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Effectiveness of pharmacological treatments for severe agitation in real-world emergency settings: protocol of individual-participant-data network meta-analysis

Abstract

Background

Severe psychomotor agitation and aggression often require immediate pharmacological intervention, but clear evidence-based recommendations for choosing among the multiple options are lacking. To address this gap, we plan a systematic review and individual-participant-data network meta-analysis to investigate their comparative effectiveness in real-world emergency settings with increased precision.

Methods

We will include randomized controlled trials investigating intramuscular or intravenous pharmacological interventions, as monotherapy or in combination, in adults with severe psychomotor agitation irrespective of the underlying diagnosis and requiring rapid tranquilization in general or psychiatric emergency settings. We will exclude studies before 2002, those focusing on specific reasons for agitation and placebo-controlled trials to avoid concerns related to the transitivity assumption and potential selection biases. We will search for eligible studies in BIOSIS, CENTRAL, CINAHL Plus, Embase, LILACS, MEDLINE via Ovid, PubMed, ProQuest, PsycINFO, ClinicalTrials.gov, and WHO-ICTRP. Individual-participant data will be requested from the study authors and harmonized into a uniform format, and aggregated data will also be extracted from the studies. At least two independent reviewers will conduct the study selection, data extraction, risk-of-bias assessment using RoB 2, and applicability evaluation using the RITES tool. The primary outcome will be the number of patients achieving adequate sedation within 30 min after treatment, with secondary outcomes including the need for additional interventions and adverse events, using odds ratios as the effect size. If enough individual-participant data will be collected, we will synthesize them in a network meta-regression model within a Bayesian framework, incorporating study- and participant-level characteristics to explore potential sources of heterogeneity. In cases where individual-participant data are unavailable, potential data availability bias will be explored, and models allowing for the inclusion of studies reporting only aggregated data will be considered. We will assess the confidence in the evidence using the Confidence in Network Meta-Analysis (CINeMA) approach.

Discussion

This individual-participant-data network meta-analysis aims to provide a fine-tuned synthesis of the evidence on the comparative effectiveness of pharmacological interventions for severe psychomotor agitation in real-world emergency settings. The findings from this study can greatly be provided clearer evidence-based guidance on the most effective treatments.

Systematic review registration

PROSPERO CRD42023402365.

Peer Review reports

Background

Psychomotor agitation is a common medical emergency characterized by inner tension and excessive motor activity [1, 2]. While milder forms can be managed with less invasive interventions like de-escalation techniques, severe agitation poses a heightened risk of harm to the patient, hospital staff, and others. In such scenarios, rapid pharmacological intervention with intramuscular or intravenous drugs becomes essential to quickly calm the patient and, ideally, address the underlying condition without causing oversedation or other adverse events [2, 3].

Multiple pharmacological options are available, including various first- and second-generation antipsychotics, benzodiazepines, either alone or in combination. These options can differ importantly in their effectiveness and risk-to-benefit ratios. Despite this, there are no clear, evidence-based recommendations on the most appropriate treatments, leading to inconsistencies across guidelines [2,3,4,5,6,7,8,9]. For example, the Australian schizophrenia guideline recommends intramuscular olanzapine as the first choice and droperidol as the second [9], while the German S3 schizophrenia guideline recommends parenteral lorazepam as first and antipsychotics as second [4]. These discrepancies underscore the lack of consensus and the need for more robust evidence to guide clinical practice and optimize management strategies.

The complexity of this issue is further compounded by the fact that, until recently, few trials have been conducted in real-world emergency settings with severely agitated participants of various underlying etiologies, who often cannot provide informed consent. Such trials are crucial to generating applicable evidence in this area. Notable global efforts include the paradigmatic TREC trials (Tranquilização Rápida-Ensaio Clínico; Rapid Tranquillization-Clinical Trial) [10,11,12,13,14], and several subsequent trials [15,16,17], which feature pragmatic designs and large sample sizes. Despite the existence of these individual trials, previous systematic reviews on this topic did not focus on emergency settings, making their generalizability uncertain [18,19,20,21,22]. One network meta-analysis, which can compare all available pharmacological interventions even if not directly compared in single trials, excluded psychiatric emergency departments, where agitation is frequent [23]. Furthermore, these meta-analyses were based on study-level data, lacking the increased power and detailed information of individual-participant data needed to generate more precise estimates and examine potential subgroup differences in this heterogeneous condition [24, 25].

Currently, there is no individual-participant-data network meta-analysis that synthesized the evidence from the relevant clinical trials mentioned above. This highlights the need for a comprehensive and fine-grained evidence synthesis to provide more definitive answers and inform treatment decisions for severe agitation in real-world emergency settings.

Objectives

To address this critical gap, we plan a systematic review and individual participant data network meta-analysis of clinical trials to examine the effectiveness and tolerability of intramuscular or intravenous pharmacological interventions for severe psychomotor agitation in general and psychiatric emergency departments.

By synthesizing data from trials conducted in real-world settings and integrating individual participant data with network meta-analysis, we aim to overcome the limitations of previous reviews and provide more precise and applicable information. This approach will enable us to address the heterogeneity of the condition and explore potential subgroup differences, ultimately contributing to the development of more uniform and evidence-based guidelines.

Methods

The protocol of this systematic review was registered to PROSPERO on March 9, 2023 (ID: CRD42023402365) and reported according to the PRISMA statement extension for protocols (PRISMA-P) (eAppendix-1) [26]. The status of the review at the time of submission and any modifications made from the initial version of the protocol are outlined in eAppendix-2. If further amendments to the protocol are necessary, we will update the PROSPERO registration and provide clear reporting of any deviations from the original protocol in the published manuscript.

Eligibility criteria

Participants

We will include adults (as defined in the original studies) with acute and severe psychomotor agitation and/or aggression requiring parenteral pharmacological intervention in emergency settings (see the “Study design and setting”), irrespective of the underlying diagnosis, diagnostic criteria, sex, and ethnicity (see also eAppendix-3 for a table of the inclusion and exclusion criteria).

Eligible patients are expected to be unable to provide informed consent [27], and thus, we will exclude studies conducted in settings requiring informed consent by the patient themselves before treatment (see also “Interventions” and “Study design and setting”), such as industry-sponsored studies designed for regulatory purposes [28]. Including these studies would lead to important selection bias and undermine the generalizability of the findings.

