Lead Investigator: Gustavo Turecki, McGill University
Title of Proposal Research: Using Artificial Intelligence to Personalize Treatment Selection and Trajectory Monitoring of Bipolar Disorder
Vivli Data Request: 7811
Funding Source: Government: IMADAPT ERA PERMED 2020 grant
Commercial: Aifred Health
Additional Contracts or Consultancies: The researchers that are funded are paid via contracts as the Aifred Health research team is being subcontracted by the Douglas Mental Health University Institute.
Potential Conflicts of Interest: The research team members but excluding the primary investigator, work for a medical technology startup (Aifred Health) that will use the results of this study towards the future development of a clinical decision aid that the company may later commercialize. As part of the ERA-PERMED grant, the Aifred Health research team is being subcontracted by the Douglas Mental Health University Institute. Any conflicts of interest are managed as all publications must pass through Dr. Turecki, who has no interest in the company, as must the direction of the research project. All conflicts have also been declared as part of the successful local REB application. For this study, we do not foresee major potential conflicts of interest since we are only doing secondary data analysis, that will not produce any immediate commercial product.
Summary of the Proposed Research:
Bipolar disorder is a mood disorder characterized by extreme mood swings that cycle between emotional lows (depression) and highs (mania or hypomania). In bipolar disorder type 1, the mania is often severe with psychotic features and requires hospitalization. In bipolar disorder type 2, periods of elevated mood are milder and referred to as hypomania. An estimated 2.8% of the US population lives with bipolar disorder, with 82.9% of them having serious levels of impairment (figures from the National Institute of Mental Health [NIMH]). It is a serious and persistent mental illness (SPMI) that causes tremendous suffering to patients and their loved ones, with a 10-30 times greater risk of suicide in bipolar disorder as compared to general population. Bipolar disorder also poses a significant economic burden to healthcare systems. For example in the USA, the total cost burden is $202.1 billion, which breaks down to an estimated $81,559 per person.
Bipolar disorder is highly clinically complex and there is a dearth of strategies to effectively deal with failure of first line treatments. Only one third of bipolar patients will respond to initial treatment and the majority of patients must try several treatment options or combinations, spanning months or years before identifying one that works best for them. Even once and if the initial treatment selection is successful, there is an 80-90% chance of lifetime recurrence – 49% of these within a 2 year period, increasing the risk of prolonged functional impairment and suicide. Therefore, it is crucial to track the trajectory of the patient longitudinally to identify risk of relapse or switches towards manic or depressive phases, in order to intervene as quickly as possible. Improving our ability to select the most effective treatment for individual patients would mark a significant advancement in the treatment of bipolar disorder.
Deep learning is a process that can identify complex patterns in clinical trial data to embed knowledge of treatments within an artificial neural network (ANN). Our research aims to train an ANN with clinical trial data in order to find patterns in patient characteristics that respond best to a specific treatment and to identify how their condition will vary over time. We have previously demonstrated that this approach has been successful in generating differential treatment benefit prediction in unipolar major depression. Now, we aim to apply this method to bipolar depression as similar datasets exist for this condition, and there are similar challenges in treatment selection and morbidity as in unipolar major depression. The aim of the present analysis will be to predict remission probability via a machine learning model of individual subject data, with a view to identifying predictors of individual patient response in bipolar depression. In addition to predicting remission probability for the current depressive episode, our project will seek to identify predictors and to generate predictive models of switch from bipolar depression to manic and hypomanic phases, or of a lack of sustained remission (i.e., return to a depressive state soon after remission is achieved). The ultimate aim of this research is to develop an evidence-based approach to personalizing treatments for bipolar disorder while monitoring the trajectory of the patient to assist in the prevention of relapse or recurrence.
