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MMVR BioGears Overview

The document presents an overview of BioGears, an open-source human physiology engine designed for medical training and research. It outlines the agenda for a presentation, details on the engine's modeling approach, system architecture, and verification processes, along with milestones and achievements since its inception in 2013. The purpose of BioGears is to create a publicly available platform that supports accurate simulated physiology across various training applications and enhances medical education for military and civilian use.

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1 Agenda (1:30pm – 5:00 pm) 1. Engine Overview [~50min] • Bio Break [10min] 2. Using the Graphical User Interface (GUI) [~50min] • Bio Break [10min] 3. Interacting with the Application Program Interface (API) [~50min] 4. Questions and Help [until 5pm] Recommended Preparation • If you would like to follow along using the GUI (item 2), please download and unzip the Toolkit from: https://biogearsengine.com/download BioGears: An OpenSource Human Physiology Engine
2 Presentation for MMVR Presenters: Jeff Webb, Rachel Clipp, PhD, Aaron Bray Applied Research Associates, Inc. (ARA) 7 April 2016 BioGears Overview
3 • Program Overview • Modeling Approach • Verification and Validation • Systems Review • Architecture and Tools • Coming Soon BioGears Overview Agenda
4 PROGRAM OVERVIEW
5 • Organization: Applied Research Associates, Inc. (ARA) • Telemedicine & Advanced Technology Research Center (TATRC) Award #: W81XWH-13-2-0068 • Principal Investigator: Mr. Jeff Webb • Amount: $6,959,593 • Period of Performance: Sept 2013 – Sept 2018 • License: Apache 2.0 permissive free software • Disclaimer: This work is supported by the US Army Medical Research and Materiel Command. The views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as an official Department of the Army position, policy, or decision unless so designated by other documentation. Project Information
6 High Level Objectives • Create a publicly available physiology research platform that enables accurate and consistent simulated physiology across training applications • Lower the barrier to create medical training content • Engage the community to develop and extend physiology models • Meet the training needs of the military • Expand the body of knowledge regarding the use of simulated physiology for medical education
7 Organs & Tissues Extravascular & Intravascular Homeostatic Feedback • Baroreceptors & Osmoreceptors • Chemoreceptors • Local Autoregulation Physics Based Approach • Fluid Mechanics – Gases & Fluids • Thermodynamics – Heat Transfer Substances Transport, Diffusion, & Clearance • Gases • Hemoglobin • Ions Patients External Systems Internal Systems Equipment Features Inputs • Parameter Setting • Chronic Conditions • Insults • Interventions Outputs • Acute Events • Clinical Assessments • System Vitals • Compartment Data Scenarios • Static and Repeatable and/or • Real-Time Dynamic Personalized Configuration Interfaces Drugs PK/PD Environment Substances & Thermal Nervous Sympathetic & Parasym. Anesthesia Machine Inhaler System Interactions Cardiovascular Hemodynamics & ECG Respiratory Gas Exchange Blood Chemistry Quantities & Panels Renal Blood Filtering Endocrine Hormones Gastrointestinal Ingestion Energy Thermal & Metabolic • Hormones • Nutrients • Drugs Physiology Engine Overview
8 Milestones 2015 Link to Version History FY13 September: project kick-off FY14 FY15 October: Alpha Build 1.0.0 release and website launch March: 2.0.0 release July: 3.0.0 release FY16 October: Beta Build 4.0.0 release and users conference December: 5.0.0 release March: 5.1.0 release Summer: planned Release Candidate 6.0.0 • System updates • Serialization, modularity, and optimization FY17 Several releases throughout • New systems/features FY18 October: Final Contractual release Maintenance only We are here
9 Current Collaborators and Integrators • 2,700+ downloads of the engine and related files since the Alpha Build Release (October 2014) • 1,400+ pages of physiology modeling methodology and software documentation, and validation data available to our user community • Used by the government/military, academic institutions, and commercial businesses • Let us know if/when you use BioGears and we can add you to the home page of our website!