Moreover, severe agitation is a transdiagnostic condition, and pharmacological intervention often precedes formal diagnostic procedures [2]. For this reason, we will not restrict to any diagnostic group, and trials with broad inclusion criteria regarding the causes of agitation or aggression will be eligible. Nevertheless, we will exclude trials focusing on specific reasons of agitation by their inclusion criteria such as studies focusing on delirium or dementia (for which antipsychotics are generally contraindicated). The inclusion of such studies would pose serious concerns to the transitivity assumption for the network meta-analysis (see the “Data synthesis”) [29]. Similarly, we will exclude studies focusing on children, adolescents [30], and patients of advanced age as defined by the inclusion criteria of the original studies. However, we will not further exclude participants based on their age at the IPD level.

Interventions

We will include drugs administered intramuscularly or intravenously, either as monotherapy or in combination, to calm patients with severe agitation, i.e., antipsychotics (e.g., haloperidol, droperidol, olanzapine, aripiprazole, ziprasidone, clotiapine, chlorpromazine, promethazine), benzodiazepines (e.g., lorazepam, midazolam, diazepam), antihistamines (e.g., diphenhydramine), alpha2 adrenergic agonists (e.g., clonidine, dexmedetomidine), and ketamine. The intramuscular and intravenous formulations of the same drug will be regarded as distinct interventions. A combination of eligible drugs will be considered as a new eligible intervention. There will also be no restriction in terms of dose, which will be included as a covariate in the analysis (see “Data synthesis”). We will exclude barbiturates as they are no longer used for this indication due to their narrow therapeutic window. We will also exclude studies with oral and inhaled formulations given that these routes of administration require cooperative patients [2], who would not have been eligible for our analysis (see “Participants”). Moreover, we will exclude studies that used placebo, due to the ethical concerns surrounding its use in emergency settings and the potential for selection bias in such trials.

Comparison groups

There is no single comparison group in a network meta-analysis, given that all eligible experimental interventions will be compared with each other. Yet, we will use intramuscular haloperidol as a reference in the forest plots (see “Data synthesis”) because it is a widely accessible and frequently used drug for agitation [19].

Outcomes

The outcomes were selected in accordance with the design of the TREC trials [10,11,12,13,14], which were based on early consultation with frontline clinicians in busy state hospital psychiatric emergency settings. This selection aimed to cover a broad range of effectiveness, acceptability, tolerability, and service use outcomes (see also a list of outcomes in eAppendix-4).

Primary outcome

Our primary outcome will be the proportion of patients with adequate sedation achieved in each study arm within 30 min after the first administration of the intervention (preferably as close as possible to 20 min). This time-point was selected as primary because severe agitation is a clinical emergency that should be treated as quickly as possible in real-world settings, and it was identified as highly relevant in previous studies [10,11,12,13,14, 23]. Moreover, we will also analyze time to adequate sedation (see below), and secondary time-points will include 10 min, 30 min, 45 min, 60 min, 2 h, 4 h, and 24 h after the first administration of the intervention.

The trials may use various methods to define adequate sedation, including clinical judgement or cutoffs of various rating scales, e.g., Richards’ Sedation Scale [31] or Sedation Assessment Tool (SAT) [32]. If the available data allow, we will aim to apply relatively homogeneous cutoffs and definitions of adequate sedation, i.e., “calm” or “asleep” but ideally not oversedated. Any decisions on choosing the most appropriate definition in each study will be documented and made in consultation with experts in this field (e.g., authors of the original studies who have agreed to join the review team). Nonetheless, according to a previous systematic review, definitions of adequate sedation are expected to be similar across studies and rating scales [23]. We will also use relative effect size indices, which are not anticipated to be substantially affected by potentially different definitions (see “Data synthesis”) [33].

Secondary outcomes

We will also collect data on the following secondary outcomes, if available:

  1. 1.

    Time to adequate sedation, as defined above

  2. 2.

    The proportion of patients requiring additional pharmacological intervention, e.g., an additional dose of the same or another medication

  3. 3.

    The proportion of patients requiring physical restraints

  4. 4.

    Mean scores of rating scales measuring the severity of agitated behavior, e.g., Positive and Negative Syndrome Scale Excitement Component (PANSS-EC) [34] and Overt Aggression Scale (OAS) [35]. However, implementing these rating scales can be difficult in real-world emergency settings, and thus, they may only be available in some of the eligible trials.

  5. 5.

    The proportion of patients discharged from the hospital or the emergency setting. Although hospital discharge is an important service use outcome measured in previous studies [10,11,12,13,14], it may heavily depend on other factors such as accessibility rather than the initial intervention taken.

  6. 6.

    The proportion of patients with important adverse events, i.e., seizures, dystonia, akathisia, parkinsonism, any extrapyramidal side effect, use of antiparkinsonian medications, QTc interval prolongation and arrhythmias, falls, respiratory depression, aspiration, allergic reaction, bronchospasm, oversedation, hypotension, nausea, and vomiting. The adverse events could be reported in various ways across trials, and we will aim to harmonize them using the Medical Dictionary for Regulatory Activities (MedDRA) terminology [36].

  7. 7.

    Mean scale scores of the severity of extrapyramidal side effects, e.g., Simpson-Angus Scale (SAS) [37] and Barnes Akathisia Rating Scale (BARS) [38]

  8. 8.

    The proportion of patients that died due to any reason

  9. 9.

    The proportion of patients with a serious adverse event [39]

  10. 10.

    The proportion of patients that dropped out of the study due to any reason, ineffectiveness, and adverse events

Secondary outcomes will be examined within 30 min, 1 h, 2 h, 4 h, and 24 h after the first administration of the intervention, depending on the availability of data across studies. If longer-term data are available in the respective studies, they will be considered.

Study design and setting

We will include randomized controlled trials (RCTs) that compared at least two drugs, at least two doses of the same drug, two different formulations of drugs, two different combinations of drugs, or combination of drugs versus monotherapy for severe agitation and aggression. We will include studies focusing on investigating rapid tranquilization by assessing sedation within 30 min after the intervention (see “Outcomes”), and a similar approach was used in a previous meta-analysis [23]. Eligible RCTs would be conducted in general or psychiatric emergency rooms or psychiatric emergency wards, where participants with severe psychomotor agitation requiring rapid tranquilization may not be able to provide informed consent before (see “Participants” and “Interventions”). Therefore, placebo-controlled RCTs and other studies requiring informed consent from participants prior to intervention will be excluded. RCTs in which the participants or legal guardians could provide informed consent after the intervention will be included. We will also exclude studies conducted in other specialized settings, such as palliative care, intensive, and critical care units (see “Participants”).

We will include both open and blinded (single- and double-blind) RCTs, but studies with a high risk of bias in the randomization process will be excluded (see the “Risk-of-bias assessment”). In crossover trials, we will use only the first phase to avoid carry-over effects as agitation is often resolved after the first treatment [40]. It is not expected that cluster randomized trials would be found, but in that case, we will consider the implications of clustering in extracting study treatment effects [41].

