The introduction of measurement-based care for patients with schizophrenia may improve psychiatric symptoms and guideline adherence rate

Abstract

Introduction

Measurement-based care (MBC) is a clinical approach that uses objective measurements to quantitatively assess patients’ symptoms and conditions as the basis for treatment, with the assessments shared with patients and used in practice. One indicator of MBC, the guideline adherence rate (GAR), provides a comprehensive evaluation of the extent to which psychiatrists’ prescribing practices conform to clinical practice guidelines. Although the practice of MBC has been reported to improve clinical outcomes in depressive disorders, improvements in clinical outcomes with MBC have not been reported in schizophrenia. We examined longitudinal changes in psychiatric symptoms and the GAR among patients with schizophrenia receiving MBC.

Methods

Sixty-five patients with schizophrenia were included. The Positive and Negative Syndrome Scale (PANSS) total score and the GAR were compared longitudinally between time point 1 (T1) and time point 2 (T2).

Results

The PANSS total score was significantly lower at T2 than at T1. The GAR was significantly higher at T2 than at T1.

Conclusion

Improvements in psychiatric symptoms were consistent with previous findings on MBC reported in depressive disorders. In addition, improvements in the GAR indicated that psychiatrists’ prescribing practices aligned more closely with the guideline over time within an MBC environment. These findings suggest that the MBC framework may also be clinically useful in the treatment of schizophrenia. The results were obtained from a specialized schizophrenia outpatient clinic in a national center, which represents a best-case environment for guideline adherence and monitoring. Therefore, further studies are needed to examine the generalizability of these findings.

Graphical abstract

Graphical Abstract.

Significance statement.

Measurement-based care (MBC) is a clinical approach in which symptoms and treatment response are quantitatively assessed, and the assessments are shared with patients to guide clinical practice. While improvements in clinical outcomes with MBC have been demonstrated in depressive disorders, improvements in clinical outcomes associated with MBC practice have not been reported in schizophrenia. We observed improvements in psychiatric symptoms along with increases in the guideline adherence rate (GAR), an indicator reflecting the extent to which pharmacotherapy aligns with clinical practice guidelines, in this longitudinal study of 65 patients with schizophrenia receiving MBC. Our findings suggest that MBC may also have clinical utility in the treatment of schizophrenia. However, these findings come from a highly specific, resource-rich setting—a specialized schizophrenia outpatient clinic at a national center representing a best-case environment for guideline adherence and monitoring—which may limit their external validity.

Introduction

An evidence-practice gap exists in clinical settings due to the insufficient implementation of guideline-recommended treatments.^1–3 This gap also extends to the treatment of patients with schizophrenia.^4–9 The “Guideline for Pharmacological Treatment of Schizophrenia 2022”¹⁰ in Japan aims to promote patient recovery. The guideline summarizes extensive evidence on the efficacy and safety of pharmacological treatments, including improvements in psychiatric symptoms, cognitive impairment, and antipsychotic adverse effects. Worldwide, efforts are ongoing to encourage psychiatrists to adhere to clinical guidelines.^11–13 In Japan, the “Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE)” project was initiated to improve psychiatrists’ adherence to guidelines.^14–17 Several reports have indicated that education about clinical guidelines via the EGUIDE project has been effective in terms of improving treatment-related behaviors among psychiatrists.^18–21

The EGUIDE project developed an indicator called the “guideline adherence rate (GAR)” to measure the evidence–practice gap.^22,23 The GAR is a comprehensive evaluation of the extent to which psychiatrists’ treatment adheres to the “Guideline for Pharmacological Treatment of Schizophrenia 2022”.¹⁰ The GAR ranges from 0% to 100%. The greater the degree of adherence to the guideline is, the greater the GAR. For example, if monotherapy with second-generation antipsychotics (SGAs) is used, as recommended in the guideline, the GAR is 100%. Previous studies have reported that a higher GAR is associated with milder psychiatric symptoms, longer working hours, better memory function, and longer sleep duration in patients with schizophrenia, all of which are indicators of the relationship between psychiatrists’ adherence to the guideline and patient outcomes.^24–27 Although the volumes of the putamen and pallidum are known to be larger in patients with schizophrenia than in healthy controls, higher GAR has been reported to be associated with smaller volumes of the putamen and pallidum in treatment-resistant schizophrenia (TRS).²⁸ This GAR quantifies adherence to guideline-recommended pharmacotherapy and may serve as a decision aid in shared decision making (SDM).

