Elektronische Erfassung des Longitudinalen Verlaufs Bipolarer Störungen Prof. Dr. Dr. Michael Bauer Charité Universitätsmedizin Berlin Charité Campus Mitte (CCM) Klinik für Psychiatrie und Psychotherapie
Bipolare Störungen Epidemiologische Basisdaten Lebenszeitprävalenz ca. 1-2% Spektrum 3-6% Prävalenz bei Frauen und Männern gleich 1/3 der Erkrankten unternimmt einen Suizidversuch, etwa 15-20% mit Erfolg Hohe Rezidivrate: > 90% Müller-Oerlinghausen, Berghöfer, Bauer (2002) Lancet
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Bipolare Störung MANIE RAPID CYCLING NORMALE STIMMUNG DEPRESSION 12 Monate
W, 64, Bipolar II, Ultra Rapid Cycling Drug Legend #1 Lithobid (lithium carbonate) 300 mg #4 Remeron (mirtazapine) 30 mg #7 Premarin (conjugated estrogens) 1.25 mg #2 Effexor XR capsules (venlafaxine) 150 mg #5 Valium (diazepam) 10 mg #3 Effexor XR capsules (venlafaxine) 75 mg #6 Synthroid (levothyroxine) 25 mcg 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 5
Bipolar Disorder: Difficult to Study Bipolar disorder is > Episodic (manic, hypomanic, depressed, mixed, rapid cycling) > Recurrent and life-long illness > High variability within and between patients Major treatment goals: relapse and suicide prevention, interepisodic mood stability Longitudinal mood charting is essential methodology for diagnostic, treatment and research 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 6
Why Self-Charting? Only possibility to record daily Daily recording may capture observations that would be missed if data were only collected at clinical (doctors) visit Daily self-reported data provides doctor important information about a patients mood variation 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 7
Why Self-Charting? Study in bipolar disorder with weekly clinician ratings captured only 34.1% of days of depression and 14.1% of days of mania compared to daily patient selfreporting (Denicoff et al. 1997) 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 8
Why Self-Charting? Several studies in bipolar disorder: process of self-charting of illness parameters may enhance and compliance with prescribed medications (Denicoff et al. 2000, Post et al. 2003) Enhanced adherence to medication improves outcome 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 9
Benefits of Mood Charting Track pattern of disorder > Cycle (episode) length > Frequency > Episode type > Switching speed Track episode triggers (early warning signs of relapse) Track treatment response > New medications > Polypharmacy > Other treatments 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 10
Paper-Based Mood Charting NIMH Life Chart (R. Post) STEP-BD Mood Chart (G. Sachs) Chronosheet (P. Whybrow) Used for individual treatment, patient education and longitudinal studies 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 11
Traditional Longitudinal Studies Paper-based mood charting instruments Expensive High patient drop-out rate High missing value rate Large imbalances in number of observations 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 12
Traditional Data Quality Handwritten documents Data entry introduces errors Slow analysis Poor patient compliance Recall errors 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 13
Computers Commonplace Worldwide 47% increase in household personal computers since 2000 56% of US households have 1 personal computers Microsoft Windows used on 90% of personal computers in US Microsoft Windows used on 85% of personal computers worldwide 60% of households in Australia, Denmark, Netherlands, Norway, Singapore, South Korea and Sweden have 1 personal computers as of 2001 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 14
Automating Data Collection Error checks at time of data entry Data definition is standardized Preferred by many patients Used to monitor a variety of chronic illnesses 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 15
Technology Options: Overview Common technologies that have been used to automate patient data collection for a variety of medical conditions are: >PDA (Personal digital assistant) >IVR (Telephone/Interactice Voice Response) >Web-based software >Software on a home computer Each technology has strengths and weaknesses 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 16
Personal Digital Assistant (PDA) Strengths >Eliminates data entry errors >Devices are portable and re-usable by study sponsor Weaknesses >Most software is not validated >Not suitable for text entry >Devices can be hard to read >Devices can be lost or stolen >Data is not secure >High maintenance cost 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 17
Integrated Voice Response (IVR) Strengths >Very easy to use >Patients only need a touch-tone phone >No costs to patient Weaknesses >Simple yes/no or multiple choice questions only >High initial cost to sponsor 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 18
Web Based Software Strengths >Eliminates data entry costs >No patient hardware costs for sponsor Weaknesses >Patients must have access to a computer >On-line system security requires trained IT staff to monitor 24 by 7 >Custom software development is expensive 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 19
Software On Home Computer Strengths >Eliminates data entry costs >No patient hardware costs for sponsor >Low administrative support costs Weaknesses >Patients must have access to a computer >Security must be designed into the system >Custom software development is expensive 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 20
Technology Selection Evaluate technology alternatives for each unique project, patient population and clinical setting Use validated software or devices (FDA) Consider on-going administrative and support costs, and initial development costs 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 21
Successful Projects Focus on Process A well-defined process is required for a successful project regardless of which technology is used On-going commitment of project sponsors and management Process documentation for staff and patients Staff and patient training Clear assignment of responsibilities On-going security and privacy management 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 22