We will include studies since 2002, i.e., when the first paradigmatic TREC trials were conducted [10, 11]. This decision is also based on the difficulties in retrieving IPD from older trials of more than 20 years ago (e.g., data no longer available) [42] and the potential differences in the design and quality compared with more recent trials [43]. We will exclude studies whose publications have been retracted [44,45,46]. There will be no restrictions on the study eligibility criteria in terms of the language of publication and country of origin [47].

Information sources and search strategy

We will search for eligible trials in multiple electronic databases, i.e., BIOSIS, CENTRAL, CINAHL Plus, Embase, LILACS, MEDLINE via Ovid, PubMed, ProQuest, PsycINFO, and the clinical trial registries ClinicalTrials.gov and WHO-ICTRP. There will be no restrictions on the search strategies in terms of language, publication status, and document type [47]. The search strategies will be developed in collaboration with an information specialist (see the “Acknowledgements”) using keywords for agitation, tranquilization treatment, emergency settings, and clinical trials as presented in eAppendix-5. The final search strategies for all databases will also be reported according to the PRISMA extension for reporting literature searches (PRISMA-S) [48]. We will also inspect the reference list of included studies and previous reviews [18,19,20,21,22,23].

We will contact the authors of eligible studies and pharmaceutical companies associated with eligible industry-sponsored studies, if identified, to request anonymized IPD and any additional relevant studies. E-mails will be sent to the first and/or corresponding author of the studies, and in the event of nonresponse, we will send reminders and attempt to contact other authors or use alternative communication methods such as telephone. If there are persisting uncertainties regarding the eligibility criteria and/or IPD availability due to inadequate author responses despite our efforts, we will classify the study as “awaiting classification” and exclude the study from the analysis.

Study selection and data collection

Study selection

Two independent reviewers will perform a two-step screening process to identify eligible studies from the records obtained in the search. In the first step, they will assess the titles/abstracts to identify potentially relevant studies. In the second step, full texts of potentially relevant or unclear records will be obtained, and the reviewers will assess them against the eligibility criteria. Any disagreements between the two reviewers will be resolved through consultation with a third senior reviewer. In cases where further information is needed, the study authors will be contacted to request additional clarification. The process of study selection will be reported with a flow diagram [49].

Data collection and extraction

We will seek information from individual-participant and/or aggregated data of the eligible studies, covering study identification, study methodology, population, intervention, and outcomes at different time points (see eAppendix-6 for a more detailed list of data items). We will request IPD from the included studies, and we will aim to standardize and harmonize the collected data into a unified format. To ensure data integrity, we will examine for missing, outlier, and duplicated values, assess the adequacy of randomization (if possible with the available data), and cross-check with the summary statistics reported in the published studies. In case of any discrepancies or concerns, we will collaborate with the study authors to address and resolve the issues. When IPD are not available, two independent reviewers will extract aggregated data from the original reports in a Microsoft Access database (see more details in eAppendix-6).

Risk-of-bias assessment

Two independent reviewers will assess the risk of bias in the eligible studies, specifically focusing on the effects of assignment to the intervention using the Risk of Bias 2 (RoB 2), which considers the domains of the randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of reported results [50]. RoB 2 utilizes signaling questions and algorithms to assign domain-level judgements of risk of bias, categorized as “low risk,” “some concerns,” or “high risk” [50]. An overall-level judgement will also be assigned according to the worst judgement in a domain [50]. We will prioritize the information provided by the available IPD over the information available in the original published reports to assess the risk of bias. These judgements will be used to inform the assessment of within-study bias in the evaluation of the confidence of the evidence (see “Confidence in the evidence”).

Assessment of applicability

As per our eligibility criteria, the eligible RCTs could provide evidence on the effectiveness of interventions in real-world settings. To further assess potential applicability issues, two independent reviewers will utilize the Rating of Included Trials on the Efficacy-Effectiveness Spectrum (RITES) [51]. The RITES tool considers the domains of participant characteristics, trial setting, flexibility, and clinical relevance of the interventions, with each domain rated on a 5-Likert scale from 1 “strong emphasis on efficacy” to 5 “strong emphasis on effectiveness” [51]. These judgements will be used to inform the assessment of potential indirectness of the evidence (see the “Confidence in the evidence”).

Data synthesis

Effect sizes

The effect size for continuous outcomes will be the standardized mean difference (SMD) due to the use of various rating scales to measure sedation or agitation, for dichotomous outcomes will be the odds ratio (OR) due to its preferable mathematical properties in meta-analysis [52, 53], and for time-to-event outcomes will be the hazard ratios (HRs). To support interpretation of the summary effects, we will aim to transform relative effects to absolute risks using as the control event rate the pooled absolute risk in the reference group (i.e., intramuscular haloperidol) [54]. Effect sizes will be presented along with 95% confidence/credible intervals (95% CI) and 95% prediction intervals (95% PI). Moreover, we will rank the interventions in network meta-analysis using the surface under the cumulative ranking curve (SUCRA) when treatment effects are measured with precision [55, 56].

Synthesis approach

We will opt for applying IPD network meta-regression models in a Bayesian framework [57,58,59], using a random-effects model. The regression models will include independent variables about the intervention, as well as study-level factors and covariates acting as potential prognostic factors and/or effect modifiers, i.e., age, sex assigned at birth, baseline severity of agitation, diagnostic subgroups (e.g., psychosis, alcohol intoxication), medication use before the intervention, setting (psychiatric or general emergency settings and the proportion of patients with alcohol or substance intoxication), route of administration, dose, publication year, and definition of adequate sedation. The final specification of the regression models and potential standardizations of the variables will be determined a posteriori based on the available data across studies and their clinical relevance. A tentative list of potential effect modifiers by order of importance is provided in eAppendix-6.

We will use minimally informative priors for location parameters (i.e., intercept and coefficients) and half-normal distribution for the between-study standard deviations (τ). Heterogeneity will be quantified with the between-study variance (τ2), assumed to be common across the treatment comparisons in the network meta-analysis [47], and the 95% PI of the treatment effects [60].

For missing outcome and/or covariate data, we will consider using multilevel joint modelling multiple imputation by taking into consideration the stratification of patients in trials and the missingness [61, 62]. Multiple imputed datasets will be generated and analyzed, and the results will be combined using Rubin’s rules [63].

Although we aim to obtain IPD from all eligible studies, we anticipate that this may not be the case for all outcomes. In such cases, we will consider a two-stage approach to combine studies with available IPD with those reporting only aggregate data [57, 58, 64] or conventional network meta-analyses based on the aggregated data in a frequentist framework [65, 66], as Bayesian models with IPD integration can be computationally intensive. This decision will be based on the nature of the outcome and the amount of available IPD across studies, allowing a realistic strategy that maintains scientific rigor [25].

