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  ["project_title"]=>
  string(110) "PREDIKET: Data-driven identification of clinical and laboratory predictors of response to esketamine treatment"
  ["project_narrative_summary"]=>
  string(743) "Many patients with depression do not respond to standard medications, while some improve after treatment with esketamine. It is still difficult to predict who will benefit, how long the effect will last, and who may experience side effects. In this study, we will analyze anonymized data from clinical trials to identify clinical and laboratory factors linked to treatment response. We will use information such as age, sex, BMI, comorbidities, current medications, baseline depression scores, blood test results, and early changes in symptoms. We will look not only at single factors, but also at patterns and combinations of variables. The results may help personalize treatment, improve safety, and reduce exposure to ineffective therapies."
  ["project_learn_source"]=>
  string(6) "pubmed"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(6) "Monika"
    ["last_name"]=>
    string(6) "Bijata"
    ["degree"]=>
    string(17) "PhD, Habilitation"
    ["primary_affiliation"]=>
    string(44) "Nencki Institute of Experimental Biology PAS"
    ["email"]=>
    string(22) "m.bijata@nencki.edu.pl"
    ["state_or_province"]=>
    string(20) "Masovian Voivodeship"
    ["country"]=>
    string(6) "Poland"
  }
  ["project_key_personnel"]=>
  array(1) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(8) "Weronika"
      ["p_pers_l_name"]=>
      string(5) "Bakun"
      ["p_pers_degree"]=>
      string(3) "Msc"
      ["p_pers_pr_affil"]=>
      string(44) "Nencki Institute of Experimental Biology PAS"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(3) "yes"
    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
  }
  ["project_funding_source"]=>
  string(170) "FIRST TEAM FENG.02.02-IP.05-0075/24 from the Foundation for Polish Science, co-financed by the European Union under the European Funds for Smart Economy 2021-2027 (FENG)."
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1578) "Background:Esketamine shows rapid antidepressant effects in some patients with depression, but treatment response varies, durability is uncertain, and adverse events may occur. Reliable clinical and laboratory predictors that could guide personalized treatment are still lacking.

Objective: To identify clinical, demographic, and laboratory predictors, as well as their combinations, that are associated with response, durability of effect, and safety of esketamine treatment.

Study Design: Secondary analysis of anonymized individual participant-level data from multiple clinical trials of esketamine. A data-driven approach will be applied to explore multidimensional patterns of predictors.

Participants: Adult patients diagnosed with major depressive disorder or treatment-resistant depression who participated in esketamine clinical trials.

Primary and Secondary Outcome Measures: The primary outcome will be change in depression severity measured by validated clinical scales (e.g., MADRS). Secondary outcomes will include early symptom changes, durability of response over time, and the occurrence of adverse events. Both single predictors and profiles based on combinations of variables will be evaluated.

Statistical Analysis: Feature selection will be performed using the Boruta algorithm to identify relevant clinical and laboratory variables. Selected predictors will be further analyzed using multivariable regression models and interaction analyses to assess their direction and magnitude of association with treatment outcomes. "
  ["project_brief_bg"]=>
  string(3146) "Esketamine is an important recent advance in the treatment of depression, especially treatment-resistant depression, offering rapid improvement for some patients who do not respond to standard therapies. However, treatment response is highly variable, ranging from sustained benefit to no response or adverse effects. Currently, clinicians lack reliable tools to predict treatment outcome and durability of response.

Although multiple clinical, demographic and biological factors have been investigated as potential predictors of response to ketamine, the current evidence is inconclusive. Most studies have examined single variables in isolation and results have been heterogeneous and difficult to replicate. Limited attention has been given to interactions between variables and to the possibility that clinically meaningful predictors may emerge only from specific combinations or profiles of patient characteristics. As a result, robust, multidimensional predictive models applicable in clinical practice are still lacking.

