Baseline Predictors of Antipsychotic Treatment Continuation and Response at Week 8 in Patients with Alzheimer’s Disease with Psychosis or Aggressive Symptoms: An Analysis of the CATIE-AD Study

Abstract

Background/Objective:

The aim of the present study was to investigate predictors of atypical antipsychotic (AAP) treatment continuation and response by week 8 in patients with Alzheimer’s disease (AD) who have psychotic/aggressive symptoms using the Clinical Antipsychotic Trials of Intervention Effectiveness–Alzheimer’s Disease (CATIE-AD) dataset.

Methods:

Clinical data was utilized from 421 AD outpatients with psychotic/aggressive symptoms who needed interventional treatment. Logistic regression analyses were performed to examine which baseline sociodemographic and clinical characteristics contributed to treatment ‘continuation’ and ‘response’, the latter of which was evaluated by the Clinical Global Impression of Change (CGI-C), Neuropsychiatric Inventory (NPI), and Brief Psychiatric Scale (BPRS).

Results:

The treatment continuation rate was 48.7%, and CGI-C, NPI, and BPRS response rate by the last observation carried forward method were 42.7%, 48.6%, and 37.5%, respectively. No significant predictor was identified for treatment continuation in the Caucasian patients (n = 331), while better treatment response was predicted by a lower Mini-Mental State Examination score, treatment with risperidone (versus olanzapine and quetiapine), history of diabetes mellitus, healthier physical status, and more severe initial psychotic symptoms.

Conclusions:

Comparatively high intolerability from AAPs in the short term was confirmed. We found that baseline clinical predictors to treatment response in Caucasian AD patients with psychotic/aggressive symptoms include treatment with risperidone (versus quetiapine and olanzapine), diabetes mellitus, global physical status, cognitive impairment, and psychotic symptoms. Going forward, these findings may help to determine treatment strategies or care plans.

Keywords

Aggression Alzheimer’s disease atypical antipsychotic neuropsychiatric symptom psychosis

INTRODUCTION

Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is characterized by specific cognitive dysfunctions [1, 2]. During the course of this disease, various neuropsychiatric symptoms (NPSs) of dementia emerge; psychotic or aggressive symptoms particularly increase the burden of stress and direct costs of care for caregivers, and accelerate institutionalization [3 –7]. Moreover, these severe NPSs in patients with AD influence caregiver burden more than other factors (e.g., physical and cognitive impairment in patients and types of the caregiver) [8]. Therefore, these distressful symptoms may often necessitate interventional treatments including the usage of psychotropics [9].

The effectiveness of pharmacological interventions on psychotic or aggressive symptoms has been evaluated in patients with dementia during the last decade [10]. It has been noted that antipsychotics (APs) have beneficial effects on these symptoms in this patient population within 12 weeks [9 –11]. However, their effectiveness has been reported to be limited during a period extending beyond 12 weeks due to their insufficient efficacy or intolerability including increased risk of death [9 –11]. For example, the Clinical Antipsychotic Trials of Intervention Effectiveness–Alzheimer’s Disease (CATIE-AD) Phase 1, a 36-week, double-blind randomized controlled study, was one of the largest studies to investigate the effectiveness of atypical APs (AAPs) on psychotic or aggressive symptoms in 421 patients with AD [12 –14]. They demonstrated that the proportion of treatment improvers and the time until treatment discontinuation were not different between patients taking between AAP and placebo [13]. Moreover, the proportion of discontinuation due to any reason (i.e., lack of efficacy, adverse events, or death) was about 80% [13]. Thus, the recent treatment guideline for NPSs recommended that non-pharmacological interventions should be a priority over pharmacological interventions to improve NPSs (mainly, agitation or depressive symptom) and reduce caregiver burden in patients with AD [15].

Given that cases exist wherein NPSs are more severe than non-pharmacological interventions can manage and treatment with APs may not be avoidable, it is crucial to detect a subgroup of patients that demonstrates response to APs as soon as possible such that unnecessary side effects are prevented. To this end, it would be helpful to elucidate the associations between APs treatment continuation/response and baseline sociodemographic and clinical characteristics in AD patients with severe NPSs in order to identify patients with good response during the premedication stage, leading to better individualized care. A few studies have investigated prognosis such as mortality/serious events in patients with dementia during AP treatment and have found predictors such as older age, male gender, and absence of hallucination [16, 17]. However, no study has examined predictors of AP treatment continuation and response in patients with AD.