Shared decision making is defined as a process in which clinicians and patients work together to select treatments by integrating the best available evidence with patients’ values and preferences.²⁹ Measurement-based care (MBC) is an approach in which patients’ symptoms and clinical status are quantitatively assessed using objective measures, and the assessments are used in routine practice as a basis for treatment decisions.³⁰ Within MBC, assessments are assumed to be shared between clinicians and patients and used to guide treatment decisions and adjustments, indicating that MBC functions as a framework for operationalizing SDM in routine clinical practice.³¹ Indeed, several studies have reported that the practice of MBC, in which psychiatrists and patients with depressive disorders jointly use quantitative assessments during decision-making of treatment, is associated with more frequent treatment adjustments and improved clinical outcomes.³² Although MBC has also attracted increasing attention as a promising framework for schizophrenia care, improvements in clinical outcomes and treatment processes among patients with schizophrenia have not yet been reported in real-world clinical settings where MBC is implemented. Time constraints and additional work burden are often cited as barriers to MBC implementation because comprehensive, multidomain assessments that extend beyond symptom severity to include adverse effects, social functioning, and cognitive function are required.³³ Nevertheless, SDM based on comprehensive MBC, including feedback on the GAR, is routinely implemented in clinical practice at the specialized schizophrenia outpatient clinic of the National Center of Neurology and Psychiatry. In this context, we examined longitudinal changes in psychiatric symptoms and the GAR among patients with schizophrenia receiving MBC.

Methods

Patients with schizophrenia (n = 146) were enrolled from both outpatient and inpatient units of the Department of Psychiatry, National Center of Neurology and Psychiatry. All patients were evaluated in the specialized outpatient clinic for schizophrenia at the National Center Hospital, National Center of Neurology and Psychiatry. Patients were diagnosed based on the criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). For the primary longitudinal analyses, we included patients who had undergone 2 assessments. In the cross-sectional analyses, patients who were assessed only once were also included. Following the methodology of a previous study,^34,35 we standardized several medication dosages to equivalent units. Specifically, daily total antipsychotic dosages were expressed as chlorpromazine equivalents (CPZ-eq), antiparkinsonian drugs as biperiden equivalents (biperiden-eq), antidepressants as imipramine equivalents (imipramine-eq), and anxiolytics and hypnotics as diazepam equivalents (diazepam-eq). The authors declare that all procedures associated with this study were conducted in accordance with the ethical standards of the relevant national and institutional human experimentation committees and with the 1975 Declaration of Helsinki (revised 2008). This study was approved by the Research Ethics Committee of the National Center of Neurology and Psychiatry (approval number: B2023-080). Written informed consent was obtained from all participants.

Guideline adherence rate

To assess psychiatrists’ adherence to the guideline for the pharmacological treatment of schizophrenia, we reviewed the prescribed medications and calculated the GAR (Supplementary Tables 1 and 2).²³ The GAR is 100% when patients with schizophrenia are treated with SGA monotherapy, which is the recommended treatment, 80% when 2 antipsychotics are used in combination, 60% when 3 or more antipsychotics are used, and 10% when no antipsychotics are prescribed. Additional conditions are calculated as follows: -10% for the prescription of first-generation antipsychotics (FGAs), -10% for the prescription of anticholinergics, -20% for each additional prescription of psychotropics other than antipsychotics and anticholinergics, and -15% for the prescription of pro re nata (PRN) psychotropics. For example, in a patient with schizophrenia prescribed risperidone, levomepromazine, biperiden, and brotizolam, the GAR is calculated as follows: starting from 80% for concomitant use of 2 antipsychotics (risperidone and levomepromazine), -10% is applied for the prescription of an FGA (levomepromazine), -10% for the prescription of an anticholinergic (biperiden), and -20% for the prescription of a psychotropic other than antipsychotics and anticholinergics (brotizolam), resulting in a final GAR of 40%. The guideline treatment recommendations and GARs differ depending on whether the patient has TRS. The Japanese definition of TRS was used as the diagnostic criterion.³⁶ When clozapine monotherapy is administered to TRS patients, the GAR is 100%; when clozapine is not prescribed, the rate is calculated to be -40%. The other calculation conditions are in accordance with the above calculation method. If the calculation result is 0% or less, the GAR is considered to be 0%.

