Case Study for Clinical Relevancy: Asthma

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Case Study for Clinical Relevancy Asthma
Scott T. Weiss, M.D., M.S.
Professor of Medicine Harvard Medical
School Director, Center for Genomic
Medicine Director, Program in Bioinformatics Assoc
iate Director, Channing Laboratory Brigham and
Womens Hospital Boston, MA
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Outline

• Context focus on process and data
• Overview of Asthma DBP
• Smoking as an example of the data issues
• Predicting COPD in those with asthma
• Predicting asthma exacerbations
• Genetic prediction of asthma exacerbations
  current status
• DNA collection
• Lessons Learned
• Conclusions

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Context

• Channing Lab - extensive genetics
  pharmacogenetics resources focused on airways
  diseases
• Faculty with clinical, epidemiology, genetic, and
  bioinformatics training and experience
• multidisciplinary research collaborative track
  record
• Good i2b2 driver from bench to clinic
• Strong focus and direction for Cores

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Broad Goals of Channing Program in Predictive
Medicine

• Genetic variation ? clinical practice
• ? Disease risk (asthma diagnosis)
• ? Natural history (exacerbations)
• ? Individual response to medication
  (pharmacogenetics)
• Develop predictive tests (genetic and nongenetic)
  in Channing populations
• Validate these tests in Partners asthma cohort
  (PAC) at least as proof of concept

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I2B2 Airways DBP Overview
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Before we start

• Numerous important covariates
• e.g. age, tobacco, comorbidities, medications
• Adjust outcomes for covariates
• Some (eg age, gender,Dx, encounter) readily
  available
• Obtained through Core 4
• Others require substantial effort e.g.
  medications, tobacco use, comorbid conditions
• Collaboration - NLP experts in Core 1

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Phenotypes from text

• Extract specific data items
• Medication
• Smoking status
• Diagnoses (Co-morbidity)
• Extract findings to assist with case selection
• Extract findings to assist with clinical
  predictions

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Smoking Status- Examples
Smoker
Non-Smoker
Past Smoker
???
Hard to pick
Hard to pick
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Smoking -Text Processing
Manually classified
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Smoking Status
Preliminary results

• Raw sample 20,000 reports
• Feature extraction gt3000
• Feature selection 25 - 1000
• Gold standard sample cases 2,800
• Correct classification rate 46 - 81(compared to
  Gold Standard)

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Smoking Status
Preliminary results
Baseline performance
Increase, combine features should improve
performance
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Data Extraction
Data Mining Pipeline
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Asthma Preceding COPD

• Significant overlap of asthma and COPD DX
• Common denominator smoking
• Asthma is known to precede and predict the
  development of COPD independent of smoking
• Could we develop a multivariate clinical
  predictor that would predict which asthmatics
  would get COPD?

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Study Design

• Source Partners Healthcare Research Patient
  Data Repository (RPDR).
• RPDR MGH, BWH, etc clinical repository for
  researchers.
• Training 9349 asthmatics (843 COPD, 8506
  controls) first encounter 1988 1998.
• Test A future set of 992 asthmatics (46 COPD,
  946 controls) first encounter from 1999-2002.

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Data Collection

• Criteria Patients observed for at least 5
  years, at least 18 at the first encouter, and
  race, sex, height, weight, and smoking available.
  
• Comorbodities International Classification of
  Diseases, 9th Revision (ICD-9) codes as admission
  diagnosis or ER primary diagnosis (104)
• COPD ICD-9 code for Chronic Bronchitis,
  Emphysema Chronic Airways Obstruction, not
  otherwise specified.

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Analysis

• Model A Bayesian network was generated from the
  training set of 9349 asthmatics (843 COPD, 8506
  controls) encountered between1988 and 1998 from
  104 comoribities and race, gender, age, smoking.
• Results The risk of COPD is modulated by
  gender, race, and smoking history, and 14
  comorbidities Viral and chlamydial infections,
  diabetes mellitus, volume depletion, acute
  myocardial infarction, intermediate coronary
  syndrome, cardiac dysrhythmias, heart failure,
  acute upper respiratory infections, acute
  bronchitis and bronchiolitis, pneumonia, early or
  threatened labor, normal delivery, shortness of
  breath, respiratory distress.

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Network Model
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Validation

• Propagation a Bayesian network can compute the
  probability distribution of any variable given an
  instance of some or all the other variables.
• Test data a future set of 992 asthmatics (46
  COPD, 946 controls) first encounter from
  1999-2002.
• Prediction for each patient, predict the
  probability of COPD given the other elements in
  the network (co-morbidities and demographics).
• Validation compare the predicted with the
  observed COPD status.