Development of ChronoRecord Software for Personal (Home) Computer University of California Los Angeles (UCLA), Neuropsychiatric Research Institute Michael Bauer, UCLA, Berlin Michael Bauer, UCLA, Berlin Peter C. Whybrow, UCLA, CA Laszlo Gyulai, University of Pennsylvania, Philadelphia Tasha Glenn, ChronoRecord Association, Inc., Fullerton, CA, USA
ChronoRecord Goals Reduce missing data Reduce administrative costs Eliminate data entry errors Standardize data Reduce patient drop-out rates Provide feedback to patients and clinicians Accelerate analysis 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 24
ChronoRecord Patient Data Daily >Mood > Sleep > Medications > Life events Weekly weight Menstrual cycle for females 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 25
Patient Software 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 26
Patient Mood Entry 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 27
Patient Sleep Entry 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 28
Patient Medication Entry 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 29
Patient Data Management 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 30
ChronoRecord Data Security All patient and administrative programs are password protected All data stored on patient s computer is encrypted All data returned by diskette or e-mail is encrypted 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 31
Immediate Analysis Drug Legend #1 Depakote (divalproex) 250 mg #4 Clonazepam 1.0 mg #2 Depakote (divalproex) 500 mg #5 Zyprexa (olanzapine) 2.5 mg #3 Celexa (citalopram) 20 mg #6 Zyprexa (olanzapine) 5 mg 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 32
ChronoRecord Data Processing Data transferred from multiple sites to a central location All transferred data is encrypted All patient demographic, diagnosis and test data are available All patient identifying demographic data removed Data extracted to SQL compliant database using ODBC Daily medication summary table created 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 33
ChronoRecord Validation Study 96 patients with bipolar I or II diagnosis (DSM-IV) 3 academic centers UCLA (M. Bauer), Univ. of Ottawa (P. Grof) and Univ. of Pennsylvania (L. Gyulai) Patients entered data for 3 months Patient mood self-rating compared with HAMD and YMRS on 4 days 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 34
Validation Study Patient Sample 80 (83%) patients completed study Average of 114.7 days of data per patient 6.1% missing mood data (similar for sleep and medication) Missing mood data not related to mood or demographics (MCAR) 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 35
Validation Study Analysis: Depression Strong correlation between ChronoRecord self-rated mood and HAMD (-.683, p<.001, N=281) Both linear regression and linear mixed model have significant coefficients 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 36
Validation Analysis: Mania Strong correlation between ChronoRecord selfrated mood and YMRS (.719, p<.001, N=107) Both linear regression and linear mixed model have significant coefficients 35 30 25 20 15 Regression Mixed Model Outpatient* Inpatient 10 5 0 0 20 40 60 80 100 120 ChronoRecord 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 37
Mania Validation Study Results ChronoRecord mood self-ratings validated with HAMD Insufficient manic ratings by clinicians to validate with YMRS Results published in Bipolar Disorders 2004:6;67-74 On-going collection of inpatient data in Berlin to validate with YMRS 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 38
Ongoing Mania Validation Preliminary Results 13 inpatients with mania added to 80 in previous study. Patient self-rated mood compared with YMRS and MRS over several weeks. Correlation between ChronoRecord self-rated mood and YMRS increased to (.580, p<.001, N=304) Correlation between ChronoRecord and MRS (.821, p<.821, N=12) 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 39
Validation Study Conclusions Patients like using ChronoRecord Immediate feedback of mood charts helps clinicians and patients Feedback increases long-term study participation rates Automated system produces high quality data for analysis 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 40
W, 40, Bipolar I, Dysthymic, Depressive Episode Drug Legend #1 Depakote (divalproex) 250 mg #4 Zoloft (sertraline) 100 mg #7 Topamax (topiramate) 25 mg #2 Desyrel (trazodone) 150 mg #5 Synthroid (levothyroxine - T4) 75 mcg #3 Remeron (mirtazapine) 30 mg #6 Topamax (topiramate) 100 mg 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 41
W, 22, Bipolar II, Lithium Non- Response, Response to Lamotrigine Drug Legend #1 Lithium Carbonate 300 mg #4 Zoloft (sertraline) 100 mg #2 Lamictal (lamotrigine) 25 mg #5 Risperdal (risperidone) 1 mg #3 Welbutrin SR (bupropion) 100 mg 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 42
W, 43, Bipolar I, Response to Lithium and Thyroid Hormone (T4) Drug Legend #1 Lithium Carbonate capsules 300 mg #4 Levothroid (levothyroxine - T4) 100 mcg #2 Depakote (divalproex) 500 mg #5 Levothroid (levothyroxine - T4) 200 mcg #3 Paxil (paroxetine) 40 mg 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 43
ChronoRecord Association The non-profit ChronoRecord Association distributes software at minimal cost For details, see www.chronorecord.org Patient software available in 7 languages: English (US, Canada and UK), Spanish, German, Polish, Portuguese, Italian and Dutch Global data collection; database > 350 patients 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 44
Sites Using ChronoRecord Canada Martin Alda, MD, Dalhousie University, Halifax Paul Grof, MD, University of Ottawa Germany Michael Bauer, MD, Charité University Medicine Berlin Tom Bschor, MD, Technische Universität Dresden Poland Janusz Rybakowski, MD, Karol Marcinkowski University of Medical Science, Poznan Aleksandra Suwalska, MD, Karol Marcinkowski University of Medical Science, Poznan US UK Natalie Rasgon, MD, Stanford University Peter C. Whybrow, MD, University of California, Los Angeles Rodrigo A. Muñoz, MD, University of California, San Diego Laszlo Gyulai, MD, University of Pennsylvania Anthony Cleare, MD, Institute of Psychiatry, London 30-Aug-06 Bauer et al, Using Technology To Improve Longitudinal Studies.. in Bipolar Disorder 45