Open Source Software Solutions for Clinical Research: Applications for HIV Research

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AIDS CLINICAL ROUNDS

Open Source Software Solutions for Clinical Research: Applications for HIV Research

Jason A. Young, Ph.DAssistant ProfessorDepartment of Medicine, UCSD

AIDS Clinical Rounds - 8.3.12

UCSD CFAR BIT CoreBioinformatics and Information Technologies (BIT) Core

Aims• Provide bio/informatics expertise• To be agile, interactive, affordable• Committed to open source

Resources • The BIT Core team!• 24/7, secure web & data servers• 500+ node compute cluster• A collection of open source software solutions and services

Clients• Center For AIDS Research (CFAR)• AntiViral Research Center (AVRC)• AIDS Research Institute (ARI)

• IAVI Neutralizing Antibody Consortium (IAVI-NAC)• California Collaborative Treatment Group (CCTG)• ... other research investigators and growing ...

Website: https://cfar.ucsd.edu/bitE-mail: [email protected]: http://github.com/beastcore

https://cfar.ucsd.edu/bit

https://cfar.ucsd.edu/bit

mailto:[email protected]


http://github.com/beastcore


Outline

1. Web and Mobile Research Services

2. Clinical Data Management

3. Bioinformatics Expertise

Web and Mobile Research Services

Accessible:Non-technical users can create websites and maintain content using only a web browser. Comes with built-in workflows, permissions, etc.

Widely deployed:Broad user base includes NASA, Nokia, Novell, and major universities (Harvard, MIT and Penn State).

Large development and support base:340 core developers and >300 solution providers in 57 countries.

Mature:First released in 2001. Provides developers a robust framework for custom product development.

Secure:Best security record of any major CMS.

Extensible:Over 1900 projects extending core functionality currently available.

“A powerful, flexible open source Content Management System”

Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com

Doug Richman (UCSD)


Doug Richman (UCSD)

ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com

Doug Richman (UCSD)


• Core Service Request Forms• Feedback Forms• Developmental Grant Submission System• Laboratory Experiment Tracking System• Retroviral Seminar Series Calendar

Doug Richman (UCSD)


Constance Benson (UCSD)



• Clinical trial information• Events calendar• AIDS rounds presentation slides*


Constance Benson (UCSD)


UCSD UCSF MGH

AIEDRP AIEDRP

AIEDRP

AIEDRP

AIEDRP

AIEDRP AIEDRP

HIVe: HIV e-resource (hive.ucsd.edu)• Acute and Early HIV (AEH) cohort network• Data standardization and sharing• 3 active & 7 legacy AEH sites



UCSD UCSF MGH

AIEDRP AIEDRP

AIEDRP

AIEDRP

AIEDRP

AIEDRP AIEDRP

ucsd.hive.ucsd.edu

HIVe: HIV e-resource (hive.ucsd.edu)• Acute and Early HIV (AEH) cohort network• Data standardization and sharing• 3 active & 7 legacy AEH sites

mgh.hive.ucsd.edu

ucsf.hive.ucsd.edu

hive.ucsd.edu


Web and Mobile Research ServicesSan Diego Primary Infection Cohort (SDPIC)(1996 - Present)• 2 Screening Programs (~12k screens)• 7 Research Studies (~2.5k enrollments)• Data: Demographics, risk factors, partner information, labs, viral sequences, and much more...• Specimen: Over 200k

UCSD UCSF MGH

AIEDRP AIEDRP

AIEDRP

AIEDRP

AIEDRP

AIEDRP AIEDRP

Susan Little (UCSD)

ucsd.hive.ucsd.edu


Susan Little & Davey Smith (UCSD)


Susan Little & Davey Smith (UCSD)


• AEH screening program (Rapid/NAT)• Obtain NAT results online or over the phone in two weeks


Susan Little (UCSD)


Susan Little (UCSD)


• Hyper-local (92103/92104) HIV testing campaign (Rapid/NAT)• Public media advertising campaign • Storefront & door-to-door testing• Goal: What are the barriers to testing?


Susan Little (UCSD)


Susan Little (UCSD)


• Hyper-local (92103/92104) HIV testing campaign (Rapid/NAT)• Public media advertising campaign • Storefront & door-to-door testing• Aim: What are the barriers to HIV testing?