Requested Studies:
Safety and Efficacy of Olanzapine (LY170053) in the Long-term Treatment for Patients With Bipolar I Disorder, Depressed
Data Contributor: Lilly
Study ID: NCT00618748
Sponsor ID: 11682
Efficacy and Safety of Olanzapine in the Treatment of Patients With Bipolar I Disorder, Depressed: A Randomized, Double-Blind Comparison With Placebo
Data Contributor: Lilly
Study ID: NCT00510146
Sponsor ID: 11218
Olanzapine/Fluoxetine Combination Versus Lamotrigine in the Treatment of Bipolar I Depression
Data Contributor: Lilly
Study ID: NCT00485771
Sponsor ID: 7980
Bipolar Depression Assessment Study on Tx Response
Data Contributor: Lilly
Study ID: NCT00191399
Sponsor ID: 9370
Olanzapine Versus Divalproex and Placebo in the Treatment of Mild to Moderate Mania Associated With Bipolar I Disorder
Data Contributor: Lilly
Study ID: NCT00094549
Sponsor ID: 7029
A Randomized, Double-Blind Study of Depakote Monotherapy, Olanzapine Monotherapy, and Combination Therapy of Depakote Plus Olanzapine in Stable Subjects During the Maintenance Phase of Bipolar Illness
Data Contributor: AbbVie
Study ID: NCT00071253
Sponsor ID: M02-551
An Inpatient Study of the Effectiveness and Safety of Depakote ER in the Treatment of Mania/Bipolar Disorder
Data Contributor: AbbVie
Study ID: NCT00060905
Sponsor ID: M02-540
Olanzapine Versus Divalproex in the Treatment of Acute Mania
Data Contributor: Lilly
Study ID: F1D-US-HGHQ
Sponsor ID: F1D-US-HGHQ
Prevention Of Bipolar Relapse With Olanzapine And Other Mood-Stabilizers. A Prospective Observational Study (PROTECT)
Data Contributor: Lilly
Study ID: F1D-SB-B018
Sponsor ID: F1D-SB-B018
A Controlled Trial of the Efficacy of Rapid Initial Dose Escalation of Olanzapine to Treat Acute Behavioral Agitation in Schizophrenia and Bipolar I Disorder
Data Contributor: Lilly
Study ID: F1D-US-HGIY
Sponsor ID: F1D-US-HGIY
Olanzapine Versus Lithium in Relapse Prevention in Bipolar Disorder
Data Contributor: Lilly
Study ID: F1D-MC-HGHT
Sponsor ID: F1D-MC-HGHT
A Double-Blind Randomized Comparison of the Efficacy and Safety of Short-Acting Intramuscular Olanzapine, Short-Acting Intramuscular Lorazepam and Intramuscular Placebo in Acutely Agitated Patients Diagnosed with Mania Associated with Bipolar Disorder
Data Contributor: Lilly
Study ID: F1D-MC-HGHW
Sponsor ID: F1D-MC-HGHW
Olanzapine Versus Placebo in the Prevention of Relapse in Bipolar Disorder
Data Contributor: Lilly
Study ID: F1D-MC-HGHL
Sponsor ID: F1D-MC-HGHL
Placebo-Controlled Olanzapine Monotherapy in the Treatment of Bipolar I Depression
Data Contributor: Lilly
Study ID: F1D-MC-HGGY
Sponsor ID: F1D-MC-HGGY
Olanzapine Versus Placebo in the Treatment of Bipolar Disorder, Manic or Mixed
Data Contributor: Lilly
Study ID: F1D-MC-HGGW
Sponsor ID: F1D-MC-HGGW
Placebo- and Haloperidol-Controlled Double-Blind Trial of Olanzapine in Patients with Manic or Mixed Episode of Bipolar I Disorder
Data Contributor: Lilly
Study ID: F1D-JE-BMAC
Sponsor ID: F1D-JE-BMAC
A European observational study of health outcomes associated with treatment for mania in Bipolar Disorder.
Data Contributor: Lilly
Study ID: F1D-EW-HGKV
Sponsor ID: F1D-EW-HGKV
A Randomized, Double-Blind, Placebo-Controlled, Phase 3 Study to Evaluate the Efficacy and Safety of Once a Day, TAK-375 (Ramelteon) Tablet for Sublingual Administration (TAK-375SL Tablet) in the Treatment of Acute Depressive Episodes Associated With Bipolar I Disorder in Adult Patients Who Are on Lithium and/or Valproate
Data Contributor: Takeda
Study ID: NCT01467700
Sponsor ID: TAK-375SL_201
A Randomized, Double-Blind, Placebo-Controlled, Phase 3 Study to Evaluate the Efficacy and Safety of Once a Day, TAK-375SL as an Adjunctive Therapy to Treatment-as-Usual in the Maintenance Treatment of Bipolar I Disorder in Adult Patients
Data Contributor: Takeda
Study ID: NCT01467713
Sponsor ID: TAK-375SL_203
A Randomized, Double-Blind, Placebo-Controlled, Phase 3 Study to Evaluate the Efficacy and Safety of Once a Day, TAK-375 (Ramelteon) Tablet for Sublingual Administration (TAK-375SL Tablet) as an Adjunctive Therapy in the Treatment of Acute Depressive Episodes Associated With Bipolar 1 Disorder in Adult Subjects
Data Contributor: Takeda
Study ID: NCT01677182
Sponsor ID: TAK-375SL_301
Olanzapine Added to Mood Stabilizers in the Treatment of Bipolar Disorder
Data Contributor: Lilly
Study ID: F1D-MC-HGFU
Sponsor ID: F1D-MC-HGFU
Olanzapine Versus Placebo in the Treatment of Mania Associated with Bipolar I Disorder
Data Contributor: Lilly
Study ID: F1D-MC-HGEH
Sponsor ID: F1D-MC-HGEH
A Multicenter, Randomized, Double-Blind, Placebo-Controlled Study of Aripiprazole in the Treatment of Patients With Bipolar I Disorder With a Major Depressive Episode. CN138-146 LT is the 26-week Open Label Extension Phase of the Above Titled Protocol, CN138-146 ST.
Data Contributor: Otsuka
Study ID: NCT00094432
Sponsor ID: CN138-146