Transitivity assumption and incoherence

The transitivity assumption is a prerequisite for conducting indirect comparisons and network meta-analysis [47]. We anticipate that the trials fulfilling the eligibility criteria (see the “Eligibility criteria” and eAppendix-3) to be sufficiently similar and that the examined interventions can be jointly randomized. We will also examine distribution of potential effect modifiers among the different treatment comparisons (see in the “Synthesis approach”). The potential statistical disagreement between direct and indirect evidence (incoherence) will be examined for each pairwise comparison using the separating indirect from direct evidence (SIDE) approach [67] and for the entire network using a design-by-treatment interaction test [68].

Sensitivity analyses

We will conduct the following sensitivity analysis to investigate the robustness of the findings for the primary outcome: (1) exclusion of open- and single-blinded studies and (2) exclusion of studies with an overall high risk of bias. We will further investigate the potential data availability bias by examining potential differences between studies with available and unavailable IPD in terms of their study design and participant characteristics and their effect sizes.

Reporting bias and small-study effects

We will assess reporting biases for each comparison using the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN) framework, which considers both within-study and across-study reporting bias [69, 70]. This framework assigns risk levels of “low risk,” “some concerns,” and “high risk” due to missing evidence to each comparison [69, 70]. Small-study effects are examined using contour-enhanced [71] and comparison-adjusted funnel plots [72] and by including the study variance as covariate in a meta-regression model. The assessments will be used to inform reporting biases in the evaluation of the confidence in the evidence.

Confidence in the evidence

We will assess confidence in the evidence using the Confidence in Network Meta-Analysis (CINeMA) framework which takes into consideration the domains of within-study bias, indirectness, reporting bias, heterogeneity, imprecision, and incoherence [60, 73]. Specifically, we will assess the confidence in the evidence for the following outcomes: adequate sedation after the intervention (primary outcome), need for additional medication, death, respiratory depression, arrhythmias, oversedation, and at least one extrapyramidal side effect. We will set the margin of equivalence for the odds ratios for these outcomes within the range of 0.83 to 1.20, which is required to assess the domains of imprecision, heterogeneity, and incoherence [60, 73].

Statistical software

Data analysis will be conducted in R statistical software [74] using the packages meta [75] and netmeta [65], crossnma [58, 59], and self-programmed routines in JAGS [76, 77].

Patient and public involvement

The topic of the project was deemed highly relevant by our experiential advisors (consultants or co-authors), consisting of patient and relative representatives from Bündnis für psychisch erkrankte Menschen (BASTA) and Aktionsgemeinschaft der Angehörigen psychisch Kranker (ApK e.V. Bavaria). The experiential advisors will actively participate in meetings regarding the progress of the project and contribute to all important stages of the review such as the design of the protocol and identification of relevant outcomes, the interpretation of findings from an experiential perspective, and the dissemination of findings using accessible language to reach a wider public audience. Patient and public involvement will be reported using the GRIPP2-SF checklist [78].

Discussion

Severe psychomotor agitation is an emergency condition requiring prompt pharmacological intervention, but no clear evidence-based recommendations exist for choosing among the various options, with current treatment guidelines displaying important inconsistencies [2,3,4,5,6,7,8,9]. To fill this gap and better inform treatment decision-making, our planned individual-participant-data network meta-analysis aims to provide a fine-grained synthesis of the evidence on the comparative effectiveness and tolerability of intramuscular or intravenous drugs administered either as monotherapy or in combination for severe psychomotor agitation in real-world emergency settings.

Contextualizing with existing literature

Our planned analysis aims to overcome the limitations of previous reviews [18,19,20,21,22,23]. First, our review aims to provide highly applicable evidence-based information on the effectiveness of pharmacological interventions by designing our eligibility criteria to identify studies conducted in real-world settings, unlike most previous reviews that did not differentiate from explanatory trials [18,19,20,21,22]. This is crucial because explanatory trials, such as those using placebo, conducted by the industry and requiring informed consent before the intervention, typically exclude patients with severe forms of agitation encountered in real-world settings, making their generalizability uncertain [79].

Second, we plan to use an advanced meta-analytic approach, incorporating network meta-analysis and leveraging the advantages of integrating more detailed information from individual-participant data [24, 25]. This approach will enable us to establish hierarchies among the different pharmacological interventions across effectiveness and tolerability outcomes, provide estimates with increased precision, and explore factors that may influence these outcomes, such as differences among diagnostic subgroups and between psychiatric and general emergency settings. Thus, our analysis can extend far beyond a previous network meta-analysis based on aggregated data from studies conducted only in general emergency departments [23]. To our knowledge, such an individual-participant-data network meta-analysis does not exist, highlighting a certain gap in the literature.

Limitations and challenges

Although an IPD network meta-analysis can offer a more elaborate analysis necessary to provide more precise answers for this topic, it is more complex and time and resource intensive [24, 25, 80,81,82]. A major challenge will be acquiring IPD, which may take longer than initially planned, and although we will aim and anticipate to acquire most of the IPD, it may still not be feasible to obtain them for some studies or outcomes [42, 83]. We will explore potential data availability biases [84] and consider meta-analytic models allowing the synthesis of studies providing IPD alongside those reporting only aggregate data [57, 58, 64].

Another challenge would be the management, harmonization, and analysis of the IPD datasets, which are expected to vary substantially in format, completeness, and level of detail [80, 81]. Our main goal is to harmonize these datasets into a common format, allowing a detailed synthesis of the evidence. However, decisions regarding the exact model specification and standardization of variables will need to be made a posteriori based on the available data. This process may require trade-offs between preserving data detail and achieving harmonization, but we will make pragmatic choices that maintain scientific rigor.

Conclusion

Identifying the most appropriate pharmacological intervention for the management of severe psychomotor agitation is crucial in real-world emergency settings, but clear evidence-based recommendations are lacking. We hope the findings of our individual-participant-data network meta-analysis will provide the necessary information on the effectiveness and tolerability of pharmacological interventions to guide treatment decision-making for this common and heterogeneous emergency condition and facilitate the creation of more uniform and acceptable guideline recommendations. Moreover, our analysis will help identify potential gaps in the literature and pharmacological options that may warrant additional research. The findings will be published in a peer-reviewed scientific journal and further disseminated through plain-language summaries and presentations to ensure broad accessibility and uptake, facilitating their implementation in clinical practice.

Availability of data and materials

Not applicable.