10 MODELING APPROACH
11 Engine Overview Computation Approach: • Time-stepping transient analysis for linearization of differential equations • Currently 90Hz for 2x real-time simulation on standard laptops • Dynamically change/add/remove elements to represent physiological mechanisms • Stabilization analysis for initialization and implementation of conditions • Designed with low computational overhead • Faster than real-time on typical PC, multiple instances on single or multicore processors • Build Targets include Windows, Mac, Linux, and Raspberry Pi Modeling Approach: • Top-down approach to model development with bottom-up hooks for engine expansion • Multi-scale for varying fidelity, allowing integration of models from any level
12 • Discrete entities that approximate the behavior of a distributed system • Electronic- Hydraulic/Thermal Analogy: body system fluid dynamics and thermodynamics modeled using electrical circuit math • Generalized definitions of Nodes, Paths, and Elements for simple understanding, implementation, and modification Background: Lumped Parameter Modeling Lumped Parameter Modeling of Fluids P=Pressure, F=Flow, R=Resistance, C=Compliance, I=Inertance
13 • Data model • Generic and reusable node and path definitions • Uses the same equations/code with native units • Other element types not in table: • Switch • Valve/diode • Polarized elements Lumped Parameter Approach Circuit Types and Elements Definitions
14 Connection types: • Direct circuit connection – e.g. Anesthesia Machine and Respiratory • Feedback – e.g. Nervous and Endocrine • Substance exchange – e.g. Respiratory and Cardiovascular gas exchange Physiology System Interaction Environment PK/PD Cardiovascular Blood Tissue Extravascular Respiratory Anesthesia Machine Renal Gastrointestinal Energy Nervous Endocrine Inhaler
15 System Data Flow and Modeling Approach Advantages • Modular and extensible • Model fidelity easily modified by adding/removing nodes and elements to circuit • Fully dynamic physics based mechanistic models (rather than state based) – cascading effects • Unlimited stacking/combining of conditions, insults, interventions, interfaces, etc. • Homeostasis based modeling with pathophysiology actions • Able to integrate existing/new models • Not necessarily lumped parameter • Mixed fidelity • Able to simultaneously run any number of instances/patients Preprocess Uses feedback mechanisms to modify elements for the next time step. Process Calculates the entire state of the system for the next time step Assessments Called on demand to calculate and set assessment specific data Reset Initializes the system Initialization Resting: Functionality specific to resting patient stabilization Conditions: Functionality specific to applying conditions PostProcess Advances time by moving the next time step to current
16 Engine Initialization Resting Conditions Running Stabilization Simulation Time = 0 • Dynamic stabilization drives towards patient homeostasis • Each step the engine executes until all specified stabilization criteria are satisfied Step 1: Patient initialization • Systems feedback modifies values to achieve specified patient parameters (e.g. baseline mean arterial pressure) • Standard environment used Step 2: Condition initialization • Conditions applied to represent new patient homeostatic state • Environment changes applied to simulate long term acclimation Simulation begins • Acute insults, interventions, and parameter modification applied instantaneously through actions Step 3: Feedback Mechanisms • Feedback mechanisms that would interfere with chronic conditions (e.g. baroreceptors) are activated Feedback
17 Engine Data Library Tidal Volume varies with weight Circuit is modified to vary arterial pressure Driver is modified to vary heart rate 1 2 3 Patient File Parameters Gender FunctionalResidualCapacity Age 2: HeartRateBasline 1: Weight 3: MeanArterialPressureBaseline Height RespirationRateBaseline BodyFatFraction RightLungRatio CarinaToTeethDistance TotalBloodVolumeBaseline Contractility Patient Modification Example Patients • Properties modify system setup, circuit values, and feedback parameters Substances • Physical and transport related properties • E.g. MolarMass, IonicState, Sedation Compounds • Concentrations of multiple substances • E.g. Saline, Blood Environments • Surrounding properties • E.g. AmbientTemperature, ClothingResistance Nutrition • Meal composition (masses) • E.g. Protein, Calcium Stabilization • Percent difference criteria for stabilization – resting and conditions
18 • Anatomical Compartments defined by sub- circuits and allow access via an anatomy tree • Several overlapping compartment types • Fluid • Liquid • Vascular • Urine • Chyme • Gas • Pulmonary • Tissue • Thermal • Electrical • Compartment properties are combined from children (liquid example) • Volume is a sum • InFlow is a sum • OutFlow is a sum • Pressure comes from an assigned child node (sum does not make sense) • Substance quantities (mass, concentration, etc.) are calculated on demand for any level in hierarchy • There are thousands of compartment data values that are updated each time-step Left Kidney Left Renal Artery Left Renal Vein Left Nephron Left Afferent Arteriole Left Glomerular Capillaries Left Efferent Arteriole Left Peritubular Capillaries Left Bowmans Capsules Left Tubules Left Ureter Compartment Example: Kidney Definition Key: Vascular Urine