Thus, the aim of the present study was to identify baseline predictors of treatment continuation and response during 8 weeks of treatment with AAPs in patients with AD with psychosis or aggressive symptoms. To address this, we analyzed the CATIE-AD Phase 1 dataset, which is ideal given its detailed background information and inclusion of a clinically homogenous AD among clinic/research-based population [12, 13].

METHODS

Participants

Four hundred and twenty-one AD ambulatory outpatients with psychotic symptoms or aggressive behaviors were enrolled in this study. All patients were diagnosed as having dementia of the Alzheimer’s type according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-4) or probable AD on the basis of their history and results of structural brain imaging [12]. Regarding psychiatric disorders, patients with drug abuse, delirium, symptomatic psychiatric disorders, and other dementias were excluded [12, 13]. Patients were also excluded if they were currently benefitting from any psychotropic before the trial. Severity of psychotic symptoms or aggressive behaviors in eligible patients with AD was very serious, such that they caused trouble in everyday life and patients needed antipsychotic treatment as a result. The severity of NPSs was evaluated with the Neuropsychiatric Inventory ([NPI]: scores range from 0 to 120, where a higher score reflects a more severe neuropsychiatric condition) [18, 19]. The severity of psychotic and behavioral symptoms was measured with the Brief Psychiatric Scale ([BPRS]: scores range from 0 to 108, where a higher score reflects a more severe psychotic level) [18, 19]. The NPI and BPRS were administered to informants/caregivers who had lived with, or visited the patients for at least 8 hours per week over 3 days per week [12, 13]. Moreover, the Clinical Global Impression of Change ([CGI-C]: scores range from 1 to 7, where a higher score reflects a worse impression of change by clinicians) was used to evaluate patients’ clinical alteration, as determined by the clinician’s impression [20]. While the CGI-C score was measured at weeks 2, 4, and 8, NPI and BPRS scores were measured at baseline, and weeks 2, 4, and 8. Subjects’ global cognitive function was measured with the Mini-Mental State Examination ([MMSE]; scores range from 0 to 30, where a higher score reflects a higher neurocognitive level). Activities of Daily Living (ADL) were assessed with the Alzheimer’s Disease Cooperative Study ([ADCS]; scores range from 0 to 78, where a higher score reflects a higher ADL level) [21, 22]. General medical health was evaluated using the General Medical Health Rating ([GMHR]; scores range from 1 to 4, where a higher score reflects a healthier state) [23]. Caregivers’ burden or distress was assessed with the Burden Interview (scores range from 0 to 88, where a higher score reflects a more severe caregiver burden) [24].Baseline sociodemographic and clinical data were examined after the screening of eligible subjects. Trial medicine (olanzapine: 2.5 mg or 5.0 mg, quetiapine: 25 mg or 50 mg, risperidone: 0.5 mg or 1.0 mg, or placebo) was randomly assigned to patients in phase 1. Study physicians decided the starting doses and appropriate doses according to patients’ responses.

Sociodemographic and clinical characteristics as predictor variables

As predictive variables, sociodemographic and clinical characteristics were selected based on previous studies investigating predictors or risk factors of NPSs [25 –39]. The potential predictors are listed in Table 1. Age (y) [25 –27], body weight (lb) [28], body-mass index (BMI) [28], duration of education (y) [26, 27], total scores on the MMSE [29, 40], ADCS [29], GMHR [29], and Burden Interview [27], and income (money) received in the past months ($)[30, 31] were defined as continuous variables. Sex [26 , 32], race (Caucasian or non-Caucasian) [26 , 33], present marital status (married or not) [26 , 34], residence (own home or not) [26 , 35], care service (receiving or not) [36], medical history (diabetes mellitus, hypertension, any cardiac disorders [any coronary artery disease, aortic or mitral valve disease, any arrhythmia, cardiac failure congestive, and inflammatory heart disease], and cerebrovascular accidents) [37 –39], intake of medications within 14 days prior to baseline (antihypertensive drugs [ACE-Inhibitors, β-blocking agents, high or low ceiling diuretics, and selective calcium channel blocker], any anticholinesterase, and any psychotropics) [27, 38], and intake of trial medications (olanzapine, quetiapine, risperidone, or placebo)[12, 13] were defined as categorical variables.