Abbreviations

95% CI:

95% Confidence or credible intervals

95% PI:

95% Prediction intervals

ApK e.V.:

Angehörigen psychisch Kranker

BARS:

Barnes Akathisia Rating Scale

BASTA:

Bündnis für psychisch erkrankte Menschen

CINeMA:

Confidence in Network Meta-Analysis

HR:

Hazard ratio

IPD:

Individual-participant data

MedDRA:

Medical Dictionary for Regulatory Activities

OAS:

Overt Aggression Scale

OR:

Odds ratio

PANSS-EC:

Positive and Negative Syndrome Scale-Excitement Component

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

RCT:

Randomized controlled trial

RITES:

Rating of Included Trials on the Efficacy-Effectiveness Spectrum

ROB-MEN:

Risk Of Bias due to Missing Evidence in Network meta-analysis

SAS:

Simpson-Angus Scale

SIDE:

Separating indirect from direct evidence

SMD:

Standardized mean difference

SUCRA:

Surface under the cumulative ranking curve

TREC:

Tranquilização Rápida-Ensaio Clínico

τ:

Between-study standard deviation

References

  1. APA. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, vol. 5. DC: American psychiatric association Washington; 2013.

    Google Scholar 

  2. Garriga M, Pacchiarotti I, Kasper S, Zeller SL, Allen MH, Vázquez G, Baldaçara L, San L, McAllister-Williams RH, Fountoulakis KN, et al. Assessment and management of agitation in psychiatry: expert consensus. World J Biol Psychiatry. 2016;17(2):86–128.

    Article  PubMed  Google Scholar 

  3. Roppolo LP, Morris DW, Khan F, Downs R, Metzger J, Carder T, Wong AH, Wilson MP. Improving the management of acutely agitated patients in the emergency department through implementation of Project BETA (best practices in the evaluation and treatment of agitation). J Am Coll Emerg Physicians Open. 2020;1(5):898–907.

    Article  PubMed  PubMed Central  Google Scholar 

  4. DGPPN: S3-Leitlinie Schizophrenie. In: In. Edited by Deutsche Gesellschaft für Psychiatrie und Psychotherapie PuNeVD. 2019.

    Google Scholar 

  5. Pajonk F-G, Messer T, Berzewski H. In: S2k-Leitlinie Notfallpsychiatrie: Springer. 2020.

    Chapter  Google Scholar 

  6. National Collaborating Centre for Mental H. In: Violence and aggression: short-term management in mental health, health and community settings. 2015.

    Google Scholar 

  7. Baldaçara L, Diaz AP, Leite P, Pereira LA, Dos Santos RM, Gomes VdP, Calfat EL, Ismael F, Périco CA, Porto DM. Brazilian guidelines for the management of psychomotor agitation. Part 2. Pharmacological approach. Braz J Psychiatry. 2019;41:324–35.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Brown MD, Byyny R, Diercks DB, Gemme SR, Gerardo CJ, Godwin SA, Hahn SA, Hatten BW, Haukoos JS, Ingalsbe GS. Clinical policy: critical issues in the diagnosis and management of the adult psychiatric patient in the emergency department. Ann Emerg Med. 2017;69(4):480–98.

    Article  PubMed  Google Scholar 

  9. Galletly C, Castle D, Dark F, Humberstone V, Jablensky A, Killackey E, Kulkarni J, McGorry P, Nielssen O, Tran N. Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for the management of schizophrenia and related disorders. Aust N Z J Psychiatry. 2016;50(5):410–72.

    Article  PubMed  Google Scholar 

  10. TREC Collaborative Group. Rapid tranquillisation for agitated patients in emergency psychiatric rooms: a randomised trial of midazolam versus haloperidol plus promethazine. BMJ. 2003;327(7417):708–13.

    Article  PubMed Central  Google Scholar 

  11. Alexander J, Tharyan P, Adams C, John T, Mol C, Philip J. Rapid tranquillisation of violent or agitated patients in a psychiatric emergency setting Pragmatic randomised trial of intramuscular lorazepam v haloperidol plus promethazine. Br J Psychiatry. 2004;185:63–9.

    Article  PubMed  Google Scholar 

  12. Raveendran NS, Tharyan P, Alexander J, Adams CE. Rapid tranquillisation in psychiatric emergency settings in India: pragmatic randomised controlled trial of intramuscular olanzapine versus intramuscular haloperidol plus promethazine. BMJ. 2007;335(7625):865.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Huf G, Coutinho ES, Adams CE. Rapid tranquillisation in psychiatric emergency settings in Brazil: pragmatic randomised controlled trial of intramuscular haloperidol versus intramuscular haloperidol plus promethazine. BMJ. 2007;335(7625):869.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dib JE, Yaacoub HE, Ikdais WH, Atallah E, Merheb TJ, Ajaltouni J, Akkari M, Mourad M, Nasr ME, Hachem D, et al. Rapid tranquillisation in a psychiatric emergency hospital in Lebanon: TREC-Lebanon - a pragmatic randomised controlled trial of intramuscular haloperidol and promethazine v intramuscular haloperidol, promethazine and chlorpromazine. Psychol Med. 2022;52(13):2751–9.

    Article  PubMed  Google Scholar 

  15. Mantovani C, Labate Cm Fau - Sponholz A, Jr., Sponholz A Jr Fau - de Azevedo Marques JM, de Azevedo Marques Jm Fau - Guapo VG, Guapo Vg Fau - de Simone Brito dos Santos ME, de Simone Brito dos Santos Me Fau - Pazin-Filho A, Pazin-Filho A Fau - Del-Ben CM, Del-Ben CM: Are low doses of antipsychotics effective in the management of psychomotor agitation? A randomized, rated-blind trial of 4 intramuscular interventions. 2013(1533–712X (Electronic)).

  16. Knott JC, Taylor DM, Castle DJ. Randomized clinical trial comparing intravenous midazolam and droperidol for sedation of the acutely agitated patient in the emergency department. Ann Emerg Med. 2006;47(1):61-7. https://doi.org/10.1016/j.annemergmed.2005.07.003.

  17. Chan EW, Lao KSJ, Lam L, Tsui SH, Lui CT, Wong CP, Graham CA, Cheng CH, Chung TS, Lam HF, Ting SM, Knott JC, Taylor DM, Kong DCM, Leung LP, Wong ICK. Intramuscular midazolam, olanzapine, or haloperidol for the management of acute agitation: A multi-centre, double-blind, randomised clinical trial. EClinicalMedicine. 2021;32:100751. https://doi.org/10.1016/j.eclinm.2021.100751.

  18. Bak M, Weltens I, Bervoets C, De Fruyt J, Samochowiec J, Fiorillo A, Sampogna G, Bienkowski P, Preuss WU, Misiak B, Frydecka D, Samochowiec A, Bak E, Drukker M, Dom G, The pharmacological management of agitated and aggressive behaviour: A systematic review and meta-analysis. Eur Psychiatry. 2019;57:78-100. https://doi.org/10.1016/j.eurpsy.2019.01.014.

