托普利)大大增加了患有认知障碍和多病共存老年人的抗胆碱能负担,这一发现与以前的研究结果一致 [8] [9]。多种常见的心血管药物,例如美托洛尔、卡托普利和地高辛,具有抗胆碱能特性,这些药物被广泛用于改善心力衰竭患者(包括认知障碍患者)的症状,预防住院并延长其生存期 [8]。这些药物的ACB分数较低,但经常共同开处方,导致ACB分数达到3或更高。大量的抗胆碱能负担可能是由于接触多种ACB 1级药物引起的,而心血管药物的累积抗胆碱能特性可能是导致心力衰竭患者认知障碍发生率增加的重要原因 [8] [9] [10]。

我们还发现,患有MCI和痴呆症的患者经常接触多种中枢神经系统活性药物,包括三环类抗抑郁药,选择性5-羟色胺再摄取抑制剂,苯二氮卓类,抗精神病药和阿片类药物。抑郁症,烦躁和睡眠问题在痴呆症患者中很普遍,患病率从25%增至45%,并且老年人中神经系统活性药物的使用有所增加 [11] [12]。然而,对于具有痴呆的人抗抑郁药物治疗有效性的证据是不足的,精神药物可导致痴呆症患者认知功能下降更快 [13] [14] [15],共同开具其他可能导致抗胆碱能负担的慢性疾病的药物(例如β-受体阻滞剂,利尿剂和华法林)可能会加剧这种风险。

我们的发现还强调了潜在的处方药级联反应,增加了认知障碍患者受到伤害的风险。例如,许多患者同时服用利尿剂和膀胱抗毒蕈碱药,其他人开了口服类固醇激素,以及抗精神病药物如喹硫平和镇静药物如异丙嗪,可能会导致谵妄或精神病。慢性疼痛、抑郁和行动不便可能导致痴呆患者功能失禁,使用利尿剂可能会加重病情,大小便失禁反过来可能导致抑郁加剧。用于治疗失禁的膀胱抗毒蕈碱药可能会加剧平衡问题和跌倒。

我们研究的优势在于大量老年住院患者使用多种抗胆碱能药物,但我们对住院患者存在多种抗胆碱能药物处方联合使用所致高抗胆碱能负荷的关注仍然不够。诸多研究已经证明老年痴呆患者累计高抗胆碱能负担 [16] [17] [18],我们发现,19%的诊断为认知障碍的老年住院患者至少长期使用一种ACB 2级或3抗胆碱能药物,这与老年患者的认知功能受损的其他研究结果一致 [19] [20] [21]。我们的分析基于医院系统数据,这些数据可能比患者自我报告更好地记录了患者对药物的使用情况 [22]。

我们的研究也有一些局限性。医院系统中的处方模式可能无法代表其他机构中的处方模式,我们的调查结果反映了对住院患者处方的最佳实践建议,但无法涵盖患者在院外通过其他途径所获得的药物。尽管我们选择使用ACB量表,但是没有金标准来定义具有抗胆碱能作用的药物,而且ACB量表并未考虑剂量和剂型。我们使用住院诊断数据区分患者是否患有认知障碍(MCI或痴呆)的能力有限,并且在我们的数据中两者均可能被漏报 [23]。

多种因素可能导致处方抗胆碱能负担增加,其中包括关注患者主诊断,忽略对认知障碍的预防和治疗;无法获得社区长期护理服务;护理人员负担过重以及多个临床医生开出的零散处方 [24] [25] [26]。各科室临床工作者之间互相交流,关注患者多病共存,多重用药的情况显得尤为重要。

治疗多种慢性疾病的药物是否对患者是真正“必要”,这一问题应该不断地重新衡量和考虑,随着认知发生变化,及时调整治疗方案。临床医生应牢记,MCI和痴呆都与预期寿命缩短独立相关 [27],会影响有关治疗的决定,从而延长治疗时间。由于使用了Beers标准 [6],临床医生可能已经意识到,有认知障碍的老年人应该避免服用高抗胆碱能药物,未来的研究应该调查不同剂量及剂型抗胆碱能药物引起的累积抗胆碱能负担是否与认知障碍患者的不良健康结果相关。与此同时,医师开处方时应该意识到,许多用于治疗老年患者多病共存的药物都有继发性、非治疗目的的抗胆碱能作用。如果同时使用这些药物可能导致显著的抗胆碱能作用,从而对该群体的健康产生负面影响。

NOTES

*通讯作者。

文章引用: 张丽娜 , 刘 璐 , 侯继文 , 张俊青 , 魏亚琳 , 郭宗君 (2020) 老年患者抗胆碱能药物使用情况横断面研究。 临床医学进展, 10, 1074-1084. doi: 10.12677/ACM.2020.106162

参考文献

[1] Vangala, V.R. and Tueth, M.J. (2003) Chronic Anticholinergic Toxicity. Identification and Management in Older Patients. Geriatrics, 58, 36-37.