Definitions of treatment continuation or response at week 8 as outcomes

In the present study, we investigated predictors of 4 outcomes. Treatment continuation until week 8 was a primary outcome. Treatment continuation integrated judgement from various viewpoints of efficacy and tolerability by patients, caregivers, and clinicians, according to previous CATIE-AD trials [12]. Next, we utilized 3 definitions of treatment response as secondary outcomes: (1) from 1 (very much improved) to 3 (minimally improved) in the CGI-C score, (2) reduction of more than 8 points in the NPI total score from baseline [41], and (3) reduction rate of higher than 25% in the BPRS total score from baseline [19]. The last observation carried forward (LOCF) method was employed to evaluate each response outcome because the discontinuation rate at week 8 was about 50% in the original study (Table 2).

Table 1

Independent variables in simple logistic regression models

Continuous variables	Average±SD (range)
Age, y (n = 421)	77.9±7.5 (51–103)
Body weight, lb (n = 417)	150.2±31.4 (77–302)
Body-Mass Index (n = 410)	25.4±4.6 (14.6–45.2)
Education, y (n = 421)	12.3±3.4 (0–21)
MMSE total score (n = 416)	15±5.8 (4–29)
ADCS total score (n = 413)	39.0±17.2 (2–76)
GMHR score (n = 420)	3.3±0.7 (2–4)
Burden interview total score (n = 409)	34.4±16.0 (0–76)
Received income in past months, dollars (n = 409)	1456.8±1361.5 (0–4900)
Categorical variables	n (%)
Gender, female/male	235 (55.8)/186 (44.2)
Race, Caucasian/non-Caucasian	331 (79)/88 (21)
Present marital status, married/not	249 (59.1)/172 (40.9)
Residence, own home/not	307 (72.9)/114 (27.1)
Care service, taking/not	161 (38.5)/257 (61.5)
Previous medical history (Yes)
Diabetes Mellitus	59 (14)
Hypertension	219 (52)
Cardiac disorders	118 (28)
Cerebrovascular accidents	43 (10.2)
Intake of medication at baseline
Antihypertensive drugs	125 (29.7)
Anticholinesterase	205 (48.7)
Psychotropic	136 (32.3)
The trial’s medicine
Olanzapine	100 (n = 23.8)
Quetiapine	94 (n = 22.3)
Risperidone	85 (n = 20.2)
Placebo	142 (n = 33.7)

ADCS, Alzheimer’s Disease Cooperative Study; BPRS, Brief Psychiatric Rating Scale;CGI-C, Clinical Global Impressions of Change; GMHR, General Medical Health Rating; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory.

Table 2

Number and rate of those who continued treatment by week 8 and/or achieved treatment response at week 8

Outcomes	Those who continued		CGI-C score		NPI score		BPRS score
	treatment by week 8		responders		responders		responders
			(score range: 1–3)		(≥ 8 points reduction		(≥ 25% reduction of
					of total score)		of total score)
	Number	Rate (%)	Number	Rate (%)	Number	Rate (%)	Number	Rate (%)
	205	48.7	163	42.7	186	48.6	145	37.5

BPRS, Brief Psychiatric Rating Scale; CGI-C, Clinical Global Impressions of Change; NPI, Neuropsychiatric Inventory. CGI-C, NPI, and BPRS scale were evaluated by last observation carried forward (LOCF) methods at week 8.

Statistical analysis

As preliminary investigations, single logistic regression analyses were used to explore potential predictors for each treatment outcome (i.e. treatment continuation or response) among the following variables: age, sex, race, body weight, BMI, duration of education, total scores on the MMSE, ADCS, GMHR, and Burden Interview, income (money) received in past months, present marital status, their own residence, care service, medical history (diabetes mellitus, hypertension, any cardiac disorder, and cerebrovascular accidents), intake of medications at baseline (antihypertensive drugs, any anticholinesterase, and any psychotropic), and intake of the trial AAP (olanzapine, quetiapine, risperidone, or placebo). Subsequently, multiple logistic regression models (stepwise forward selection) were performed to verify predictors among the selected variables found to be significant by the single regression analyses [31]. Moreover, age and scores in the MMSE, GMHR, and BPRS at baseline were also added as predictors based on previous reports[31, 42]. If a sample size of one variable is 20 or more, this was considered an appropriate independent variable for the multiple logistic regression model [31]. The Hosmer-Lemeshow test was performed for an evaluation of each multiple logistic regression model adjustment or fitting. A p value <0.05 was considered statistically significant. As for the multivariable regression analysis for the secondary outcomes (3 response outcomes [CGI-C, NPI, and BPRS]), a Bonferroni corrected p value (<0.05/3 = 0.016) was considered statistically significant. IBM SPSS Statistics for Windows, Version 22.0 (Armonk, NY: IBM Corp.) was used for all the statistical analyses.

