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Modeling Pre-clinical Diagnostics Test Using System Dynamics Leeza Osipenko PhD Colloquium 22 nd System Dynamics Conference Oxford, UK July 25, 2004. Outline. Introduction Research Topic Pre-clinical Diagnostics ELIP test Previous Work in the Field Research Methodology

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Modeling Pre-clinical Diagnostics Test Using System DynamicsLeeza OsipenkoPhD Colloquium22nd System Dynamics ConferenceOxford, UK July 25, 2004

  • Introduction
  • Research Topic
    • Pre-clinical Diagnostics
    • ELIP test
    • Previous Work in the Field
  • Research Methodology
  • Model Development
    • Model Scheme
    • Model Parts
    • Obstacles/Limitations
    • Validation/Verification
  • Anticipated Results Example
  • Research Contributions
  • Future Work
  • Pre-clinical diagnostics is rooted in immunology – new stage in medical research
  • ELIP test – Probability of Pathology in Pregnancy
  • Modeling and simulation in medical diagnostics has been practiced and there exists a number of tools used by physicians and researchers
  • Successful models of pre-clinical diagnostics tests are needed in order to show their benefits and bring the innovation to the healthcare market
  • System Dynamics (SD) can be used as a well-suited tool for building simulations in the medical field
research topic
Research Topic
  • SD model of the ELIP test diagnostics technology
  • Evaluation of the model’s results in the socio-demographic context
    • Physical Health
    • Healthcare industry
    • Financial Benefits
    • Effectiveness of pre-clinical diagnostics as a health-monitoring tool
  • Systems engineering framework: SD as a tool for Systems Engineers
what is pre clinical diagnostics
What is Pre-clinical Diagnostics?
  • Pre-clinical Diagnostics is a method of detecting a disease before its clinical manifestation
  • Many diseases which develop over time have 3 stages:

Stage 1 Stage 2Stage 3

Quiet period – a person thinks he’s healthy-no signs of sickness, but anover- or under-production of auto-Abs starts to occur to cause a disease in the near future

Sickness is determined – a person might still feel fine, but the doctors are capable of detecting an illnessusing conventional methods

A Sick Person – A patient complains

about an illness, doctors make a

diagnosis and begin the treatment

elip test
  • Detecting probability of Pathology in Pregnancy is a new method evaluating the state of immunoregulation in the development of an embryo
  • This method is based on the detection of the amount of embryotrope antibodies in the blood of a female before and during the pregnancy
  • ELIP-Test is a patented method, which currently is being applied in Moscow clinics and other regions of Russia
  • The test has been demonstrating very successful results for over 7 years
    • an increase in successful pregnancy outcomes and a decrease in newborn’s pathologies.
previous work in the field
Previous Work in the Field
  • Computer Diagnostics
  • Software Tools: DATA 4.0 and such - decision-making and data evaluation tools for medical personnel and doctors
  • Models of the immune system and its parts (Princeton, Santa Fe, Carnegie-Mellon)
    • Analysis of immune behaviors using computer
  • Computer test-beds for various drugs, medical appliances and tools
research methodology
Research Methodology
  • Identify a problem: Dynamic simulation of the ELIP test
  • Dynamic hypothesis: The number of antibodies produced to 4 principle embryotrope antigens determines the outcome of pregnancy*
  • Development of casual loop diagrams
  • Computer simulation model of the system at the root of the problem
  • Validation and verification of the results
  • The use the model’s results to draw the conclusions of the test’s effectiveness in socio-demographic and financial contexts
  • Evaluation of SD methods within the Systems Engineering framework
model development

5 years worth of data

2 pools of the ELIP test results during pregnancy

2 pools of the results of the test given before pregnancy and then during (to the same women)

Dynamic elements

Relationships between the antigens

Relationships between treatments and auto-Abs production

The rest of the elements in the model are mathematically defined

Casual loop diagrams

Building a Model by parts

“What if” scenarios

Sensitivity analysis

Model results summary

Model Development
model parts
Simple Model Example

in Stella: outer shell

Simple Model Example: inner shell

Model Parts
obstacles and limitations
Obstacles and Limitations
  • Data – never enough
  • Model boundaries
  • Avoiding complexity but preserving reliability
  • Validation/verification
  • Generalization
  • Acceptance of the results
validation and verification
Validation and Verification
  • Qualitative and quantitative validation of the model
  • Comparison to the actual field test results
  • Sensitivity analysis
  • Expert opinion
  • Time
  • What type of validation would be sufficient?
anticipated results example
Anticipated Results Example
  • Results of Pregnancy Outcomes –

supervised patients at one

of the Moscow clinics. 1999 data.



Pathologies 4%

Deaths 3%

Healthy 83%


Pathologies 10%

Expected Simulation Results - After using ELIP test as a diagnostic technology and necessary treatments

research contributions
Research Contributions

Primary contributions of this work:

  • Demonstration of the effectiveness of the ELI-P-test (medical, economic, socio-demographic). More specifically, I anticipate to forecast the following:
    • Number of lives saved (higher birth rate)
    • Improved female health (decrease in reported cases)
    • Improved child (future adult) health (decrease in reported cases)
    • Money saved

– Treatment of children with pathologies decreases

– Savings for insurance companies

– Productive population increases

  • Demonstration of the effectiveness of the dynamic modeling approach
    • Specification of SD modeling techniques for medical diagnostics.

Secondary contributions of this work: 

  • An example for building models of other pre-clinical diagnostics tests (which can be applied and marketed separately)
  • Guidelines for building SD models in the medical diagnostics field
future work
Future Work
  • Model refinement
  • Precision test
  • Model expansion
    • Incorporation of treatments
    • External factors
    • Immunology variables
  • Other models of pre-clinical diagnostics test
  • Publication of the results in scientific and popular science journals

Modeling Pre-clinical Diagnostics Test Using System DynamicsWorkshopLeeza OsipenkoPhD Colloquium22nd System Dynamics ConferenceOxford, UK July 25, 2004

discussion topics
Discussion Topics
  • My Research
  • System Dynamics Applications in Medicine:
    • Few exist due to complexity, validity concerns
    • What can we do to promote further interest in the field?
  • System Dynamics and Dynamic Systems:
    • Differences, similarities, integration
  • Validation/Verification of SD Models
    • Forecasting models
    • Degree of certainty
my research
My Research
  • Building models and simulations for pre-clinical diagnostics
  • Systems Dynamics as a tool for modeling in medical diagnostics
  • DATA
  • Examining the validity of the models
  • Acceptance of the results
sd applications in medicine
Few models exist due to complexity and validity concerns

How can these and other difficulties be addressed?

To what degree can computer simulations replace in vivo testing?

What can we do to promote further interest in the field?

Qualified specialists who can bridge the gap between medical doctors and engineers

More theoretical research

Refinement of the models

Tests, examples, trials, etc.

SD Applications in Medicine
system dynamics and dynamic systems
System Dynamics and Dynamic Systems:
  • Clear definitions of DS and SD
  • When is the incorporation necessary?
  • How can it be achieved?
  • Issues:
    • Complexity
    • Reliability
    • Time
    • Money
    • Collaboration
    • Validation
model validation verification
Model Validation/Verification
  • Types of Models
    • Forecasting Models
  • Data
    • Statistical Analysis
    • Historical Data
    • How much data is enough?
  • What constitutes a robust model?
  • How sensitivity analysis shall be performed?
  • What are the requirements for validation and verification of the models?