Web and Mobile Research ServicesiFormBuilderiOS Mobile Data

Collection Platform


Collection PlatformFunctionality • Runs on all iOS devices• 25+ field widgets• Flexible skip logic• GPS functionality• HIPAA compliant• Encrypted data upload to cloud


Collection Platform

One year for LTW...• 1317 iPad administered surveys• 1062 individuals tested for HIV• 24 newly diagnosed HIV(+) cases

Functionality • Runs on all iOS devices• 25+ field widgets• Flexible skip logic• GPS functionality• HIPAA compliant• Encrypted data upload to cloud

Outline




Open source Clinical Content Analysis and Management SystemOCCAMS: Designed to handle all aspects of complex and evolving clinical research studies

Clinical Data Management

William of Ockham (1288-1347)

Open source Clinical Content Analysis and Management System

April 2010

OCCAMS development begins

~15 years

Numerous data managementsolutions and providers

OCCAMS: Designed to handle all aspects of complex and evolving clinical research studies

June 1996

SDPIC InfectionCohort Begins




April 2010


~15 years



1. Web-accessible (eCRFs)2. Patient centric (no data duplication)3. Broad data support4. Integrated specimen handling5. QA workflows and auditing reports6. PHI-compliant with granular permissions7. Modular, flexible and extensible8. Open source (Plone/Python-powered)

June 1996





April 2010


Present

+2.5k AEH enrollments+12k AEH screens

~15 years



1. Web-accessible (eCRFs)2. Patient centric (no data duplication)3. Broad data support4. Integrated specimen handling5. QA workflows and auditing reports6. PHI-compliant with granular permissions7. Modular, flexible and extensible8. Open source (Plone/Python-powered)

June 1996


September 2010

Alpha version launched for SDPIC



SDPIC pre-OCCAMS workflow...1. Nurse sees patient, completes

source docs

2. Nurse completes case report form (CRF)

3. CRF and source documentation consistency checked by AVRC staff

4. CRF entered into database by students


Challenges


1. Rooms full of paper and binders.2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete.3. Several opportunities for transcription errors.4. No QC after students entered CRF to database.5. Data duplication in cases where patients on multiple studies.6. CRF changes not automatically reflected in database.7. Arduous auditing process.

SDPIC pre-OCCAMS workflow...

SDPIC post-OCCAMS workflow...1. Nurse sees patient, completes

source docs

2. Nurse direct enters data via eCRF that is automatically generated based

on study and visit week

3. Workflow notifies AVRC staff eCRF ready for QC

4. High volume eCRFs entered by students


Challenges


1. Rooms full of paper and binders.2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete.3. Several opportunities for transcription errors.4. No QC after students entered CRF to database.5. Data duplication in cases where patients on multiple studies.6. CRF changes not automatically reflected in database.7. Arduous auditing process.


Challenges1. Rooms full of paper and binders.2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete.3. Several opportunities for transcription errors.4. No QC after students entered CRF to database.5. Data duplication in cases where patients on multiple studies.6. CRF changes not automatically reflected in database.7. Arduous auditing process.


occams.clinical

occams.import

occams.export

occams.datastore

occams.form

Core

OCCAMS Modular Development

• EAV Database Structure~60 SQL tables total

• Robust versioning supportData versioning (audit trail)Form versioning (revision history)


occams.clinical

occams.import

occams.export

occams.datastore

occams.form

Core


Clinical Data ManagementForm Versioning with OCCAMS

Version 11/2010 - ...

A = B = C =


Version 11/2010 - ...

Version 27/2010 - ...

A = B = C =

A = B = D =


Version 11/2010 - ...

Version 27/2010 - ...

A = B = C =

A = B = D =

A = B = E =

Version 310/2010 - ...


Version 11/2010 - ...

A = B = C =

A = B = D =

A = B = E =

Version 310/2010 - ...

Version 27/2010 - ...


Which Form to Use?Example. Visit on 8/2010

Version 11/2010 - ...

Version 27/2010 - Retract

A = B = C =

A = B = D =

A = B = E =

Version 310/2010 - ...


Which Form to Use?Example. Visit on 8/2010

Version 11/2010 - ...

Version 27/2010 - Retract

A = B = C =

A = B = D =

A = B = E =

Version 310/2010 - ...