Measurement-based care

Since September 2019, the specialized outpatient clinic for schizophrenia at the National Center Hospital, National Center of Neurology and Psychiatry, has been offering MBC to patients with schizophrenia on an ongoing basis. The specific assessment items of MBC in the outpatient setting as of 2025 are shown in Supplementary Table 3. We conduct inspections and provide feedback on these items every 6 months to a year. For each patient, the diagnosis is explained, including whether the patient meets the criteria for TRS. All assessment results and the calculated GAR are shared to support decision-making regarding guideline-recommended pharmacotherapy. For the GAR, feedback is given to patients on their prescriptions by giving them a results form that explains the reason for the point reduction.

Outcome measures

Psychotic symptoms of schizophrenia were assessed by psychiatrists and clinical psychologists using the Positive and Negative Syndrome Scale (PANSS).³⁷ Drug-induced extrapyramidal symptoms (EPSs) were assessed by psychiatrists and clinical psychologists using the Drug-Induced Extrapyramidal Symptoms Scale (DIEPSS).^38,39 The DIEPSS is used to evaluate the severity of drug-induced EPSs that occur during treatment with antipsychotic medications. This scale consists of 8 individual items (assessing gait, bradykinesia, drooling, muscle rigidity, tremor, akathisia, dystonia, and dyskinesia) and one summary item (assessing overall severity), and each item is rated on a five-point scale (0-4). Work hours were assessed by psychiatrists and clinical psychologists using the Social Activity Assessment. Patients were interviewed about their working conditions during the previous 12 weeks, and a 12-week average of hours worked per week was calculated.^40,41 Positive and Negative Syndrome Scale, DIEPSS, and work hours were rated by psychiatrists/clinical psychologists who were aware of the prescriptions used to calculate the GAR. Assessments were not blinded, and inter-rater reliability was not evaluated. In principle, the same rater assessed both time points.

Statistical analysis

All the statistical analyses were performed using IBM SPSS Statistics 29.0 software (IBM, Armonk, NY, United States). The Kolmogorov–Smirnov test rejected the hypothesis that the GAR, the PANSS total score, the DIEPSS total score (8 individual rating items), the DIEPSS overall severity score, and work hours followed a normal distribution; therefore, nonparametric tests were performed. Among the participants who underwent a second evaluation, the GAR, PANSS total score, DIEPSS total score, DIEPSS overall severity score, and work hours at time point 1 (T1) and time point 2 (T2) were compared using Wilcoxon’s signed-rank test with correspondence. The mean interval between T1 and T2 was 267.7 days (SD = 91.8). In addition, patients who underwent only a single assessment were included, and cross-sectional associations at T1 between the GAR and other outcome measures were examined using Spearman’s rank correlation coefficients. To further examine whether the associations observed in the cross-sectional analyses were influenced by potential confounding factors, multivariable analyses were conducted using multiple linear regression models, with each outcome entered as a dependent variable. In these models, the GAR was the primary independent variable, and age, sex, total CPZ-eq, and FGA use were covariates. In models in which the PANSS total score was not the outcome, the PANSS total score was additionally included as a covariate. Similarly, in models in which the DIEPSS total score or the DIEPSS overall severity score was not the outcome, the DIEPSS total score was included as an additional covariate. The threshold for statistical significance was P < .05. A Bonferroni correction was applied for multiple comparisons.

Results

A total of 65 patients were included in the longitudinal analyses. Their demographic and clinical characteristics are presented in Table 1. At T1, the mean age was 34.1 (SD = 12.1) years, 46.2% of the patients were male, 9.2% met criteria for TRS, 4.6% were inpatients, none were receiving clozapine, and the mean duration of illness was 12.3 (SD = 11.5) years. The total CPZ-eq and the CPZ-eq of the FGAs did not significantly differ between T1 and T2 (z = 1.8, P = 7.8 × 10^-2 and z = -1.8, P = 7.5 × 10^-2). Although the CPZ-eq of SGAs tended to be higher at T2 (z = 2.1, P = 3.5 × 10^-2), the difference was not statistically significant after correcting for multiple comparisons. The biperiden-eq tended to be lower at T2 (z = -2.5, P = 1.3 × 10^-2), but this difference was also not statistically significant after correcting for multiple comparisons. Diazepam-eq was significantly lower at T2 (z = -3.7, P = 2.4 × 10^-4).