[2] Roe, C.M., Anderson, M.J. and Spivack, B. (2002) Use of Anticholinergic Medications by Older Adults with Dementia. Journal of the American Geriatrics Society, 50, 836-842.
https://doi.org/10.1046/j.1532-5415.2002.50208.x

[3] Mulsant, B.H., Pollock, B.G., Kirshner, M., et al. (2003) Serum Anticholinergic Activity in a Community-Based Sample of Older Adults: Relationship with Cognitive Performance. Archives of General Psychiatry, 60, 198-203.
https://doi.org/10.1001/archpsyc.60.2.198

[4] Tune, L.E. (2001) Anticholinergic Effects of Medication in Elderly Patients. The Journal of Clinical Psychiatry, 62, 11-14.

[5] Dyer, C.B., Ashton, C.M. and Teasdale, T.A. (1995) Postoperative Delirium. A Review of 80 Primary Data-Collection Studies. Archives of Internal Medicine, 155, 461-465.
https://doi.org/10.1001/archinte.1995.00430050035004

[6] American Geriatrics Society (2012) American Geriatrics Society Updated Beers Criteria for Potentially Inappropriate Medication Use in Older Adults. Journal of the American Geriatrics Society, 60, 616-631.
https://doi.org/10.1111/j.1532-5415.2012.03923.x

[7] Campbell, N.L., Boustani, M.A., Lane, K.A., et al. (2010) Use of Anticholinergics and the Risk of Cognitive Impairment in an African American Population. Neurology, 75, 152-159.
https://doi.org/10.1212/WNL.0b013e3181e7f2ab

[8] Shaukat, A., Habib, A., Lane, K.A., et al. (2014) Anticholinergic Medications: An Additional Contributor to Cognitive Impairment in the Heart Failure Population? Drugs & Aging, 31, 749-754.
https://doi.org/10.1007/s40266-014-0204-2

[9] Parkinson, L., Magin, P.J., Thomson, A., et al. (2015) Anticholinergic Burden in Older Women: Not Seeing the Wood for the Trees? The Medical Journal of Australia, 202, 91-94.
https://doi.org/10.5694/mja14.00336

[10] Green, A.R., Oh, E., Hilson, L., et al. (2016) Anticholinergic Burden in Older Adults with Mild Cognitive Impairment. Journal of the American Geriatrics Society, 64, e313-e314.
https://doi.org/10.1111/jgs.14554

[11] Gerlach, L.B., Olfson, M., Kales, H.C., et al. (2017) Opioids and Other Central Nervous System-Active Polypharmacy in Older Adults in the United States. Journal of the American Geriatrics Society, 65, 2052-2056.
https://doi.org/10.1111/jgs.14930

[12] Bhattacharjee, S., Oh, Y.M., Reiman, E.M., et al. (2017) Prevalence, Patterns, and Predictors of Depression Treatment among Community-Dwelling Elderly Individuals with Dementia in the United States. The American Journal of Geriatric Psychiatry, 25, 803-813.
https://doi.org/10.1016/j.jagp.2017.03.003

[13] Rosenberg, P.B., Mielke, M.M., Han, D., et al. (2012) The Association of Psychotropic Medication Use with the Cognitive, Functional, and Neuropsychiatric Trajectory of Alzheimer’s Disease. International Journal of Geriatric Psychiatry, 27, 1248-1257.
https://doi.org/10.1002/gps.3769

[14] Orgeta, V., Qazi, A., Spector, A., et al. (2015) Psychological Treatments for Depression and Anxiety in Dementia and Mild Cognitive Impairment: Systematic Review and Meta-Analysis. The British Journal of Psychiatry, 207, 293-298.
https://doi.org/10.1192/bjp.bp.114.148130