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Predictive Validation
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One variable at the time
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Asthma Exacerbations

• Asthma attacks involve worsening of asthma
  symptoms including bronchoconstriction and
  inflammatory response
• Major cause of morbidity and mortality in asthma
• 11.7 million Americans have an exacerbation every
  year (3.9 million children)
• In US children, exacerbations are the third
  leading cause of hospitalizations (198,000
  occurrences per year)
• Cost of asthma exacerbations US4 billion
  dollars, Partners20 million dollars

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RPDR Exacerbation Prediction

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Genetic Prediction of Asthma Exacerbation

• Objective
• Predict asthma exacerbation from genetic data
• Subjects
• 290 CAMP participants
• Not on steroids
• Followed for 10 years
• Have genetic data available
• Phenotype
• Case Reported overnight hospitalization(s)
  (n83)
• Control No overnight hospitalizations or ER
  visits (n207)
• Genotype
• 2443 SNPs from 349 candidate genes
• In Hardy-Weinberg equilibrium among controls
• Minor allele frequency gt 0.05

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Exacerbation Model
132 of 2443 SNPs in 55 of 349 genes predict
exacerbation
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Validation

• Method Prediction on fitted values
• Result Area under the ROC curve (AUROC) is 0.97

• AUROC measures accuracy as trade-off between
  sensitivity and specificity

AUROC Rating
0.5 - 0.6 Fail
0.6 - 0.7 Poor
0.7 - 0.8 Fair
0.8 - 0.9 Good
0.9 - 1.0 Excellent
AUROC 0.97
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Cross-Validation

• Method 20-fold cross-validation to test
  robustness
• Data is split into 20 groups
• One group is used as independent and remaining 19
  are used to quantify the model
• (2) is repeated until each group has been
  independent set
• Result AUROC is 0.84 (good)

AUROC 0.84
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Partners Asthma DNA collection 1

• Recruit Partners asthma patients
• Partners Asthma Center, NWH, MGH
• High quality spirometric phenotyping
• Blood for DNA extraction and storage
• Children and adults
• High cost (gt1000/subject)
• Low intensity 6 months only 100 subjects
  recruited
• Doctors and patients need education

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Partners Asthma DNA collection 2

• Recruit Partners asthma cohort patients
• Leverage CRIMSON blood samples
• Leverage data mart for phenotype data
• Blood for DNA extraction and storage
• Children and adults cases and controls
• low cost (lt30/subject)
• High intensity 9 months gt3000 subjects recruited

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Figure 1 Data Flow for Asthma DBP
Channing RPDR ADMPN Send to RPD
converts ADMPN to
MRN sends to pathology
Pathology (Crimson) MRN Crimson
ID ADMPN sends back to Channing with
sample for DNA extraction
Figure 1 Legend Deidentified data file analyzed
by Channing subjects for DNA collection selected.
File sent to RPDR converted back to MR and sent
to Crimson. Samples identified and given Crimson
ID ADMPN and sample Sent back to Channing.
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Recruitment for DBP from Crimson at BWH Asthma
Cases by Utilization and Race
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Recruitment for DBP from Crimson at BWH Asthma
Cases and Controls by Race
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Summary of Samples to 04/07/08
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Lessons learned 1

• Get what you ask for
• Regular meetings, regular meetings
• Negotiate your demands
• Tools are not enough
• Leverage your peers
• Recruiting patients is hard work
• IRB is hard work

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Lessons learned 2

• You can never have enough statistics or
  bioinformatics
• Genotyping and its technologies are secondary
• The RPDR data are dirty!
• Listen to Shawn
• Be flexible

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Summary Airways disease as a driver for i2b2

• Typical complex disease challenge
• Big impact on health care system
• Potential for large clinical impact
• Core 1 Extracting phenotypes from free text
  statistical models
• Core 2 Viewer for CRC
• Core 4 Data provisioning

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Conclusions

• The stronger the existing program, the more
  successful the I2B2 collaboration
• Communication is key
• Fit the question to the data not the other way
  around
• Data access will be an issue for the future

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Collaborators (and what they did)

• Scott, Zak, John, and Susanne money, project
  management, IRB, and big picture
• Ross Channing bioinformatics, file structures,
  geek to geek translation with the cores, beta
  testing, 850 collection, IRB, links to other
  genetic bioinformatics tools and projects
• Shawn and Vivian asthma and control data mart
• Anne, LJ, James nongenetic predictors in CAMP
• Marco and Blanca nongenetic predictors in PAC
• Marco and Blanca genetic predictors in CAMP
• Marco and Blanca genetic predictors in PAC
• Lynn Crimson

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Acknowledgments

• Ross Lazarus Susanne Churchill
• Blanca E. Himes Anne Fuhlbrigge
• Marco F. Ramoni LJ Wei
• Isaac Kohane James Sigornivitch
• Shawn Murphy Lynn Bry