RESULTS

Patient characteristics

Four hundred and twenty-one patients with AD (235 females [55.8%]; age = 77.9±7.5 y) were included in this analysis. Baseline sociodemographic and clinical characteristics are shown in the Table 1. The rates of treatment continuation and each response outcome during the 8 weeks of treatment are displayed in Table 2. While the treatment continuation rate was 48.7% in eligible patients, the responder ratio, as defined by CGI-C, NPI, or BPRS scores was 42.7%, 48.6%, and 37.5%, respectively, employing the LOCF method.

Potential predictors for treatment continuation or response (preliminary single logistic regression analyses)

Table 3 displays the associations between baseline sociodemographic and clinical characteristics and treatment continuation and response. Treatment continuation was associated with non-Caucasian race and a shorter duration of education. Response defined by the CGI-C was related to non-Caucasian race and lower MMSE scores. Response defined by the NPI was related to non-Caucasian race, history of diabetes mellitus, and treatment with risperidone. Finally, response defined by the BPRS was related to higher GMHR scores and history of cardiacdisorders.

Table 3

Predictors of treatment continuation and response at week 8 using simple regression analyses

	Those who continued			CGI-C score responders			NPI score responders			BPRS score responders
	treatment by week 8			(score range: 1–3)			(≥ 8 points reduction of			(≥ 25% reduction of
							total score)			total score)
Variables	OR	p-value	95% CI	OR	p-value	95% CI	OR	p-value	95% CI	OR	p-value	95% CI
Continuous variables
Age (y)	1.00	0.930	0.98–1.03	1.03	0.061	1.00–1.06	1.01	0.372	0.96–1.04	1.00	0.957	0.97–1.03
Body weight (lb)	1.00	0.344	0.99–1.00	1.00	0.223	0.99–1.00	1.00	0.311	1.00–1.01	1.00	0.892	0.99–1.01
Body-Mass Index	0.98	0.299	0.94–1.02	0.99	0.553	0.94–1.03	1.02	0.28	0.98–1.07	1.00	0.998	0.96–1.05
Education (y)	0.93	0.021 ^*	0.88–0.99	0.96	0.199	0.90–1.02	0.94	0.058	0.89–1.00	0.99	0.674	0.93–1.05
MMSE total score	0.99	0.729	0.96–1.03	0.95	0.009 ^**	0.92–0.99	0.99	0.558	0.96–1.03	1.01	0.785	0.97–1.04
ADCS total score	1.01	0.356	0.99–1.02	1.00	0.393	0.98–1.00	1.00	0.635	0.99–1.01	1.01	0.381	0.99–1.02
GMHR score	1.01	0.923	0.76–1.35	1.20	0.244	0.88–1.63	1.02	0.885	0.76–1.38	1.46	0.019 ^*	1.06–2.01
Burden interview
total score	1.00	0.567	0.98–1.01	1.00	0.849	0.99–1.01	1.01	0.114	1.00–1.02	1.01	0.453	0.99–1.02
Received income in past months ($)	1.00	0.992	1.00–1.00	1.00	0.608	1.00–1.00	1.00	0.983	1.00–1.00	1.00	0.902	1.00–1.00
Categorical variables
Gender (Female)	1.15	0.483	0.78–1.69	1.29	0.224	0.86–1.95	0.85	0.424	0.57–1.27	0.94	0.762	0.62–1.42
Race (Caucasian)	0.53	0.009 ^**	0.33–0.85	0.57	0.026 ^*	0.35–0.93	0.57	0.025 ^*	0.35–0.93	0.78	0.329	0.48–1.28
Married in the present	0.75	0.151	0.51–1.11	0.72	0.118	0.48–1.09	1.03	0.874	0.69–1.56	0.98	0.927	0.65–1.49
Residence (own home)	0.93	0.744	0.61–1.43	0.82	0.400	0.52–1.30	1.30	0.259	0.83–2.03	1.06	0.798	0.67–1.69
Care service (taking)	0.84	0.400	0.57–1.25	0.78	0.251	0.52–1.19	0.79	0.261	0.52–1.19	1.03	0.885	0.68–1.57
Previous medical history (yes)
Diabetes Mellitus	1.11	0.721	0.64–1.92	1.30	0.381	0.73–2.31	1.91	0.034 ^*	1.05–3.46	0.81	0.499	0.44–1.49
Hypertension	1.38	0.103	0.94–2.02	1.14	0.523	0.76–1.71	1.25	0.273	0.84–1.87	0.94	0.783	0.63–1.43
Cardiac disorders	0.98	0.921	0.64–1.50	1.08	0.73	0.69–1.71	1.49	0.086	0.95–2.34	1.78	0.014 ^*	1.12–2.81
CVA	1.01	0.984	0.54–1.89	0.86	0.675	0.44–1.71	1.27	0.487	0.65–2.46	1.49	0.240	0.77–2.90
Previous or present medicines (taking)
Antihypertensive drugs	0.8	0.299	0.53–1.22	0.86	0.489	0.55–1.33	1.09	0.714	0.70–1.68	0.93	0.737	0.59–1.45
Anticholinesterase	1.17	0.415	0.80–1.72	0.96	0.824	0.64–1.43	1.10	0.651	0.74–1.64	1.05	0.823	0.69–1.58
Any psychotropic	0.76	0.195	0.51–1.15	1.11	0.623	0.72–1.71	1.06	0.778	0.69–1.63	0.92	0.723	0.60–1.43
The trial’s medicine (taking)
Olanzapine	1.19	0.449	0.76–1.86	1.19	0.469	0.75–1.89	0.69	0.119	0.43–1.10	0.79	0.335	0.46–1.28
Quetiapine	0.73	0.177	0.46–1.16	1.07	0.799	0.65–1.75	1.52	0.096	0.93–2.49	1.56	0.079	0.95–2.54
Risperidone	1.04	0.882	0.64–1.67	1.36	0.227	0.83–2.24	1.76	0.029 ^*	1.06–2.92	1.26	0.376	0.76–2.08
Placebo	1.08	0.702	0.72–1.62	0.67	0.054	0.42–1.01	0.67	0.062	0.43–1.02	0.73	0.159	0.47–1.13