• Multiple versions of a form can exist simultaneously• The correct form for a visit date is auto-presented • Draft forms can be created and edited concurrently



occams.clinical

occams.import

occams.export

occams.datastore

occams.form

occams.lab

occams.sequence

occams.symptom

occams.drug

occams.partner

occams.edi

occams.transmission

Core Add-ons



occams.clinical

occams.import

occams.export

occams.datastore

occams.form

occams.lab

occams.sequence

occams.symptom

occams.drug

occams.partner

occams.edi

occams.transmission

Core Add-ons


• Currently undergoing finalization of remaining core features and testing• Public Beta release aimed for first half of 2013

Outline




Bioinformatics ExpertiseBioinformatics Expertise

HyPhy (hyphy.org)A molecular evolution and statistical sequence analysis software package• Positive/Negative selection detection• Recombination analysis• Nucleotide, protein and codon model selectionSome of the most popular functions are implemented in a webserver hosted at datamonkey.org

Galaxy (galaxy.psu.edu)A web-based, scalable, framework for genomic tools, data integration, and reproducible analyses.• Filter sequences obtained from public databases by specific traits, i.e. find exons with the greatest number of SNPs.• Deep sequencing analysis tools (read mapping, chip-SEQ, metagenomic taxonomic breakdowns).

Custom Bioinformatics ServicesExamples... • Sequence analysis (traditional and NGS) • Network analysis• Machine Learning

http://www.datamonkey.org

http://www.datamonkey.org

Bioinformatics ExpertiseNetwork Analysis

AEHStudy

PartnerStudy

ETNAT/Rapid

Testing

LTWNAT/Rapid

Testing

ScreeningPrograms

ObservationalStudies

Partner Counseling &

Referral Services(PCRS)

AEHInfection

NAT(+)/Rapid(-)<70 EDI

Example: SDPIC Transmission Network

Bioinformatics Expertise

Phylogenetic LinkGenetic distance between

HIV pol sequences isolated from any two individuals

is <= 1%

Epidemological LinkPartner Counseling and Referral

Services (PCRS) results in index to partner linkage being identified

(both persons enrolled on study)

SDPIC Transmission Network




is <= 1%




AEHAEHHIV (-) Chronic

AEH Study Partner Study

“Epilinks”








is <= 1%






AEHAEHHIV (-) Chronic

Partner Study



is <= 1%

“Phylolinks”

AEH Study



1. How effective is PCRS in an AEH setting?2. What is the structure of the SDPIC transmission network?3. Do HIV(+) reported partners represent likely transmission links?



is <= 1%





Bioinformatics Expertise1. How effective is PCRS in an AEH setting?

Number Needed To Interview Previous PCRS studies report...NNTI: 11-15A single AEH PCRS study reports...NNTI: 25 (25/1)SDPIC...NNTI: 5.9

Sheldon Morris & Susan Little

Bioinformatics Expertise2. What is the structure of the SDPIC transmission network?

Scale-free network structure• Highly-connected nodes critical



Bioinformatics Expertise3. Do HIV(+) reported partners represent actual transmission links?

Only ~34% of seroconcordant epi-linked pairs are also phylo-linked


Scale-free network structure• Highly-connected nodes critical


Bioinformatics ExpertiseMachine Learning

Lance Hepler & IAVI NAC

Example: HIV bnAb epitope and bnAb resistance prediction

Bioinformatics ExpertiseMachine Learning

IDEPI: IDentify EPItopesA pipeline for predicting HIV-1 bnAb epitopes from bnAb neutralization titers matched with gp160 sequences.

CARTAS: ComputAional Real Time Antibody SurveillanceIDEPI extended to predict HIV resistance to bnAb using gp160 sequencesInput:

IDEPI inferred predictive model based on neutralization titers23.5k HIV-1 group M gp160 sequences from Los Alamos HIV-1 database

Lance Hepler & IAVI NAC

Example: HIV bnAb epitope and bnAb resistance prediction


2F5

HIV bnAb epitope and bnAb resistance prediction

Learn More: http://cfar.ucsd.edu/research/croi

http://cfar.ucsd.edu/research/croi



B12






2F5 + B12






2F5 + B12

Near real-time surveillance





BIT CoreSergei PondDave MoteMarco MartinezJennifer Rodriguez-MuellerSteve WeaverKonrad SchefflerJoel WertheimLance HeplerMartin Smith

AVRCSusan LittleSheldon MorrisRichard HaubrichConnie BensonDavey SmithSanjay Mehta... and all the other superheros ...

CFARDoug RichmanKim SchafferBryna Block

Acknowledgements

Website: http://cfar.ucsd.edu/bitTwitter: @ucsdbitEmail: [email protected]: http://github.com/beastcore

IAVI NACPascal Poignard

http://cfar.ucsd.edu/beaste

http://cfar.ucsd.edu/beaste





Open Source Software Solutions for Clinical Research: Applications for HIV Research

Health & Medicine

Transcript of Open Source Software Solutions for Clinical Research: Applications for HIV Research