The availability of anonymized individual participant-level data from large clinical trials provides a unique opportunity to address these limitations. Such datasets make it possible to move beyond single-factor analyses and to explore complex relationships between clinical variables and treatment outcomes. By analyzing pooled patient-level data from esketamine trials, this project aims to identify clinically meaningful predictors and patterns associated with treatment response and durability of effect.

The study will use routinely collected clinical and laboratory information, including demographic characteristics, body mass index, comorbidities, time since diagnosis, concomitant medications, baseline depression severity, early symptom changes, and laboratory test results. Importantly, the analysis will not be limited to evaluating these factors independently, but will examine how they interact and combine into predictive profiles. This multidimensional approach better reflects real-world clinical complexity and increases the likelihood of generating findings that are directly applicable to patient care.

The scientific significance of this project lies in its potential to show how combinations of multiple factors shape response to esketamine treatment. Using data from several well-controlled clinical trials allows the results to be more generalizable. Identifying predictive profiles may support more precise and personalized treatment, help select patients who are more likely to benefit, identify those at higher risk of adverse events, and reduce unnecessary exposure to ineffective therapy.

From a public health perspective, depression is a leading cause of disability worldwide, and treatment-resistant depression carries particularly high personal, social and economic costs. More accurate treatment selection for esketamine may improve effectiveness, enhance safety, and optimize the use of healthcare resources. This data-driven, multidimensional approach supports the goals of precision psychiatry and evidence-based, personalized mental health care."
  ["project_specific_aims"]=>
  string(1684) "The primary aim of this project is to identify clinical, demographic and laboratory predictors, and their combinations, that are associated with response, durability of effect and safety of esketamine treatment in patients with depression.



Specific aims:

1.	To explore which clinical, demographic and laboratory variables are associated with short-term and longer-term antidepressant response to esketamine, measured by changes in standardized depression rating scales.

2.	To determine whether combinations and patterns of variables provide stronger predictive value than single parameters alone, and to define multidimensional clinical profiles associated with treatment benefit.

3.	To identify clinical and laboratory factors associated with the occurrence of adverse events and reduced tolerability of esketamine treatment.

4.	To develop and evaluate multivariable predictive models that integrate clinical and laboratory data and may support personalized treatment decisions.



The hypotheses:

1.	No single clinical or laboratory parameter is sufficient to reliably predict treatment response; instead, meaningful prediction emerges from specific combinations of variables.

2.	Multidimensional profiles based on demographic, clinical and laboratory data are more strongly associated with treatment outcomes than individual predictors.

3.	Distinct clinical and laboratory patterns are associated with a higher risk of adverse events and poorer tolerability.

These aims reflect a data-driven, multidimensional approach and support the development of personalized strategies for esketamine treatment"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(3) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(49) "new_research_question_to_examine_treatment_safety"
      ["label"]=>
      string(49) "New research question to examine treatment safety"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(1852) "We will use anonymized individual participant-level data from clinical trials of esketamine for depression that are made available through the YODA Project. No data from external sources outside the YODA Project will be used in this study.



Inclusion criteria will be:

Adult participants (≥18 years of age).

Diagnosis of major depressive disorder, including treatment-resistant depression, according to the criteria used in the original trials.

Treatment with esketamine as part of a randomized or controlled clinical trial.

Availability of baseline clinical data, including demographic variables and baseline depression severity.

Availability of follow-up assessments of depressive symptoms using standardized rating scales (e.g., MADRS).





Exclusion criteria will be:

Missing key outcome measures (e.g., absence of post-treatment depression scores).

Missing essential baseline information required for modeling (e.g., age or sex).

Participants who did not receive any dose of esketamine.



No additional exclusion criteria will be applied beyond those already defined in the original clinical trials and those required to ensure data completeness for the planned analyses. Data from all eligible trials will be pooled and analyzed together as an individual participant-level dataset. The analyses will be conducted on the secure YODA Project data platform, without downloading or transferring identifiable patient data. All analyses will be performed using de-identified data in accordance with YODA data access policies. The pooled dataset will be used to explore clinical, demographic and laboratory predictors of treatment response, durability of effect and safety, as well as multidimensional patterns and combinations of variables.