Table 1.

Demographic characteristics, guideline adherence rate (GAR), and outcomes in patients with schizophrenia at Time point 1 (T1) and Time point 2 (T2).

	T1		T2		z	P
	Mean (SD) or n (%)	Median [range]	Mean (SD) or n (%)	Median [range]
Age (years)	34.1 (12.1)	33.0 [18-64]	34.9 (12.3)	34.0 [18-65]
Sex (male)	30 (46.2)
TRS	6 (9.2)		9 (13.8)
Inpatient	3 (4.6)		0 (0)
Clozapine use	0 (0)		0 (0)
Education (years)	13.1 (2.4)	12.0 [9-21]	13.2 (2.4)	12.0 [9-21]
Age at onset (years)	21.8 (8.6)	20.0 [6-54]
Duration of illness (years)	12.3 (11.5)	8.0 [0.5-46]	13.1 (11.6)	9.0 [0.5-47]
Total CPZ-eq (mg/day)	416.4 (393.1)	300.0 [0-1800]	481.5 (351.8)	400.0 [0-1600]	1.8	7.8 × 10-2
CPZ-eq of FGAs (mg/day)	15.0 (77.4)	0 [0-600]	3.6 (20.9)	0 [0-160]	−1.8	7.5 × 10-2
CPZ-eq of SGAs (mg/day)	401.3 (379.6)	300.0 [0-1800]	477.9 (354.8)	400.0 [0-1600]	2.1	3.5 × 10-2
Biperiden-eq (mg/day)	0.69 (1.4)	0 [0-6]	0.32 (0.91)	0 [0-5]	−2.5	1.3 × 10-2
Imipramine-eq (mg/day)	15.8 (46.2)	0 [0-250]	14.1 (52.6)	0 [0-250]	−0.95	0.34
Diazepam-eq (mg/day)	7.3 (13.8)	0 [0-90]	2.7 (6.7)	0 [0-30]	−3.7	2.4 × 10-4a
GAR	48.5 (37.9)	60.0 [0-100]	74.5 (32.3)	90.0 [0-100]	4.9	1.1 × 10-6a
PANSS total score	86.7 (13.2)	86.0 [61-125]	79.6 (15.2)	80.0 [49-109]	−4.5	6.7 × 10-6a
DIEPSS total score	1.8 (2.4)	1.0 [0-15]	1.6 (2.1)	1.0 [0-12]	−0.98	0.33
DIEPSS overall severity	1.0 (0.89)	1.0 [0-4]	0.88 (0.93)	1.0 [0-4]	−1.3	0.20
Work hours (hour per week)	11.4 (14.2)	6.0 [0-60]	14.1 (17.5)	9.0 [0-104]	1.0	0.31

Data are presented as mean (SD) or n (%), as appropriate; sex, TRS status, inpatient status, and clozapine use are presented as n (%). Abbreviations: Biperiden-eq, Biperiden equivalent; CPZ-eq, chlorpromazine equivalent; Diazepam-eq, Diazepam equivalent; DIEPSS, Drug-Induced Extrapyramidal Symptom Scale; FGAs, first-generation antipsychotics; GAR, guideline adherence rate; Imipramine-eq, Imipramine equivalent; PANSS, Positive and Negative Syndrome Scale; SGAs, second-generation antipsychotics; TRS, treatment-resistant schizophrenia. z values obtained via the Wilcoxon signed-rank test for paired samples. Bonferroni-corrected significance threshold: ^aP < 4.5 × 10^-3 (0.05/11 comparisons). N = 65 for all variables except DIEPSS total score and DIEPSS overall severity, for which N = 57.