  19. Ostinelli EG, Brooke-Powney MJ, Li X, Adams CE. Haloperidol for psychosis-induced aggression or agitation (rapid tranquillisation). Cochrane Database Syst Rev. 2017;7(7):Cd009377.

    PubMed  Google Scholar 

  20. Zaman H, Sampson SJ, Beck ALS, Sharma T, Clay FJ, Spyridi S, Zhao S, Gillies D. Benzodiazepines for psychosis‐induced aggression or agitation. Cochrane Database Syst Rev 2017;12(12):CD003079.

    PubMed  Google Scholar 

  21. Uribe ES, Bravo-Rodríguez CA, Navarrete-Juárez ME, Medrano-Juarez SB, Lucio RH, Rojas-Guzman KE, Lozano-Carrillo LC: Pharmacological management of acute agitation in psychiatric patients: an umbrella review. 2024.

  22. Muir-Cochrane E, Grimmer K, Gerace A, Bastiampillai T, Oster C, Safety and effectiveness of olanzapine and droperidol for chemical restraint for non-consenting adults: a systematic review and meta-analysis. Australas Emerg Care. 2021;24(2):96-111. https://doi.org/10.1016/j.auec.2020.08.004.

  23. deSouza IS, Thode HC Jr, Shrestha P, Allen R, Koos J, Singer AJ. Rapid tranquilization of the agitated patient in the emergency department: a systematic review and network meta-analysis. Am J Emerg Med. 2022;51:363–73.

    Article  PubMed  Google Scholar 

  24. Riley RD, Dias S, Donegan S, Tierney JF, Stewart LA, Efthimiou O, Phillippo DM: Using individual participant data to improve network meta-analysis projects. BMJ Evid Based Med 2022.

  25. Kanters S, Karim ME, Thorlund K, Anis A, Bansback N. When does the use of individual patient data in network meta-analysis make a difference? A simulation study. BMC Med Res Methodol. 2021;21(1):21. https://doi.org/10.1186/s12874-020-01198-2.

  26. Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA. Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015: elaboration and explanation. BMJ : British Medical Journal. 2015;349:g7647.

    Article  Google Scholar 

  27. Dickert NW, Sugarman J. Ethics and regulatory barriers to research in emergency settings. Ann Emerg Med. 2018;72(4):386–8.

    Article  PubMed  Google Scholar 

  28. Paris G, Bighelli I, Deste G, Siafis S, Schneider-Thoma J, Zhu Y, Davis JM, Vita A, Leucht S. Short-acting intramuscular second-generation antipsychotic drugs for acutely agitated patients with schizophrenia spectrum disorders A systematic review and network meta-analysis. Schizophr Res. 2021;229:3–11.

    Article  CAS  PubMed  Google Scholar 

  29. Chaimani A, Caldwell DM, Li T, Higgins JPT, Salanti G: Undertaking network meta‐analyses. Cochrane handbook for systematic reviews of interventions 2019:285-320.

  30. Gerson R, Malas N, Feuer V, Silver GH, Prasad R, Mroczkowski. Best Practices for Evaluation and Treatment of Agitated Children and Adolescents (BETA) in the Emergency Department: Consensus Statement of the American Association for Emergency Psychiatry. West J Emerg Med. 2019;20(2):409-18. https://doi.org/10.5811/westjem.2019.1.41344.

  31. Richards JR, Derlet RW, Duncan DR. Chemical restraint for the agitated patient in the emergency department: lorazepam versus droperidol. J Emerg Med. 1998;16(4):567–73.

    Article  CAS  PubMed  Google Scholar 

  32. Calver LA, Stokes B, Isbister GK. Sedation assessment tool to score acute behavioural disturbance in the emergency department. Emerg Med Australas. 2011;23(6):732–40.

    Article  PubMed  Google Scholar 

  33. Furukawa TA, Akechi T, Wagenpfeil S, Leucht S. Relative indices of treatment effect may be constant across different definitions of response in schizophrenia trials. Schizophr Res. 2011;126(1–3):212–9.

    Article  PubMed  Google Scholar 

  34. Montoya A, Valladares A, Lizán L, San L, Escobar R, Paz S. Validation of the excited component of the Positive and Negative Syndrome Scale (PANSS-EC) in a naturalistic sample of 278 patients with acute psychosis and agitation in a psychiatric emergency room. Health Qual Life Outcomes. 2011;9:18.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Yudofsky SC, Silver JM, Jackson W, Endicott J, Williams D. The Overt Aggression Scale for the objective rating of verbal and physical aggression. Am J Psychiatry. 1986;143:35–9.

    Article  CAS  PubMed  Google Scholar 

  36. Brown EG, Wood L, Wood S. The Medical Dictionary for Regulatory Activities (MedDRA). Drug Saf. 1999;20(2):109–17.

    Article  CAS  PubMed  Google Scholar 

  37. Simpson GM, Angus JWS. A rating scale for extrapyramidal side effects. Acta Psychiatr Scand. 1970;45(S212):11–9.

    Article  Google Scholar 

  38. Barnes TRE. A rating scale for drug-induced akathisia. Br J Psychiatry. 1989;154(5):672–6.

    Article  CAS  PubMed  Google Scholar 

  39. Bhuiyan PS, Rege NN: ICH harmonised tripartite guideline: guideline for good clinical practice. 1996.

  40. Elbourne DR, Altman DG, Higgins JPT, Curtin F, Worthington HV, Vail A. Meta-analyses involving cross-over trials: methodological issues. Int J Epidemiol. 2002;31(1):140–9. https://doi.org/10.1093/ije/31.1.140.

  41. Donner A, Piaggio G, Villar J. Statistical methods for the meta-analysis of cluster randomization trials. Stat Methods Med Res. 2001;10(5):325–38.

    Article  CAS  PubMed  Google Scholar 

  42. Veroniki AA, Ashoor HM, Le SPC, Rios P, Stewart LA, Clarke M, Mavridis D, Straus SE, Tricco AC. Retrieval of individual patient data depended on study characteristics: a randomized controlled trial. J Clin Epidemiol. 2019;113:176–88.

    Article  PubMed  Google Scholar 

  43. Brunoni AR, Tadini L, Fregni F. Changes in clinical trials methodology over time: a systematic review of six decades of research in psychopharmacology. PLoS ONE. 2010;5(3):e9479.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Weeks J, Cuthbert A, Alfirevic Z. Trustworthiness assessment as an inclusion criterion for systematic reviews—what is the impact on results? Cochrane Evidence Synthesis and Methods. 2023;1(10):e12037.

