machine learning and predictive models in hepatitis c · individualize treatment. division of...
TRANSCRIPT
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Machine Learning and Predictive Models in Hepatitis C
Akbar K. Waljee, MD, MscAssociate Professor of Internal MedicineUniversity of Michigan Health Systems
Ann Arbor VA Center of Excellence
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Goals of the Talk
• Introduction to a Clinical Problem
• How can Machine Learning help?
• Examples in HCV:
– HALT-C/Michigan Medicine Cohorts
– VA Cohort
• Implications for Medicaid patients
Division of Gastroenterology & Hepatology
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Division of Gastroenterology & Hepatology
Mr. S
• 64 yo male
• HCV RNA+
• Received blood transfusion in 1978
• Early stage cirrhosis
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Division of Gastroenterology & Hepatology
Ms. J
• 23 yo female
• HCV RNA+
• Active IVDU
• Recently acquired HCV
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Division of Gastroenterology & Hepatology
Do you treat them the same?
Individualize Treatment
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Division of Gastroenterology & Hepatology
What is Machine Learning?
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Division of Gastroenterology & Hepatology
Real World Applications
• Large digital datasets
• Known outcomes
• Identify patterns in data to predict the future
– Clicking on an ad
– Purchasing items
– Signing up for a credit card
– Switching cell phone providers
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Can you differentiate? Cat vs. Dog
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Time Spent Digging Holes
Tim
e Sp
ent
Sitt
ing
in L
aps cats
dogs
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3 Variables = 3 Dimensions
Lap
Sit
tin
g
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What is Random Forest?
• A modern Machine Learning method
• Computer-based algorithm which uses decision trees to classify outcomes. e.g. Cat or Dog
• Can incorporate many variables and interactions– Identifies most important variables for prediction
– “No pre-conceived notions”
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What is Random Forest?
Dataset
Tree #1
DOG
Tree #2
CAT
Tree #3
DOG
MAJORITY VOTING (i.e. FOREST) = DOG
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Division of Gastroenterology & Hepatology
How can we improve care for an individual?
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Division of Gastroenterology & Hepatology
Example:Liver-Related Clinical Outcomes
Evaluating liver-related clinical outcomes in Hepatitis C
Konerman, et al. PLOS One 2017
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Division of Gastroenterology & Hepatology
Topical Research Questions of Interest
1. Risk Stratification and Prognosis 2. Interventions to Decrease Risk of Adverse Outcomes
Population of Interest
Risk Factors
modifiable
Rigid
Adverse Clinical
Outcome
No Clinical
Outcome
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Division of Gastroenterology & Hepatology
Predictive Models for Risk of Clinical Outcomes
• Methods:
• Develop Longitudinal Models on HALT-C for a composite clinical outcome
• Validate 1007 HCV patients at Michigan Medicine
• Predict outcomes at 1 and 3 years
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Division of Gastroenterology & Hepatology
Patient Demographics
Variable Summary statistics
HALT-C Cohort Michigan Medicine Cohort (N=1050) (N=1007)
Age (median, IQR) 49 (46-54) 49.4 (44.3-54.3)
Male 745 (71%) 612 (61%)
Race (% White) 752 (71.6%) 636 (80.1%)
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Division of Gastroenterology & Hepatology
Approach
Data Point
Outcome
No Outcome
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Division of Gastroenterology & Hepatology
Approach
Baseline
Data summarized to predict 1 year outcome
1 year Prediction Window
Time of Risk Prediction
Longitudinal Follow-UpData Point
Outcome
No Outcome
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Division of Gastroenterology & Hepatology
Approach
Baseline
Data summarized to predict 1 year outcome
1 year Prediction Window
Time of Risk Prediction
3 year Prediction Window
Data summarized to predict 3 year outcome
Time of Risk Prediction
Longitudinal Follow-UpData Point
Outcome
No Outcome
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Division of Gastroenterology & Hepatology
Results
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Division of Gastroenterology & Hepatology
Variable Importance
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Division of Gastroenterology & Hepatology
Example:Progression of Disease
Evaluating progression to Cirrhosis in Veterans
Konerman, et al. PLOS One 2019
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Division of Gastroenterology & Hepatology
HCV Models in VA Data
250,000 Veteransin 2016
3 Million 170Million
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Division of Gastroenterology & Hepatology
Progression
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Division of Gastroenterology & Hepatology
Cohort
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Division of Gastroenterology & Hepatology
Patient Demographics
• Large heterogeneous population, primarily male
Variable Summary statistics
Age (mean, sd) 52.84 (8.74)
Male 70,377 (96.8%)
Race (% White) 35,216 (52.9%)
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Division of Gastroenterology & Hepatology
Approach
Baseline
Leverage longitudinaldata
Prediction Window
Time of Risk Prediction
Longitudinal Follow-Up
Data Point
Outcome
No Outcome
Time-to-event
Person 1:Cirrhosis
Person 2:Censored
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Division of Gastroenterology & Hepatology
Results
Time N % Events Model AuROC SN SP PPV NPV
1 year 18896 0.036 CS Cox 0.807 0.79 0.71 0.11 0.99
CS Boosting 0.817 0.77 0.73 0.11 0.99
LGT Cox 0.828 0.75 0.76 0.10 0.99
LGT Boosting 0.838 0.76 0.77 0.11 0.99
3 years 14605 0.112 CS Cox 0.784 0.73 0.72 0.25 0.95
CS Boosting 0.799 0.76 0.71 0.27 0.95
LGT Cox 0.804 0.75 0.74 0.27 0.96
LGT Boosting 0.815 0.76 0.73 0.28 0.96
5 years 11334 0.206 CS Cox 0.775 0.74 0.70 0.41 0.90
CS Boosting 0.790 0.75 0.70 0.42 0.91
LGT Cox 0.794 0.75 0.71 0.42 0.91
LGT Boosting 0.805 0.73 0.74 0.41 0.92
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Division of Gastroenterology & Hepatology
HCV Treatment Approaches
Population Health Approach
• Population Level
• Untargeted
• Only need high level patient data
• Quickly scalable (available data infrastructure?)
• Economical in resource limited setting?
Precision Health Approach
• Patient Level
• Targeted
• Need granular/sparse patient data
• Scalable but need to build data infrastructure
• Optimizes targeted health and value for patients and payers
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Division of Gastroenterology & Hepatology
Value of Precision Health
Targeted Treatment
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Division of Gastroenterology & Hepatology
Value of Precision Health
Potential alternative treatment approaches in HCV Medicaid patients:
• Limited Resources
» Navigation for those not seeking care
» IVDU
• High-Risk HCV Targets
» Non-adherence, more intensive monitoring
» Reinfection
• Post Treatment Monitoring
» Improve treatment transition
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Division of Gastroenterology & Hepatology
MDHHS and University of Michigan
The Collaboration
David Neff, DOChief Medical Director,
MDHHS
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K-TOP
K-GRID is the “K” Component
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K-TOP
MTOP is the “P” Component
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K-TOP
Proposed Focus Areas to Develop Use Cases
1. Opioids
2. Hepatitis C
3. Rare Disease Registries (ie, spinal muscular atrophy, aka SMA)
4. Social Determinants and Adverse Childhood Experiences (ACE’s)
5. Superutilizors
6. Diabetes
7. Heart Disease
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Division of Gastroenterology & Hepatology
Thoughts for the group:
• What barriers do you foresee with these approaches?– Data Access
– How to deliver care to those not seeking care
– Getting Prediction models into practice
– What do patients think of treatment policies
• What alternatives should we be considering?
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THANK YOU