The results of the longitudinal analyses are shown in Table 1. The GAR was significantly greater at T2 (z = 4.9, P = 1.1 × 10^-6). The PANSS total score was significantly lower at T2 (z = -4.5, P = 6.7 × 10^-6). Work hours did not significantly differ between T1 and T2 (z = 1.0, P = .31). Because the DIEPSS total score and DIEPSS overall severity score assess drug-induced EPSs by antipsychotics, 8 patients not receiving antipsychotics were excluded, and analyses were conducted in the remaining 57 patients. The DIEPSS total score and DIEPSS overall severity score did not significantly differ between T1 and T2 (z = -0.98, P = .33 and z = -1.3, P = .20).

A total of 146 patients were included in the cross-sectional analyses. The mean age was 36.3 (SD = 13.6) years, 44.5% were male, 10.3% met criteria for TRS, 6.8% were inpatients, 1.4% were receiving clozapine, and the mean duration of illness was 13.4 (SD = 11.5) years. These demographic and clinical characteristics were similar to those of the longitudinal sample (Supplementary Table 4). Of these, 23 patients were not receiving antipsychotics; therefore, DIEPSS assessments were performed for the remaining 123 patients receiving antipsychotics. The demographic profiles of these 123 patients were also similar to those of the 146 patients, although potential selection bias should be considered (Supplementary Table 5).

The results of the cross-sectional analyses between GAR and the outcomes are shown in Supplementary Table 6. The GAR was significantly and negatively correlated with the PANSS total score (rho = -0.27, P = 8.3 × 10^-4). Furthermore, the GAR was significantly and negatively correlated with both the DIEPSS total score and the DIEPSS overall severity score (rho = -0.30, P = 9.0 × 10^-4 and rho = -0.28, P = 2.1 × 10^-3). However, in multivariable linear regression analyses adjusting for clinically relevant factors, no independent association was found between the GAR and each outcome.

Discussion

This study is the first to demonstrate longitudinal improvements in both psychiatric symptoms and the GAR in a real-world clinical setting where MBC is used as a framework for the treatment of schizophrenia. Longitudinal improvements in clinical outcomes among patients managed with MBC have been reported primarily in depressive disorders. A systematic review and meta-analysis of randomized controlled trials has shown a decrease in depression severity and an increase in remission rates.³² More recently, a longitudinal study of patients with early psychosis receiving MBC has also reported improvements over time in psychiatric symptoms and global functioning.⁴² The results of this study support the broader clinical significance of MBC across diagnostic categories.

Measurement-based care is a clinical framework in which symptoms and treatment responses are quantitatively assessed, the assessments are shared with patients, and the findings are incorporated into clinical practice to improve treatment.³¹ Specifically, quantitative assessments and feedback allow the treatment status to be shared between clinicians and patients and facilitate SDM, which in turn promotes medication adjustments and may ultimately lead to improvements in clinical outcomes (Figure 1). However, the evidence accumulated to date does not encompass all components of this process. A longitudinal study in early psychosis reported that MBC may improve treatment processes, particularly SDM.⁴² In the present study, conducted in a real-world schizophrenia care setting where SDM based on comprehensive MBC is routinely practiced, we observed improvements in the GAR, an indicator reflecting the extent to which pharmacotherapy aligns with the clinical practice guideline. In this context, our findings complement the existing literature by empirically addressing the evaluation of actual treatment following SDM, a component that has not previously been examined in this manner (Figure 1). Furthermore, the GAR is a simple indicator that can be calculated using routinely available prescription data and does not depend on specialized facilities or advanced research infrastructure. Accordingly, this study proposes a practical model for MBC implementation that may be applicable not only in a national center but also in general psychiatric practice settings.

Figure 1.