"
  ["project_main_outcome_measure"]=>
  string(2103) "The primary outcome will be change in depression severity following esketamine treatment, measured using standardized and validated clinical rating scales available in the trials (e.g., MADRS). The main endpoint will be defined as the difference between baseline score and post-treatment score at the primary assessment time point specified in each trial. When possible, outcomes will be analyzed both as continuous variables (magnitude of symptom change) and as categorical variables, such as:

Response: predefined percentage reduction in depression score from baseline (≥50% improvement).

Non-response.



Secondary outcome measures:

Durability of response - the maintenance of clinical improvement across available follow-up visits. This will be operationalized as:

Sustained response: meeting response criteria at consecutive follow-up assessments.

Relapse or loss of response: worsening of depression scores after an initial improvement.



Safety and tolerability - Defined using adverse event data reported in the trials, including:

Occurrence of any adverse event.

Occurrence of treatment-related adverse events.

Occurrence of serious adverse events.

These outcomes will be analyzed as binary variables (presence/absence) and, when possible, as categorical variables reflecting severity or type of adverse event.



Handling of outcome definitions across trials:

Because individual trials may differ slightly in assessment schedules or scale versions, outcome measures will be harmonized across studies prior to analysis. When multiple depression scales are available, priority will be given to MADRS; otherwise, comparable validated scales will be used and standardized where necessary. Any modifications to outcome definitions required for harmonization will be clearly described in the final publication. No changes to the conceptual definition of the primary or secondary outcomes are anticipated, only harmonization procedures required to ensure comparability across trials.

"
  ["project_main_predictor_indep"]=>
  string(2043) "This study does not rely on a single main predictor. Instead, the independent variables will consist of a set of clinical, demographic and laboratory characteristics that will be evaluated jointly as potential predictors of treatment response, durability of effect and safety of esketamine.



The main independent variables will include:

Demographic: age (continuous), sex (F/M), and body mass index (BMI, continuous or categorized according to standard clinical cut-offs).

Clinical: baseline depression severity (continuous score on standardized scales), duration of illness (continuous), presence of comorbidities (binary variables), and use of concomitant medications (categorized by drug class).

Laboratory: blood test results available in the trials (e.g., hematological, inflammatory, metabolic or other biochemical markers), analyzed as continuous variables and, when clinically justified, categorized using established reference ranges.

All predictors will initially be analyzed in their continuous form when possible to preserve information. Categorization will be applied only when clinically meaningful or required for interpretation (e.g., normal vs abnormal values).

Because the primary hypothesis is that meaningful prediction emerges from combinations of variables rather than from single factors, the main independent construct of interest will be multidimensional predictor profiles. Feature selection will be performed using the Boruta algorithm to identify variables that contribute to prediction of outcomes. Selected variables will then be entered into multivariable models, including interaction terms where appropriate, to assess their independent and joint effects on change in depression severity and durability of response and safety.

In the publication, all independent variables and their definitions will be reported explicitly, together with the coding strategy (continuous vs categorical), to ensure transparency and reproducibility of the analyses.

"
  ["project_other_variables_interest"]=>
  string(825) "Other variables will be used mainly to describe the study sample and to adjust analyses for differences between trials and treatment exposure. These will include:



Trial-related variables: trial ID and study arm, to account for differences between individual clinical trials.

Treatment-related variables: treatment group (e.g., esketamine dose), number of doses, and treatment schedule.

Follow-up variables: timing of assessments and length of follow-up, used to harmonize analyses across trials.

Data completeness: indicators of missing data, used for quality control and sensitivity analyses.



These variables will not be treated as primary predictors, but will be used for sample characterization, model adjustment, and ensuring comparability across studies



"
  ["project_stat_analysis_plan"]=>
  string(4137) "The analysis will be conducted using anonymized individual participant-level data from esketamine clinical trials. All analyses will be performed on the secure YODA platform using standard statistical software (R/Python).