    Article  Google Scholar 

  45. Weibel S, Popp M, Reis S, Skoetz N, Garner P, Sydenham E. Identifying and managing problematic trials: a research integrity assessment tool for randomized controlled trials in evidence synthesis. Res Synth Methods. 2023;14(3):357–69.

    Article  PubMed  Google Scholar 

  46. Kataoka Y, Banno M, Tsujimoto Y, Ariie T, Taito S, Suzuki T, Oide S, Furukawa TA. Retracted randomized controlled trials were cited and not corrected in systematic reviews and clinical practice guidelines. J Clin Epidemiol. 2022;150:90–7.

    Article  PubMed  Google Scholar 

  47. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA: Cochrane Handbook for Systematic Reviews of Interventions: John Wiley & Sons; 2019.

  48. Rethlefsen ML, Kirtley S, Waffenschmidt S, Ayala AP, Moher D, Page MJ, Koffel JB, Blunt H, Brigham T, Chang S, et al. PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews. Syst Rev. 2021;10(1):39. https://doi.org/10.1186/s13643-020-01542-z.

  49. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, Cates CJ, Cheng HY, Corbett MS, Eldridge SM, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898.

    Article  PubMed  Google Scholar 

  51. Wieland LS, Berman BM, Altman DG, Barth J, Bouter LM, D’Adamo CR, Linde K, Moher D, Mullins CD, Treweek S, et al. Rating of included trials on the efficacy-effectiveness spectrum: development of a new tool for systematic reviews. J Clin Epidemiol. 2017;84:95–104.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Doi SA, Furuya-Kanamori L, Xu C, Lin L, Chivese T, Thalib L: Questionable utility of the relative risk in clinical research: a call for change to practice. J Clin Epidemiol 2020.

  53. Bakbergenuly I, Hoaglin DC, Kulinskaya E. Pitfalls of using the risk ratio in meta-analysis. Res Synth Methods. 2019;10(3):398-419. https://doi.org/10.1002/jrsm.1347.

  54. Leucht S, Siafis S, Engel RR, Schneider-Thoma J, Bighelli I, Cipriani A, Furukawa TA, Davis JM. How efficacious are antipsychotic drugs for schizophrenia? An interpretation based on 13 effect size indices. Schizophr Bull. 2022;48(1):27–36.

    Article  PubMed  Google Scholar 

  55. Salanti G, Nikolakopoulou A, Efthimiou O, Mavridis D, Egger M, White IR. Introducing the treatment hierarchy question in network meta-analysis. Am J Epidemiol. 2022;191(5):930–8.

    Article  PubMed  Google Scholar 

  56. Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol. 2011;64(2):163–71.

    Article  PubMed  Google Scholar 

  57. Donegan S, Williamson P, D’Alessandro U, Garner P, Smith CT. Combining individual patient data and aggregate data in mixed treatment comparison meta-analysis: individual patient data may be beneficial if only for a subset of trials. Stat Med. 2013;32(6):914–30.

    Article  PubMed  Google Scholar 

  58. Hamza T, Chalkou K, Pellegrini F, Kuhle J, Benkert P, Lorscheider J, Zecca C, Iglesias-Urrutia CP, Manca A, Furukawa TA, et al. Synthesizing cross-design evidence and cross-format data using network meta-regression. Res Synth Methods. 2023;14:283–300.

    Article  PubMed  Google Scholar 

  59. Hamza T, Schwarzer G, Salanti G: crossnma: cross-design and cross-format synthesis using network meta-analysis and network meta-regression. In.; 2022.

  60. Nikolakopoulou A, Higgins JPT, Papakonstantinou T, Chaimani A, Del Giovane C, Egger M, Salanti G. CINeMA: An approach for assessing confidence in the results of a network meta-analysis. PLoS Med, 2020;17(4):e1003082.

  61. Van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R. J Stat Softw. 2011;45:1–67.

    Article  Google Scholar 

  62. Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40–9.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Rubin DB: Multiple imputation for nonresponse in surveys, vol. 81: John Wiley & Sons; 2004.

  64. Saramago P, Sutton AJ, Cooper NJ, Manca A. Mixed treatment comparisons using aggregate and individual participant level data. Stat Med. 2012;31(28):3516-36. https://doi.org/10.1002/sim.5442.

  65. Balduzzi S, Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti G, Efthimiou O, Schwarzer G. netmeta: an R package for network meta-analysis using frequentist methods. J Stat Softw. 2023;106(2):1–40.

    Article  Google Scholar 

  66. Rücker G: Network meta-analysis, electrical networks and graph theory. 2012(1759–2879 (Print)).

  67. König J, Krahn U, Binder H. Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Stat Med. 2013;32(30):5414-29. https://doi.org/10.1002/sim.6001.

  68. Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012;3(2):98–110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chiocchia V, Nikolakopoulou A, Higgins JPT, Page MJ, Papakonstantinou T, Cipriani A, Furukawa TA, Siontis GCM, Egger M, Salanti G. ROB-MEN: a tool to assess risk of bias due to missing evidence in network meta-analysis. BMC Med. 2021;19(1):304.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Chiocchia V, Holloway A, Salanti G. Semi-automated assessment of the risk of bias due to missing evidence in network meta-analysis: a guidance paper for the ROB-MEN web-application. BMC Med Res Methodol. 2023;23(1):223.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. J Clin Epidemiol. 2008;61(10):991–6.

    Article  PubMed  Google Scholar 

  72. Chaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G. Graphical tools for network meta-analysis in STATA. PLoS ONE. 2013;8(10):e76654.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Papakonstantinou T, Nikolakopoulou A, Higgins JPT, Egger M, Salanti G. CINeMA: software for semiautomated assessment of the confidence in the results of network meta-analysis. Campbell Syst Rev. 2020;16(1):e1080.

    Article  PubMed  PubMed Central  Google Scholar 

  74. R Core Team. R: a language and environment for statistical computing. 2013.

    Google Scholar 

  75. Balduzzi S, Rücker G, Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health. 2019;22(4):153–60.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Hornik K, Leisch F, Zeileis A, Plummer M: JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: 2003 2003; 2003.

  77. Plummer, M. rjags: Bayesian Graphical Models using MCMC. R package version 4-15, 2023. https://cran.rproject.org/web/packages/rjags/rjags.pdf. Accessed 17 July 2024.

  78. Staniszewska S, Brett J, Simera I, Seers K, Mockford C, Goodlad S, Altman DG, Moher D, Barber R, Denegri S, Entwistle A, Littlejohns P, Morris C, Suleman R, Thomas V, Tysall C, GRIPP2 reporting checklists: tools to improve reporting of patient and public involvement in research. Bmj. 2017;358:j3453.