ADCS, Alzheimer’s Disease Cooperative Study; BPRS, Brief Psychiatric Rating Scale; CGI-C, Clinical Global Impressions of Change; CVA, cerebrovascular accidents; GMHR, General Medical Health Rating; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory; OR, odds ratio; CI, confidence interval; ^*p < 0.05; ^**p < 0.01. Values in bold font are significant results at p < 0.05.

Predictors for treatment continuation or response in patients (multiple logistic regression analyses)

Since race type was shown to be significantly associated with treatment continuation and response (CGI-C and NPI) by single logistic analyses (Table 3), the race type was stratified into Caucasian (n = 331) or non-Caucasian race (n = 88) to avoid interaction between racial type and other sociodemographic factors (years of education or history of diabetes mellitus) in the next step. However, in the present study, the sample size of non-Caucasian patients was comparatively small, which was not appropriate to conduct a multiple logistic regression analysis, because at least 100 sample was necessary. Therefore, only Caucasian sample was analyzed in the second step.

Predictors for treatment continuation or response in the Caucasian patients (multiple logistic regression analyses)

Table 4 depicts the associations between sociodemographic and clinical characteristics and treatment continuation and response in the Caucasian patients. All models’ adjustments were not rejected (p > 0.05 for each outcome by Hosmer-Lemeshow test).

