Dahshu Virtual Journal Club

Transcription

1 Dahshu Virtual Journal Club DahShu is a nonprofit organization with the mission of promoting research and education in data science For more information, visit: Questions? Contact us at: dahshuinfo@gmail.com

2 Announcing September Topic Can data scientists find a cure for cancer? A data-driven approach to discover cancer therapeutics In Sept journal club, Dr. Chen will present the two recent papers published in Gastroenterology and Nature Communications to demonstrate this systems approach. Dr. Bin Chen Institute for Computational Health Sciences at University of California, San Francisco He will also share how a data scientist led the discovery of new therapeutic candidates for liver cancer.

3 Today s Speaker and Topic Dr. Wang currently leads the data analysis team in Digital Health Center at Mount Sinai. Today, she will present the work featured in: Dr. Pei Wang is an associate Professor of Genetics and Genomics in Icahn Medical School at Mount Sinai, New York. Yu-Feng Yvonne Chan, Pei Wang, Linda Rogers, Nicole Tignor, Nicholas Genes, et. al., The Asthma Mobile Health Study, a Large Scale Clinical Study Using ResearchKit, Nature Biotechnology, , (2017).

4 Mobile Health Apps Transforming Medical Research and Clinical Care: Using Apple s new ResearchKit for Asthma Mobile Health Study Pei Wang, PhD, Associate Professor Icahn Institute of Genomics and Multiscale biology, New

5 Outline Background: Apple Research Kit and Asthma Mobile App Asthma Mobile Health Study (3/ /2016). Chan, Wang and et. al. Nature Biotechnology, Identify asthma subgroups based on daily symptom profiles. Tignor, Wang and et. al. Pac. Sym. Biocomputing, 2016 Benefits and challenges in Mobile health study. 5

6 Apple Researchkit and Asthma Mobile Health Study

7 Apple Launched ResearchKit on March 9, 2015 ResearchKit is an open source software framework that makes it easy to create apps for medical research or other research project. Jeff Williams Apple s CEO Sage Bionetworks Stephen Friend

8 Background: Asthma is particularly amenable through mobile health application Asthma is one of the most common and costly of the chronic diseases, impacting a broad range of the population including both children and adults. It is a variable disease necessitating regular medication use, monitoring of symptoms, and avoidance of specific triggers. These characteristics of asthma make it a chronic disease that is particularly amenable to having an MHA facilitate active monitoring outside of periodic traditional medical visits.

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10 Asthma Health App Track Day/night symptoms Daily usage of controller/inhalers Triggers Peak flow Emergency visit, medical visit et.al. Physical activity Reminders and notifications Medications Local air quality Educational information Doctor Dashboard

11 Media: anyone with an iphone can help medical research

12 Media: A new Era in Medical Research

13 Results from Asthma Mobile Health Study

14 Key Achievements Demonstrated the feasibility of conducting a study using an app without direct participant contact throughout the entire process. M-based research platforms (M-health) are still in their infancy but hold tremendous potential and are poised to become a primary driver for change in biomedical research and healthcare.

15 Downloads & Enrollments between 3/9 9/9 Chan et. al. Nature Biotechnology, 2017

16 Downloads & Enrollments between 3/9 9/9 Chan et. al. Nature Biotechnology, 2017

17 7593 participants ~100K Survey Results + Real time Location/activity Rivals the size of CDC (centers for disease control) s 2013 National Asthma Survey Collects data at a fraction of the cost More comprehensive information on conditions and activities the local air quality index and steps taken while spending the day at the park enables a 360 degree view and a more holistic examination of enrolled patients.

18 Baseline Characteristics of Asthma Users Majority of our Asthma Health App Users are o Young (<35) o Male o Advanced education status o Income > $60k per year Income App Percentage <$14,999 6% $15,000-21,999 5% $22,000-43,999 14% $44,000-60,000 13% >$60,000 55% I don't know 6% Income CDC STATS (2013) <$ % $15,000-24,999 17% $25,000-49,999 20% $50,000-79,999 11% >=$75,000 21% App Percentage CDC STATS (2013) Age % 36% % 48% 65+ 5% 16% Race Black 5% 13.64% White 68% 63.96% Other 8% 5.08% Multirace 5% 2.26% Hispanic 15% 12.99% Educati on HS Nongrad 2% 16.16% HS Grad 9% 27.78% Some College 37% 33.07% College Grad 52% 22.60% Gender F 39% 59% M 61% 41% Clinical Visited ER 11% 8% Hospitalized 6% 2% CDC: Centers for Disease Control and Prevention Chan et. al. Nature Biotechnology, 2017

19 Participants are from all around the U.S. AMH subjects have similar geographic distribution as reported in National Asthma Survey by CDC in Chan et. al. Nature Biotechnology, 2017

20 We are able to reach patients with severe Asthma 64% of participants have persistent asthma (as defined by taking daily controller medication). 12% of our users are experiencing asthma symptoms, every day. 13% have been intubated. 6% have been hospitalized in the prior 6 months. CDC: 2% 11% have visited Emergency Room in the last 6 months. CDC: 8% Chan et. al. Nature Biotechnology, 2017