  79. Chan EW, Taylor DM, Phillips GA, Castle DJ, Knott JC, Kong DCM. May I have your consent? Informed consent in clinical trials — feasibility in emergency situations. J Psychiatr Intensive Care. 2011;7(2):109-13.

  80. Ventresca M, Schünemann HJ, Macbeth F, Clarke M, Thabane L, Griffiths G, Noble S, Garcia D, Marcucci M, Iorio A, et al. Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide. BMC Med Res Methodol. 2020;20(1):113.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Maxwell L, Shreedhar P, Carabali M, Levis B. How to plan and manage an individual participant data meta-analysis. An illustrative toolkit Research Synthesis Methods. 2024;15(1):166–74.

    Article  PubMed  Google Scholar 

  82. Stewart LA, Tierney JF. To IPD or not to IPD? Advantages and disadvantages of systematic reviews using individual patient data. Eval Health Prof. 2002;25(1):76–97.

    Article  PubMed  Google Scholar 

  83. Nevitt SJ, Marson AG, Davie B, Reynolds S, Williams L, Smith CT. Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review. BMJ. 2017;357:j1390.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Tsujimoto Y, Fujii T, Onishi A, Omae K, Luo Y, Imai H, Takahashi S, Itaya T, Pinson C, Nevitt SJ. No consistent evidence of data availability bias existed in recent individual participant data meta-analyses: a meta-epidemiological study. J Clin Epidemiol. 2020;118:107–14.

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to thank Dr. Farhad Shokraneh, Systematic Review Consultants Ltd., Nottingham, UK, who will develop the search strategies and will conduct the search in the electronic databases.

Funding

Open Access funding enabled and organized by Projekt DEAL. This work is funded by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung/BMBF) under the grand number 01KG2208. The funder was not involved in the design of the study, in collection of data, in data analysis or interpretation, or in manuscript writing. The study was registered prospectively.

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Authors and Affiliations

Authors

Contributions

SS, conceptualization, methodology, writing — original draft, supervision, and project administration. HW, methodology and writing — review and editing. NN, methodology and writing — review and editing. JS-T, methodology and writing — review and editing. IB, methodology and writing — review and editing. CL, writing — review and editing. Joseph E. Dib, writing — review and editing. PT, writing — review and editing. LAC, writing — review and editing. GKI, writing — review and editing. EWYC, writing — review and editing. JCK, writing — review and editing. CYLY, writing — review and editing. CM, writing — review and editing. MLM, writing — review and editing. DB, writing — review and editing. WGH, writing — review and editing. W-PH, methodology, writing — review and editing. GH, writing — review and editing. JA, writing — review and editing. NSR, writing — review and editing. ESFC, writing — review and editing. JP, writing — review and editing. CEA, methodology and writing — review and editing.GS, methodology, writing — review and editing. SL, conceptualization, methodology, writing — original draft, supervision, project administration, and funding acquisition.

Corresponding author

Correspondence to Spyridon Siafis.

Ethics declarations

Ethics approval and consent to participate

This project has been approved by the Ethics Committee of the Technical University of Munich on May 5, 2023 (2023–190-S-SR).

Consent for publication

Not applicable.

Competing interests

Spyridon Siafis, none reported. Hui Wu, none reported. Nobuyuki Nomura has received speaker fees from Eisai, Meiji Seika Pharma, Otsuka, and Sumitomo Pharma and manuscript fees from Sumitomo Pharma. Johannes Schneider-Thoma, none reported. Irene Bighelli, none reported. Carolin Lorenz, none reported. Joseph E. Dib is author of potentially eligible trials and no other conflicts of interest reported. Prathap Tharyan is author of potentially eligible trials and no other conflicts of interest reported. Leonie A. Calver is author of potentially eligible trials and no other conflicts of interest reported. Geoffrey K. Isbister is author of potentially eligible trials and no other conflicts of interest reported. Esther W. Y. Chan reports grants from the Hong Kong Research Grants Council of the Government of the Hong Kong SAR, Research Fund Secretariat of the Food and Health Bureau, National Natural Science Fund of China, Wellcome Trust, Bayer, Bristol-Myers Squibb, Pfizer, Janssen, Amgen, Takeda, RGA Reinsurance Company, AstraZeneca, Narcotics Division of the Security Bureau of the Hong Kong Special Administrative Region, Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region, Novartis, and National Health and Medical Research Council Australia and honorarium from Hospital Authority, outside the submitted work. E. W. Y. C. is also author of potentially eligible trials. Jonathan C. Knott is author of potentially eligible trials and no other conflicts of interest reported. Celene Y. L. Yap is author of potentially eligible trials and no other conflicts of interest reported. Célia Mantovani is author of potentially eligible trials and no other conflicts of interest reported. Marc L. Martel is author of potentially eligible trials and no other conflicts of interest. David Barbic is author of potentially eligible trials and no other conflicts of interest reported. William G. Honer has served as a consultant to Translational Life Sciences, Newron, AbbVie, and Boehringer Ingelheim. W. G. H. is also author of potentially eligible trials reported. Wulf-Peter Hansen, none reported. Gisele Huf is author of potentially eligible trials and no other conflicts of interest reported. Jacob Alexander is author of potentially eligible trials and no other conflicts of interest reported. Nirmal S. Raveendran is author of potentially eligible trials and no other conflicts of interest reported. Evandro S. F. Coutinho is author of potentially eligible trials and no other conflicts of interest reported. Josef Priller, none reported. Clive E. Adams is author of potentially eligible trials and no other conflicts of interest reported. Georgia Salanti, none reported. Stefan Leucht has in the last 3 years received honoraria for advising/consulting and/or for lectures and/or for educational material from Angelini, Boehringer Ingelheim, Eisai, Ekademia, Gedeon Richter, Janssen, Karuna, Kynexis, Lundbeck, Medichem, Medscape, Mitsubishi, Otsuka, Novo Nordisk, Recordati, Rovi, and Teva.

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13643_2024_2623_MOESM1_ESM.pdf

Additional file 1: eAppendix-1. PRISMA-P. eAppendix-2: Status of the review and PROSPERO registration. eAppendix-3. Table of inclusion and exclusion criteria. eAppendix-4. List of outcomes. eAppendix-5. Search strategies. eAppendix-6. Data items and data extraction of aggregated data

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Siafis, S., Wu, H., Nomura, N. et al. Effectiveness of pharmacological treatments for severe agitation in real-world emergency settings: protocol of individual-participant-data network meta-analysis. Syst Rev 13, 205 (2024). https://doi.org/10.1186/s13643-024-02623-z

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