Table 4

Significant predictors of Caucasian AD patients (n = 331) at week 8 using multiple regression analyses (stepwise)

	Those who continued			CGI-C score responders			NPI score responders			BPRS score responders
	treatment by week 8			(score range: 1–3)			(Over 8 points reduction)			(Over 25% reduction of
										total score)
Variables	OR	p-value	95% CI	OR	p-value	95% CI	OR	p-value	95% CI	OR	p-value	95% CI
Continuous variables
Age, y	–	–	–	1.04	0.22^*	1.01–1.08	–	–	–	–	–	–
Education, y	–	–	–
MMSE total score	–	–	–	0.95	0.009 ^**,¶	0.91–0.99	–	–	–	–	–	–
GMHR score	–	–	–	–	–	–	1.49	0.035^*	1.03–2.14	1.77	0.006^**,^¶	1.18–2.64
BPRS baseline score	–	–	–	–	–	–	1.02	0.022^*	1.00–1.05	1.03	0.016^*,^¶	1.01–1.05
Categorical variables
Diabetes mellitus (n = 35)							3.51	0.003^**,¶	1.52–8.09
Cardiac disorders (n = 94)										1.98	0.021^*	1.11–3.52
Risperidone (n = 68)							2.56	0.002^**,¶	1.41–4.64

BPRS, Brief Psychiatric Rating Scale; CGI-C, Clinical Global Impressions of Change; GMHR, General Medical Health Rating; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory; OR, odds ratio; CI, confidence interval. ^*p < 0.05, ^**p < 0.01, ^¶p < 0.0166≒0.05/3 (Bonferroni correction in CGI-C, NPI or BPRS responders). Values in bold font are significant results at each Bonferroni correction. Age and scores in the MMSE, GMHR, and BPRS at baseline were added to selected variables in each conceptual regression model.

Treatment continuation in the Caucasian patients

No significant predictor was identified for treatment continuation (Table 4).

Treatment response in the Caucasian patients

Logistic regression analyses were performed to determine the potential predictors of treatment response, as defined by CGI-C, NPI, and BPRS scores (Table 4). The model for CGI-C response was statistically significant (χ ² = 12.0, p = 0.002) with a correct classification of 63.4% of cases. A lower MMSE score significantly predicted CGI-C response after Bonferroni correction. Next, the model for NPI response was statistically significant (χ² = 24.7, p < 0.001) with a correct classification of 63.2% of cases. A history of diabetes mellitus and treatment with risperidone significantly predicted NPI response after Bonferroni correction. Finally, the model for BPRS response was statistically significant (χ ² = 15.8, p = 0.001) with a correct classification of 64.7% of cases. Higher GMHR score and BPRS score at baseline significantly predicted BPRS response after Bonferroni correction.

DISCUSSION

The present study was the first to systematically investigate which baseline sociodemographic and clinical characteristics predict treatment continuation and response at week 8 after initiation of AAP treatment in patients with AD who needed interventional treatment for their psychotic symptoms or agitation. We found that: (1) dropouts were about 50% irrespective of the medication, and irrespective of whether they got medication or placebo in the Caucasian patients with AD; (2) a lower MMSE score at baseline was associated with treatment response based on CGI-C; (3) risperidone was better than olanzapine or quetiapine as per the NPI; (4) diabetes mellitus was associated with better response as per the NPI; (5) better treatment response as per the BPRS was influenced by a healthier condition (higher GMHR score) at baseline; (6) and better treatment response as per the BPRS was influenced by a greater severity of its initial psychotic and behavioral symptoms (higher BPRS score at baseline).

With regard to the treatment continuation of AAPs, no sociodemographic factors were associated in the Caucasian patients with AD in the conceptual regression model while racial type and education levels were significantly related to the treatment continuation in the preliminary single logistic regression analyses. Thus, we speculate that the weak relationship between education levels and race, or any racially specific biological factors associated with pharmacokinetics or pharmacodynamics, might have influenced the treatment continuation of AAPs. On the other hand, this study did not find a relationship between treatment continuation and older age, male gender [16], which was previously reported to be linked to mortality/serious events during AAP treatment in demented patients.

In terms of treatment response to AAPs in the Caucasian patients with AD, a lower level of global cognition at baseline was associated with clinical global response based on the clinician’s impression. In contrast, another study reported that a higher level of cognition at baseline predicted treatment response to 9-week treatment with citalopram [43]; of note, citalopram’s efficacy and tolerance for delusions, anxiety, and irritability has been shown in patients with AD by previous studies [43 –45]. While findings seem incongruent, this inconsistency may be attributable to the differences in mechanisms of action between AAPs and antidepressants or to the differences in patient characteristics among studies. Moreover, our previous study reported that psychotic symptoms were associated with a lower level of global cognition in the same CATIE-AD sample [27]. Furthermore, another CATIE-AD study noted that AAPs were associated with worsening of cognitive function in patients with AD [46]. Thus, those with lower levels of global cognition at baseline may show a faster progression of AP-induced cognitive impairment, which may in turn contribute to an apparent resolution of NPSs based on physicians’ impression.

