Longitudinal Outcomes Research in Bipolar Disorder: The Heinz C. Prechter Bipolar Research Program

Transcription

1 Longitudinal Outcomes Research in Bipolar Disorder: The Heinz C. Prechter Bipolar Research Program Melvin McInnis, MD, FRCPsych Thomas B. and Nancy Upjohn Woodworth Professor of Bipolar Disorder and Depression

2 Disclosures U.S. Patent : Mood monitoring of bipolar disorder using speech analysis. Assignee: The Regents of the University of Michigan Inventors: Zahi Karam, Satinder Singh, Melvin G McInnis, Emily Mower Provost Consulting and Research Support (past 3 years): Janssen Pharmaceuticals, Otsuka Pharmaceuticals.

3 Coming together is a beginning. Keeping together is progress. Working together is success. Henry Ford

4 What is Bipolar Disorder? Previously referred to as Manic Depression Episodic periods of mania and depression Mania: pathological elevations in mood states Depression: pathological decreases in mood states Dichotomous vs Dimensional? Are bipolar individuals well in between episodes?

5 Bipolar Disorder: Course Pathology of Energy Mania Depression Dynamic Energy State Time Mania Depression anticipate change? 1 year

6 Bipolar Disorder Summary of Known Facts: High hereditary: genetic background accounts for 80% of the variance Several genes implicated, but each causes a negligible effect on the person Bipolar disorder is a clinical disorder. There are no established biological markers of disease. Highly comorbid with other medical and psychiatric illnesses High rates of suicide Management of a chronic and dynamic disorder is often complex. The longitudinal course of the illness is a component of the illness itself. Need to study the disorder over the lifetime

7 Prechter Bipolar Research Program Precision Health Clinic University Community Eligibility Screening Ineligible Eligible Initial Visit Heinz C Prechter DIGS Interview Neuropsychological Testing Blood for Genetics & Biomarkers Questionnaires Mechanisms Predictive Modeling Follow-up Questionnaires every 2 months Longitudinal Active engagement Year 1: Neuropsychological Testing Year 2: LIFE Interview > 1300 participants 75% retention Entering Year 14 Year 3: Phone check in

8 Prechter Cohort Profile: 2017 Bipolar I Observed Clinical Phenotype Bipolar II Disease Neurocognitive Temperament Personality Motivated Behaviors Life Story Sleep and Circadian Outcomes and Course McInnis et al, 2017

9 Ontology: Disciplines & Pluralistic Relationship to Phenotype Class Disciplines (10) Phenotype Classes (7) Disease Neurocognitive Temperament / Personality Motivated Behavior Life Story Sleep / Circadian Outcomes Human Volunteer Participants

10 Impulsivity: Temperament / Personality Feature Bipolar individuals have been shown to have greater measures of impulsivity. Response to urges / drives that lead to actions. Positive energy: thrill seeking that boat feeling Negative energy: deliberate harm Towards self or others Suicide

11 Impulsivity Fz P300 Inhibition Effect: Neutral vs. Happy 3 2 NoGo 1 (NoGo Go P300) uv 0 1 Neu_Inhibition Happy_Inhibition BP Fz CT_Fz SZ_Fz NoGo

12 A Biological Basis for Impulsivity? Neuronal reactivity and calcium metabolism Chen et al, 2014 Chen, Yoo, Herron Neurons derived (ipsc) from BP individuals show greater reactivity Respond to lithium O Shea Lab, CDB Rhythm in Cardiomyocytes Todd Herron, Cardiology

13 Predicting Impulsivity? Moods? There is no more difficult art to acquire than the art of observation... Use your five senses. Learn to see, learn to hear... Sir William Osler ( )

14 Mood States Vary... YMRS Mania Score Manic (10%) Distribution of YMRS and HAMD Scores over one year based on Weekly measures. Euthymic (32%) Depressed (31%) HAMD Depression Score

15 Speech for Mood Monitoring Hypothesis: Speech acoustics is a proxy measure for the internal emotional, mood and affective states Why Speech? Modulated by emotional/neurophysiological state of speaker Used in clinical assessment of psychiatric disorders Something s going on I can hear it in his voice. It scares me (family member of a patient)

16 PRIORI Predicting Individual Outcomes for Rapid Intervention Disease Neurocognitive Observed Clinical Phenotype Temperament Personality Motivated Behaviors Life Story Sleep and Circadian Outcomes and Course Program that evaluates mobile acoustic biosignals to assess & predict mood. Emphasis on integration with daily routine. App Records Calls Intervention Targeted Feedback Feature Extraction Privacy Preserving Detect Mood Symptoms YMRS HamD YMRS: Young Mania Rating Scale HamD: Hamilton Depression Rating Scale AUC = 0.70

17 Identity Vectors i Vector Originally developed for speaker identification tasks. Many applications of i vectors are possible: Language recognition Accent/dialect recognition Speech diarization Acoustic event detection

18 i vector features AUC = 0.78

19 Conclusion PRIORI Identifies mood states from mobile recordings Significantly better than baseline Manic: 0.72 ±0.20 Depressed: 0.75 ±0.14 Consideration of ivectors and personal background data significantly improves accuracy (AUC ) Goals are to predict mood states and impending changes Can we predict risk of suicidality? Drug use?

20 Suicidal Thoughts and Behaviors 27% of individuals with BPI or BPII acted on suicidal urges 15% with intent to die In the Prechter Cohort there are 44 deaths reported suicide, overdoses and accidents account for ~50% : Never 1: Fleeting thoughts or passive death wishes only : Thought but never acted 3: Acted with ambivalence/minimal consequences : Acted with intent to die/serious consequences 5 9: Uncertain 3 HC BP I BP II

21 Precision Health Ontologies Disease Neurocognitive Observed Clinical Phenotype Temperament Personality Motivated Behaviors Life Story Sleep and Circadian Outcomes and Course Full Patient Monitoring (Digital Technologies) BioMarkers Validation Precision Therapy

22 > 80 peer reviewed publications with scientists from 7 U M Schools UM experts: local network

23 *All models are wrong some are useful Longitudinal approach to mood disorders is fundamental Course of illness is an essential element of the phenotype Phenotype ontologies are plural Complex Partially hierarchical Partially recursive Observed Clinical Phenotype Disease Neurocognitive Temperament Personality Motivated Behaviors Life Story Sleep and Circadian Outcomes and Course Biological Mechanism: Better (new) Treatment Predictive Modeling: Effective Treatment *George Box, 1976