Digital Biomarkers and Predictive ehealth-systems

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1 Digital Biomarkers and Predictive ehealth-systems Markku Hämäläinen, PhD, CSO Kontigo Care AB Uppsala, Sweden

2 My professional background Organic Chemist, Uppsala University PhD in applied chemometrics at Chemical Department of Agr.Univ. Senior scientist in Chemometrics at Biacore and GE-Healthcare R&D Speciality: Biosensors and drug discovery ~50 scientific papers and patents CSO at Kontigo Care AB in Uppsala 2014

3 Kontigo Care AB Project started: Q , Prevas constructed the Breathalyzer TripleA CE-marked medical device: September 2015 First product - TripleA 1.0 launched for treatment of alcohol addiction 2 Clinical trials started: October 2015 An ISO certified ehealth company with 14 employees located in Uppsala Listed at Nasdaq First North: June 2015 Today two products: Previct Alcohol and Previct Gambling Customers: 78 Swedish municipalises use the system Lot of support from: Vinnova, Innovation Akademiska, UIC Winner of the ehealth Award 2018

4 Addictions 8 out of 10 lapse and/or relapse children in families where at least one parent is addicted Cost of Care 35 Billion SEK Total society costs ~ Billion SEK 1 month rehab ~ SEK ~6 month inhouse LVM ~ SEK Detox ~ SEK A child placed at family home ~3 000 SEK/day Social and psychological cost is even higher! A sober day with Previct 143 SEK

5 PREVICT HOW IT WORKS Andreas Zetterström The Previct system BI:SQL;Graphs; Math/Stat; AI JMP/Simca JMP Cloud Python, Keras, Tensorflow et al Digital biomarkers Nikola Katardjiev AI-based time-series modelling of data to predict a lapse/relapse - before it happens Frequent monitoring of behavioural patterns and QoL Smartphone Apps Web-based interface Visualisation and sorting of refined and simplified data to caregivers Fredrik Remaeus Objective monitoring of addictions with IoT Patient Healthcare professional Maria Winkvist Proactive measurement based care using high quality real-time data

6 PREVICT The ancient metrics for alcohol use disorder (AUD) - before ehealth Self-reporting with Questionnaires - Recall - Under/Over reporting - Time resolution - Objectivity/Honesty + Cheap Biomarkers - Time resolution + High Specificity - Personal Integrity

7 X-matrix for prediction of a future lapse PREDICTING LAPSE/RELAPSE Smartphone based remote therapy of AUD The physical measurement system(s) Breathalyzer data Pattern of missed/ignored BrAC-test Meta data: How is the system used GPS: Adherence to therapy meetings Addiction monitoring index (AMI) lapse = Y Questionnaires Mood et al. Tasks, routines and therapy Cravings button Therapy Cognitive Behavioural Therapy; CRA Motivational therapy 12-steps therapy Informed treatment (e.g. CBT,..) based on symptoms & QoL

8 Addiction monitoring index The digital biomarker AMI Addiction monitoring index (AMI) Goal Biomarker PEth Why AMI? No of ignored tests >> positive tests 1 Controlled drinking 2 Abstinence <0.05 A daily AMI-value Scale Abstinence <0.05 a b 4 Abstinence a b Treatment day PEth is a biomarker in blood

9 Weekday AMI AMI for overview Addiction Monitoring Index (AMI) A B C D E F G H 0.4 0? 0 Green <0.05 Blue =>0.05 <=0.2 Red >0.2 Black Excluded/missed test 1.3 PEth 4.1 AMI is sobriety index from Previct PEth is a biomarker in blood Treatment Day (no)

10 Weekday AMI AMI-valleys as Y =Lapse AMI enable remote identification of a lapse A short valley in AMI >= 40 is a Lapse Long time with AMI < 40 is a Relapse Lapse Recovery or relapse ongoing Treatment Day (no)

11 AMI (1.0 & 2.0) AMI 2.0 detect lapse Early identification of lapse 6 days before first statistically verified positive BAC-test (>0.05 ) 13 days before first generation of AMI PEth = 0 = do not detect the lapse * AMI 1.0 AMI 2.0 Previct more sensitive than PEth for lapse identification! Lapse Treatment Day (no)

12 Monitor behavioral change Behavioral changes Pre-Lapse Product manager redesigned AMI into: Colors and faces TripleTask App collects information on mood, routines, cravings, compliance A behavioral change comes often before a lapse Our BI/AI-system identifies a change and informs the caregiver Early intervention from caregiver can stop the lapse process Predict and Prevent -> Previct WEB-Portal for Caregivers Colors and symbols in a calendar

13 Dig.Bionark. Compliance Mood et al. AMI_0.2 BAC ( ) AMI define Lapse One year with ~10 AMI-defined lapses for patient X OK Baseline for X Mood alert for lapse Mood alert for secret drinking Dig.BioMark. (compl.) alert for lapse Sub-legal BAC identify secret drinking AMI defined lapse Lapse Recovery or relapse ongoing x x 8 out of 10 lapses pre-warned Treatment day

14 PREVICT Previct delivers knowledge to the addiction care Conventional care TripleA 1.0 TripleA TripleA 2.0 No knowledge of what really happened (between care visits) What did happen? When did it happen? Why did it happen? Warnings for increased risk to happen? What is going to happen? How to prevent that it happens?

15 Conclusions Conclusions Internet of things and ehealth enables real-time objective measurement based personalized care BI/AI-prediction open up for very early identification of lapse Prediction and Prevention are the key features of Previct - an ehealth platform for proactive treatment of addictions A similar strategy can be applied for all chronic diseases