21 Consistent trending and correlation of interrelated variables support the quality of self-reported data The peak flow measurements submitted by participants were of expected range based on known trends for patients sex, height, and asthma control status. Patients daily survey responses were all found to be significantly associated with the GINA control levels at enrollment. Chan et. al. Nature Biotechnology, 2017

22 For the first time, we were able to characterize the impact of air quality on Asthma patients. A marked increase of participants reporting air quality triggers in regions impacted by the summer 2015 Washington state wildfires during the corresponding time periods Chan et. al. Nature Biotechnology, 2017

23 Improvement of activity limitation were observed. Consider users enrolled for more than 90 days. A significant decrease of activity limitation from 25% in the first week to 20% in the last week of participation (Wilcoxon signed-rank test, p<0.0001, n=1926). The same trend persisted during the 3 summer months (Wilcoxon signed-rank test, p<0.0001, n=331) suggesting that milder weather did not drive changes in activity levels. Chan et. al. Nature Biotechnology, 2017

24 Positive feedback from 175 Milestone users Asthma control significantly improved over the study period (Paired Wilcoxon signedrank test, p<0.0001). A modest majority of participants reported that the AHA was useful in helping them manage their asthma. Chan et. al. Nature Biotechnology, 2017

25 Time-course daily symptom profiles help to identify unique subtypes. Asthma is a highly variable and heterogeneous disease, and it has therefore been difficult to characterize patient disease subtypes precisely enough to inform an optimal individualized treatment plan. Our Asthma Health App, for the first time, enables one to collect rich time series data on asthma patients activities on a daily basis. This opens up the possibility to identify at-risk subgroups of patients based on high-resolution time-course symptom data. By studying time-course daily symptom profiles, we identified a unique subgroup of patients who have relatively high symptom rates and are more sensitive to distinct environmental factors with seasonal changes, such as heat and pollen.

26 Identify asthma subgroups based on daily symptom profiles N. Tignor, P. Wang, N. Genes, L. Rogers, S. G. Hershman, E. R. Scott, M Zweig, Y. Chan, E. Schadt, Methods for clustering time series data acquired from mobile health apps, Pac Symp Biocomputing 2016; 22:

27 Challenges: Substantial missing which are not at random Users indicated that they were more likely to respond on symptomatic days

28 PIC: Probability based imputation

29 PIC: Probability based imputation and consensus clustering o Smooth the symptom profile: it makes more sense to define distance based on their symptom frequencies over a time window instead of based on events on individual days. o Integrate multiple imputation with consensus clustering.

30 PIC performs better than methods ignoring NMAR in simulation studies Tignor et. al. PSB, 2016

31 Time-course daily symptom profiles help to identify unique subtypes. By studying time-course daily symptom profiles, we identified a unique subgroup of patients (cluster 1) who have relatively high symptom rates. are more sensitive to distinct environmental factors with seasonal changes, such as heat and pollen. Tignor et. al. PSB, 2016

32 Benefits and challenges of Conducting Medical Research on Mobile Devices

33 Benefit 1: Participants are in charge Paradigm shift from traditional model of investigators owning the data Can choose to participate in the study & complete certain study activities whenever & wherever they want

34 Benefit 2: Objective Assessments Traditional: episodic assessments generally at research centers Passive monitoring & activities that can be completed at any time -> high frequency of assessments such as the local air quality index and steps taken while spending the day at the park. Objective measurements of symptoms, conditions, performance, or environmental variables etc.

35 Benefit 3: Real-time Monitoring & Feedback Report back data of interest and help the user monitor their condition Provides a powerful platform for studying ways to motivate healthy behaviors via real-time interaction on daily decisions about diet, medications, and physical activity

36 Challenges Reduce selection bias: Future efforts are aimed at including international participants and operability on a broader platform of devices. Integrate with other wearable devices: Develop new tools for monitoring a broad range of physiological parameters and connecting to broader data sources. Need to improve user s retention: Many smartphone applications have enjoyed robust initial interest that rapidly fades with time. Important to control privacy risks: The ability to protect the anonymity of an individual s data will be a critical factor in the success of this and similar large-scale research efforts New data analysis methods/tools: Immense volume of data; Extensive missing values and non-random missing pattern.

37 Conclusion We demonstrated the feasibility of conducting a study using an app without direct participant contact throughout the entire process. M-based research platforms (M-health) are still in their infancy but hold tremendous potential and are poised to become a primary driver for change in biomedical research and healthcare.

38 Acknowledgements Yvonne Chan, MD, PhD, FACEP, Director of Digital Health Center Eric Schadt, PhD, Professor and Chair Genetics and Genomics Sciences Nicol Tignor, PhD, Senior Data Scientist Linda Rogers, MD, Clinical Director Asthma Program Nicholas Genes, MD, PhD, Micol Zweig, Project Manager Weiping Ma, Sam Violante, Steve Hershman, Erick Scott.