Risperidone was more effective for NPSs than the other AAPs or placebo based on caregiver’s assessment by week 12. The same result was reported in a previous reanalysis CATIE-AD study with the LOCF methods [14]. Furthermore, a previous meta-analysis has shown superiority of risperidone or aripiprazole in efficacy against psychosis or agitation in patients with dementia compared with other AAPs with a small effect size [9, 47]. Thus, this finding of superiority of risperidone for treatment of NPSs by week 8 is consistent among the different studies. However, in the 36-week original CATIE-AD study, the rate of treatment improvers and the time until treatment discontinuation were not different between those who took each AAP and those who took placebo [13]. Therefore, an association between early response of pharmacological treatments and subsequent long-term response should further be investigated [48]. As for history of diabetes mellitus, it has been reported that poor glycemic control is a predictor for accelerating cognitive decline [39], which suggests that those with a history of diabetes at baseline may show a faster progression of AP-induced cognitive impairment. This may in turn contribute to an apparent resolution of psychotic and behavioral problems in patients with AD. Further research is necessitated to examine the relationship between history of diabetes and treatment response to AAPs in this patient population.

On the other hand, healthier status at baseline predicted treatment response in psychotic and behavioral symptoms in patients with AD. This finding is in line with the notion that healthier status is one of the protective factors against NPSs in this patient population [27 , 37].

Moreover, the association between more severe initial psychotic problems and later response outcome seems to be paradoxical; however, this is consistent with the findings that a greater degree of baseline positive symptoms is one of the predictors of remission in patients with schizophrenia with antipsychotic treatment [42]. Further research is necessitated to examine the relationship between baseline severity of NPSs and treatment response to AAPs in patientswith AD.

The present study has several limitations. First, in the multiple regression analyses, the stratification of Caucasian or non-Caucasian was conducted in an effort to keep racial homogeneity and to avoid interactions between racial type and other sociodemographic factors. As a result, we could investigate treatment continuation and response in the only Caucasian AD patients because the sample size of non-Caucasian was small. Second, the sample size of the entire group was comparatively small, which may cause type II errors. Third, in the present study, the details of the cardiac disorders were not classified and comparisons between each subtype (i.e. any coronary artery disease, aortic or mitral valve disease, any arrhythmia, cardiac failure congestive, and inflammatory heart disease) could not be performed given that the sample size of each cardiac disease subtype was less than 20, thus rendering it inappropriate to include as a variable in regression analyses [31]. Fourth, BPRS and CGI-C were rated by clinicians while NPI was administered by informants. Thus, all of the rating scales may not capture symptoms or symptom changes equally in the same subject [49]. Finally, the CATIE-AD trial enrolled subjects with psychotic symptoms or aggressive/agitation behaviors that needed interventional treatments, which was so called ‘clinical- or research-based’ sample. Importantly, patients were also capable of visiting a hospital to receive treatment. Thus, the results of the present study might not be generalizable to all patients with AD, since the sample did not include community-based subjects with mild NPSs or more severe subjects who reside in seniors’ facilities.

In conclusion, by exploring the relationships between baseline sociodemographic and clinical characteristics and AAP treatment continuation and response in an 8-week clinical trials including Caucasian AD patients with psychosis or aggressiveness symptoms, the present study found baseline predictors for AAP treatment response were: (1) treatment with risperidone (versus olanzapine and quetiapine), (2) history of diabetes mellitus, (3) healthier physical status, (4) lower cognitive levels, and (5) severer initial psychotic or behavioral symptoms. Further replication studies are necessitated to confirm these results. Although we selected candidate predictors from sociodemographic and clinical characteristics that were potentially related to predictors or risk factors of NPSs, most of them did not predict AP continuation or response in Caucasian patients with AD. Considering the remarkable dropout rate of AAP treatment for NPSs in patients with AD, clinicians are advised to determine treatment strategies or long-term detailed care plans, including non-pharmacological interventions for psychosis or aggressiveness symptoms, while further evaluating sociodemographic and clinical backgrounds in this patient population [50 –52].

DISCLOSURE STATEMENT

Authors’ disclosures available online (http://j-alz.com/manuscript-disclosures/17-0412r2).

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