[15] Nelson, J.C. and Devanand, D.P. (2011) A Systematic Review and Meta-Analysis of Placebo-Controlled Antidepressant Studies in People with Depression and Dementia. Journal of the American Geriatrics Society, 59, 577-585.
https://doi.org/10.1111/j.1532-5415.2011.03355.x

[16] Chatterjee, S., Mehta, S., Sherer, J.T., et al. (2010) Prevalence and Predictors of Anticholinergic Medication Use in Elderly Nursing Home Residents with Dementia: Analysis of Data from the 2004 National Nursing Home Survey. Drugs & Aging, 27, 987-997.
https://doi.org/10.2165/11584430-000000000-00000

[17] Palmer, J.B., Albrecht, J.S., Park, Y., et al. (2015) Use of Drugs with Anticholinergic Properties among Nursing Home Residents with Dementia: A National Analysis of Medicare Beneficiaries from 2007 to 2008. Drugs & Aging, 32, 79-86.
https://doi.org/10.1007/s40266-014-0227-8

[18] Kolanowski, A., Fick, D.M., Campbell, J., et al. (2009) A Preliminary Study of Anticholinergic Burden and Relationship to a Quality of Life Indicator, Engagement in Activities, in Nursing Home Residents with Dementia. J Am Med Dir Assoc, 10, 252-257.
https://doi.org/10.1016/j.jamda.2008.11.005

[19] Bhattacharya, R., Chatterjee, S., Carnahan, R.M., et al. (2011) Prevalence and Predictors of Anticholinergic Agents in Elderly Outpatients with Dementia. The American Journal of Geriatric Pharmacotherapy, 9, 434-441.
https://doi.org/10.1016/j.amjopharm.2011.10.001

[20] Carnahan, R.M., Lund, B.C., Perry, P.J., et al. (2006) The Anticholinergic Drug Scale as a Measure of Drug-Related Anticholinergic Burden: Associations with Serum Anticholinergic Activity. Journal of Clinical Pharmacology, 46, 1481-1486.
https://doi.org/10.1177/0091270006292126

[21] Chatterjee, S., Mehta, S., Sherer, J.T., et al. (2010) Prevalence and Predictors of Anticholinergic Medication Use in Elderly Nursing Home Residents with Dementia: Analysis of Data from the 2004 National Nursing Home Survey. Drugs & Aging, 27, 987-997.
https://doi.org/10.2165/11584430-000000000-00000

[22] Sattler, E.L.P., Lee, J.S. and Perri III, M. (2013) Medication (Re)fill Adherence Measures Derived from Pharmacy Claims Data in Older Americans: A Review of the Literature. Drugs & Aging, 30, 383-399.
https://doi.org/10.1007/s40266-013-0074-z

[23] Østbye, T., Taylor Jr., D.H., Clipp, E.C., et al. (2008) Identification of Dementia: Agreement among National Survey Data, Medicare Claims, and Death Certificates. Health Services Research, 43, 313-326.
https://doi.org/10.1111/j.1475-6773.2007.00748.x

[24] Hinton, L., Franz, C.E., Reddy, G., et al. (2007) Practice Constraints, Behavioral Problems, and Dementia Care: Primary Care Physicians’ Perspectives. Journal of General Internal Medicine, 22, 1487-1492.
https://doi.org/10.1007/s11606-007-0317-y

[25] Matlow, J.N., Bronskill, S.E., Gruneir, A., et al. (2017) Use of Medications of Questionable Benefit at the End of Life in Nursing Home Residents with Advanced Dementia. Journal of the American Geriatrics Society, 65, 1535-1542.
https://doi.org/10.1111/jgs.14844

[26] Reppas-Rindlisbacher, C.E., Fischer, H.D., Fung, K., et al. (2016) Anticholinergic Drug Burden in Persons with Dementia Taking a Cholinesterase Inhibitor: The Effect of Multiple Physicians. Journal of the American Geriatrics Society, 64, 492-500.
https://doi.org/10.1111/jgs.14034

[27] Sachs, G.A., Carter, R., Holtz, L.R., et al. (2011) Cognitive Impairment: An Independent Predictor of Excess Mortality: A Cohort Study. Annals of Internal Medicine, 155, 300-308.
https://doi.org/10.7326/0003-4819-155-5-201109060-00007

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