First, descriptive analyses will be performed to characterize the study population. Continuous variables will be summarized using means and standard deviations or medians and interquartile ranges, depending on their distribution. Categorical variables will be summarized using counts and percentages. Baseline characteristics will be described overall and, when appropriate, stratified by treatment response status.

The primary outcome will be change in depression severity measured by standardized rating scales (preferably MADRS). This outcome will be analyzed both as a continuous variable (magnitude of symptom change) and as a categorical variable (e.g., response vs. non-response based on predefined thresholds such as ≥50% reduction from baseline).

As an initial step, bivariate analyses will be used to explore associations between individual predictors and outcomes. Depending on variable type and distribution, these will include correlation analyses, t-tests or non-parametric equivalents, and chi-square tests. These analyses will be exploratory and used only to describe unadjusted relationships.

The core of the project will be a data-driven, multivariable approach. Feature selection will be performed using the Boruta algorithm. The response vector will be based on changes in MADRS scores or response status. Boruta will be used to identify variables that contribute meaningfully to outcome prediction in a multivariate setting, rather than relying on single-variable significance testing.

Variables identified by Boruta will then be entered into multivariable regression models to estimate the direction and magnitude of their effects. Depending on the outcome definition, these will include:

Linear regression models for continuous outcomes (change in depression scores),

Logistic regression models for binary outcomes (e.g., response vs. non-response, presence vs. absence of adverse events).

Interaction terms will be explored when clinically and statistically justified to assess whether combinations of variables provide additional predictive value beyond individual predictors. This will allow identification of multidimensional clinical profiles associated with treatment response or safety.

For safety outcomes, adverse events will be analyzed as binary variables (any adverse event, serious adverse events) and, when possible, by type or severity. Logistic regression models will be used to identify predictors of increased risk.

If follow-up data allow, durability of response will be analyzed using repeated measures approaches or time-to-event methods. 

All models will be adjusted for basic trial-related variables (e.g., trial ID, treatment arm) to account for differences between individual studies. Sensitivity analyses will be conducted to assess the robustness of results, including:

analyses excluding participants with missing key variables,

alternative outcome definitions,

alternative coding of predictors (continuous vs. categorical).



Missing data will be handled using appropriate methods depending on the extent and pattern of missingness, such as complete case analysis or multiple imputation, if feasible within the YODA environment.



Model performance will be evaluated using standard measures such as:

 - area under the ROC curve (AUC) for classification models,

- explained variance (R²) for continuous outcomes,

- internal validation using resampling techniques (e.g., cross-validation or bootstrapping), when computationally feasible.



This analysis plan is designed to move beyond single-factor testing and to identify multidimensional patterns and combinations of clinical and laboratory variables that are associated with response, durability of effect, and safety of esketamine treatment.

"
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(1662) "The project is planned to be completed within the 12-month data access period.

Anticipated project start date: Month 0–1 after data access approval (data access setup, dataset familiarization, and data structure review).

Data cleaning and harmonization: Months 1–2 - Preparation of analysis-ready datasets, harmonization of variables across trials, and definition of outcome measures and predictors.

Descriptive and exploratory analyses: Months 2–3 - Characterization of the study population and initial bivariate analyses.

Feature selection and multivariable modeling: Months 3–6 - Application of the Boruta algorithm, construction of multivariable regression models, and development of predictive profiles for response and safety.

Advanced and sensitivity analyses: Months 6–8 - Model validation, robustness checks, alternative outcome definitions, and safety analyses.

Final analyses and interpretation: Months 8–9 - Integration of results, preparation of figures and tables, and interpretation of findings.

Manuscript drafting: Months 9–10 - Preparation of the first draft of the manuscript.

Manuscript submission for publication: Months 10–11

Reporting results to the YODA Project: Month 11–12

Submission of a summary of findings and confirmation of compliance with data use requirements.



This timeline ensures completion of all analyses, dissemination of results, and reporting to the YODA Project within the standard 12-month data access period, with sufficient time reserved for data preparation, rigorous analysis, and manuscript preparation.