Conceptual overview of the measurement-based care (MBC) process in prior studies and this study. This figure conceptually summarizes the clinical process underlying MBC and illustrates how the components of this process have been evaluated in prior studies and in the present study. The central framework depicts the core structure of MBC. Measurement-based care is assumed to involve (1) quantitative assessments and feedback, in which symptoms and clinical status are quantified using standardized instruments and the assessments are shared with patients; (2) shared decision making (SDM), a collaborative process in which treatment decisions are made by integrating the best available evidence with patient values; and (3) medication adjustments, including review and modification of prescribed medications. Through these components, MBC is expected to improve clinical outcomes. The central black arrows represent the flow from MBC components to improved clinical outcomes. The surrounding-colored blocks represent studies examining MBC, arranged by target disorder and study design. Arrows extending from each colored block indicate which elements of the MBC process or clinical outcomes were shown to improve in each study. In a systematic review and meta-analysis of randomized controlled trials in depressive disorders (prior studies; blue), reductions in depression severity, increases in remission rates, and improvements in medication adherence were associated with MBC.³²In a longitudinal study of early psychosis (prior study; green), improvements in psychiatric symptoms and global functioning under MBC were observed, along with improvements in measures assessing SDM.⁴²In a longitudinal study of schizophrenia (this study; orange) conducted in a real-world clinical setting with MBC, reductions in psychiatric symptoms assessed by the Positive and Negative Syndrome Scale (PANSS) and improvements in the guideline adherence rate (GAR) were observed. Abbreviations: MBC, measurement-based care; SDM, shared decision making; GAR, guideline adherence rate; PANSS, Positive and Negative Syndrome Scale.

In exploratory cross-sectional analyses, no independent associations were identified between the GAR and the DIEPSS after multivariable adjustment for clinically relevant factors. By design, the GAR algorithm penalties exactly those prescription elements that are closely tied to EPS: FGAs (-10%), anticholinergics (-10%), polypharmacy and additional psychotropics (-20% each), and PRN psychotropics (-15%). Patients with more EPS are more likely to receive FGAs historically and to be prescribed anticholinergics and complex regimens; these prescriptions directly lower GAR. Thus, the negative correlation between GAR and DIEPSS is at least partly tautological and may reflect scoring rules rather than an independent relationship between “guideline adherence” and EPS.

This study has several limitations. First, all patients were drawn from a specialized schizophrenia outpatient clinic in a national center that routinely implements MBC, guideline feedback (GAR reports), and SDM. This is a best-case environment for guideline adherence and monitoring; clinicians and patients elsewhere may behave very differently. These findings come from a highly specific, resource-rich setting, limiting external validity. Second, psychiatrists or clinical psychologists assessed PANSS, DIEPSS, and work hours with knowledge of the prescriptions used to calculate the GAR. Raters were not blinded, and inter-rater reliability was not evaluated. Typically, both time points were assessed by the same rater. Therefore, there is a possibility of rater expectation bias (eg, perceiving fewer EPS when prescriptions look “cleaner”). Third, the observed improvements in outcomes cannot be directly attributed to MBC itself rather than to the passage of time or other factors, because this study did not include a control group that did not receive MBC. Future prospective studies comparing groups with and without MBC are warranted.

Taken together, our findings suggest that the MBC framework may also be useful in the treatment of schizophrenia, at least in specialized clinical settings where comprehensive MBC is routinely implemented. It remains unclear whether all components of comprehensive MBC are necessary or feasible to implement in general psychiatric practice. For broader implementation of MBC in routine clinical settings, an important next step will be to clarify which treatment processes or assessment measures within comprehensive MBC are essential for improving clinical outcomes. In this context, the GAR, which can be calculated relatively easily from prescription information, may represent one of the possible core components of MBC.

Author contributions

Keisuke Mori (Conceptualization [equal], Data curation [lead], Formal analysis [lead], Methodology [equal], Project administration [equal], Validation [lead], Visualization [lead], Writing—original draft [lead], Writing—review & editing [equal]), Junya Matsumoto (Conceptualization [equal], Data curation [lead], Funding acquisition [equal], Investigation [lead], Methodology [equal], Project administration [lead], Resources [lead], Supervision [equal], Validation [equal], Visualization [lead], Writing—original draft [equal], Writing—review & editing [lead]), Satsuki Ito (Data curation [supporting], Investigation [lead], Resources [lead], Validation [equal], Writing—review & editing [equal]), Kentaro Fukumoto (Conceptualization [supporting], Methodology [supporting], Writing—review & editing [equal]), Ken Inada (Conceptualization [supporting], Methodology [supporting], Writing—review & editing [equal]), Fumitoshi Kodaka (Conceptualization [supporting], Methodology [supporting], Writing—review & editing [equal]), Harumasa Takano (Data curation [supporting], Investigation [supporting], Resources [equal], Writing—review & editing [equal]), Shinsuke Kito (Conceptualization [supporting], Supervision [supporting], Writing—review & editing [equal]), and Ryota Hashimoto (Conceptualization [lead], Data curation [lead], Funding acquisition [lead], Investigation [lead], Methodology [lead], Project administration [lead], Resources [lead], Supervision [lead], Validation [supporting], Visualization [equal], Writing—original draft [equal], Writing—review & editing [lead])

Funding

This work was supported by the Japan Agency for Medical Research and Development (AMED) under Grant Numbers JP18dm0307002, JP19dk0307083, JP21dk0307103, JP21wm0425012, JP22dk0307112, JP22tm0424222, and JP24dk0307132; JSPS KAKENHI Grant Numbers JP19H05467, JP20H03611, JP21K17261, JP23H00395, JP23K07001, JP23K16273, and JP25K17172; and Intramural Research Grants for Neurological and Psychiatric Disorders of NCNP (Grants 3-1, 6-1, and 7-5). The funders had no role in the research design, execution, analysis, interpretation, or reporting.

Conflicts of interest

K.M. received honoraria for lectures from Sumitomo Pharma Co., Ltd., Janssen Pharmaceutical K.K., Otsuka Pharmaceutical Co., Ltd., and Mitsubishi Tanabe Pharma Corporation.

J.M. received honoraria for a lecture from Sumitomo Pharma Co., Ltd.

S.I. declares no conflicts of interest.

K.F. received honoraria for lectures from Sumitomo Pharma Co., Ltd., Meiji Seika Pharma Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., MSD K.K., Yoshitomiyakuhin Corporation, Janssen Pharmaceutical K.K., Lundbeck Japan K.K., and Viatris Pharmaceuticals Japan G.K.

K.I. received personal fees over the past 3 years from EA Pharma Co., Ltd., Viatris Pharmaceuticals Japan G.K., Eisai Co., Ltd., MSD K.K., Otsuka Pharmaceutical Co., Ltd., Ono Pharmaceutical Co., Ltd., Kyowa Kirin Co., Ltd., Kowa Co., Ltd., Shionogi & Co., Ltd., Sumitomo Pharma Co., Ltd., Daiichi Sankyo Co., Ltd., Takeda Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Nipro Corporation, Nippon Chemiphar Co., Ltd., Eli Lilly Japan K.K., Boehringer Ingelheim Japan, Inc., Nexera Pharma K.K., Novartis Pharma K.K., Nobelpharma Co., Ltd., Pfizer Japan Inc., Meiji Seika Pharma Co., Ltd., Mochida Pharmaceutical Co., Ltd., Janssen Pharmaceutical K.K., Lundbeck Japan K.K., and Yoshitomiyakuhin Corporation.

F.K. declares no conflicts of interest.

H.T. received honoraria for chairing sessions from Eli Lilly Japan K.K. and Otsuka Pharmaceutical Co., Ltd., and research funding from Biogen Japan, Bristol-Myers Squibb K.K., Eisai Co., Ltd., and Novo Nordisk Pharma Ltd.

S.K. received speaker honoraria from Eisai Co., Ltd., Inter Reha Co., Ltd., Kowa Co., Ltd., Lundbeck Japan K.K., Sumitomo Pharma Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., Teijin Pharma Ltd., and Viatris Pharmaceuticals Japan G.K.; consulting fees from Kyowa Pharmaceutical Industry Co., Ltd., Teijin Pharma Ltd., and Inter Reha Co., Ltd.; and research grants from Teijin Pharma Ltd.

R.H. received honoraria for lectures from Takeda Pharmaceutical Co., Ltd., Viatris Pharmaceuticals Japan G.K., Otsuka Pharmaceutical Co., Ltd., Janssen Pharmaceutical K.K., and Sumitomo Pharma Co., Ltd.; consulting fees from Ono Pharmaceutical Co., Ltd., EA Pharma Co., Ltd., and Boehringer Ingelheim International GmbH; honoraria for chairing sessions from EA Pharma Co., Ltd. and Sumitomo Pharma Co., Ltd.; and research funding from Boehringer Ingelheim International GmbH.

Data availability

The data analyzed in this study are not publicly available because disclosure of personal information was not included in the research protocol, and consent for data sharing was not obtained from participants.