"
  ["project_dissemination_plan"]=>
  string(1490) "The primary product of this research will be at least one peer-reviewed scientific manuscript reporting the main findings on clinical and laboratory predictors of response, durability and safety of esketamine treatment. The target audience includes clinical psychiatrists, clinical researchers, and scientists working in the fields of mood disorders, treatment-resistant depression, and precision psychiatry.



The main manuscript will focus on multidimensional, data-driven prediction of treatment outcomes using individual participant-level data from multiple clinical trials. Depending on the scope of results, additional secondary manuscripts may be prepared, for example focusing specifically on safety or subgroup analyses.



Potential target journals include high-impact, international peer-reviewed journals such as Molecular Psychiatry, American Journal of Psychiatry, Journal of Affective Disorders, Biological Psychiatry, or European Neuropsychopharmacology. Journal selection will depend on the final scope and emphasis of the results.



In addition to journal publication, study findings may be disseminated through presentations at international scientific conferences and incorporated into future research proposals and clinical discussions. Results will also be reported back to the YODA Project in accordance with data use requirements. All dissemination activities will follow principles of transparency and responsible data sharing."
  ["project_bibliography"]=>
  string(2001) "
Johnston JN, Zarate CA Jr, Kvarta MD. Esketamine in depression: putative biomarkers from clinical research. Eur Arch Psychiatry Clin Neurosci. 2025 Sep;275(6):1559-1572. doi: 10.1007/s00406-024-01865-1. Epub 2024 Jul 13. PMID: 38997425; PMCID: PMC11725628.
Kursa MB, Rudnicki WR. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11), 1–13. https://doi.org/10.18637/jss.v036.i11
Lima Constantino J, Godschalk M, van Dalfsen JH, Veraart JKE, Spijker J, van Exel E, Schoevers RA, Kamphuis J. Demographic and clinical predictors of response and remission in the treatment of major depressive disorder with ketamine and esketamine: A systematic review. Psychiatry Res. 2025 Mar;345:116355. doi: 10.1016/j.psychres.2025.116355. Epub 2025 Jan 5. PMID: 39813859.
Medeiros GC, Gould TD, Prueitt WL, Nanavati J, Grunebaum MF, Farber NB, Singh B, Selvaraj S, Machado-Vieira R, Achtyes ED, Parikh SV, Frye MA, Zarate CA Jr, Goes FS. Blood-based biomarkers of antidepressant response to ketamine and esketamine: A systematic review and meta-analysis. Mol Psychiatry. 2022 Sep;27(9):3658-3669. doi: 10.1038/s41380-022-01652-1. Epub 2022 Jun 27. PMID: 35760879; PMCID: PMC9933928.
Medeiros GC, Matheson M, Demo I, Reid MJ, Matheson S, Twose C, Smith GS, Gould TD, Zarate CA Jr, Barrett FS, Goes FS. Brain-based correlates of antidepressant response to ketamine: a comprehensive systematic review of neuroimaging studies. Lancet Psychiatry. 2023 Oct;10(10):790-800. doi: 10.1016/S2215-0366(23)00183-9. Epub 2023 Aug 22. PMID: 37625426; PMCID: PMC11534374.
Rong C, Park C, Rosenblat JD, Subramaniapillai M, Zuckerman H, Fus D, Lee YL, Pan Z, Brietzke E, Mansur RB, Cha DS, Lui LMW, McIntyre RS. Predictors of Response to Ketamine in Treatment Resistant Major Depressive Disorder and Bipolar Disorder. Int J Environ Res Public Health. 2018 Apr 17;15(4):771. doi: 10.3390/ijerph15040771. PMID: 29673146; PMCID: PMC5923813.
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  ["data_use_agreement_training"]=>
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  ["human_research_protection_training"]=>
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  ["search_order"]=>
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    ["url"]=>
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data partner
array(1) {
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}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(1) {
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}


num of trials
array(1) {
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}


res
array(1) {
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}