19 VERIFICATION AND VALIDATION
20 1. Full Scenario Suite: BioGears test suite includes scenarios to test all patient files, patient actions, substance effects, and equipment performance. Completed for 3 circuit unit tests and 80+ scenarios. 2. Circuit Verification: Verify that the BioGears circuit solver is producing comparable results to established circuit solvers. Completed for 100+ circuit elements/combinations. 3. Timing: Suite is executed by team members throughout development. It is also automatically executed when the code base is altered in the repository. 4. Output: Each scenario indicates the physiologic outputs for validation. Each output is tested to ensure it is within 2% of the results in the code repository. Comparison and error plots (shown) are generated. 5. Full Results: An email is sent to all team members when verification is complete. Each scenario either Passes (green) or Fails (red). Failures must be addressed by the team. Verification Source of Failure VentilatorPressureLossScenarioResultsReport 1 1 21.258999824523926 YpieceDisconnectScenarioResultsReport 1 1 23.98900008201599 AirwayInsultObstructionResultsReport 0 1 14.930999994277954 AtropineScenarioResultsReport 0 1 13.121000051498413 BasicScenario1ResultsReport 0 1 18.195000171661377 Scenario Verification Circuit Verification
21 1. Verification: Unit tests ensure correct implementation and sound physics principles for all tools 2. System Level Validation: All major systems (cardiovascular, respiratory, blood chemistry, etc.) are validated for clinical output level data 3. Compartment Level Calibration: Individual organs (kidney, liver, etc.) or functional units (trachea, alveoli, etc.) are validated wherever possible 4. Scenario Calibration & Validation: Every insult, intervention, and assessment includes a matrix with validation data for whole body combined effects from multiple systems 5. Combined Scenario Validation: All four showcases and several other scenarios validated for combined effects – heavily leveraged SME consultants Bryan Bergeron MD and Nicholas Moss PhD Showcase Scenario Combined Effects Validation Calibration and Validation Scenario Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% Combat Multitrauma 59 (64.8%) 10 (11.0%) 22 (24.2%) 91 Asthma Attack 26 (65.0%) 7 (17.5%) 7 (17.5%) 40 Heat Stroke 52 (76.5%) 10 (14.7%) 6 (8.8%) 68 Exposure 26 (86.7%) 2 (6.7%) 2 (6.7%) 30
22 SYSTEMS REVIEW
23 Feedback • Heart driver – variable compliance Actions • Cardiac Arrest • CPR • Hemorrhage • Pericardial Effusion Conditions • Anemia • Arrhythmia • Bradycardia • Tachycardia • Heart Failure • Pericardial Effusion Events • Asystole • Bradycardia & Tachycardia • Cardiac Arrest • Hypovolemic Shock Assessments • None Cardiovascular Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 18 (75.0%) 4 (16.7%) 2 (8.3%) 24 Compartment 54 (84.4%) 4 (6.3%) 6 (9.4%) 64 Circuit Diagram Resting Validation ECG Outputs Tachycardia Bradycardia Heart Elastance and Compliance Heart Pressure-Volume Curve
24 Feedback • Respiratory driver – from aorta O2 and CO2 • Pleural compliance – from lung volume Actions • Airway Obstruction • Bronchoconstriction • Asthma Attack • COPD Bronchitis • Intubation • Pneumothorax • Conscious Respiration • Occlusive Dressing • Needle Decompression Conditions • COPD (Bronchitis and Emphysema) • Lobar Pneumonia • Respiratory Acidosis & Alkalosis • Max Pulmonary Ventilation Rate • Right Mainstem Intubation Events • Bradypnea & Tachypnea Assessments • Pulmonary Function Test Respiratory Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 10 (83.3%) 0 (0.0%) 2 (16.7%) 12 Compartment 31 (86.1%) 2 (5.6%) 3 (8.3%) 36 Circuit Diagram Resting Validation Lung Volume and Pressures vs Accepted Variable Compliance vs Accepted
25 Feedback • Ultrafiltration – from substance molecular weight/radius and charge • Colloid osmotic pressure – from local Albumin concentration • Tubuloglomerular – from tubules sodium delivery • Osmoreceptor – from blood osmolarity • Active and Passive Reabsorption – from ratio to fluid flow • Gluconeogenesis • Drug clearance Actions • Urinate Conditions • Renal Stenosis Events • Diuresis & Antidiuresis • Natriuresis • Dehydration • Functional Incontinence Assessments • Urinalysis Renal Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 28 (56.0%) 7 (14.0%) 15 (30.0%) 50 Compartment 25 (55.6%) 12 (26.7%) 8 (17.8%) 45 Substance Params 15 (50.0%) 6 (20.0%) 9 (30.0%) 30 Assessment 6 (100.0%) 0 (0.0%) 0 (0.0%) 6 Left Kidney Circuit Diagram Resting Validation
26 Settings • Air Density • Air Velocity • Ambient Temperature • Atmospheric Pressure • Clothing Resistance • Emissivity • Mean Radiant Temperature • Relative Humidity • Respiration Ambient Temperature • Ambient Substance Actions • Environment Change • Thermal Application • Active Heating • Active Cooling • Applied Temperature Conditions • Environment Change Events • None Assessments • None Environment Circuit Diagram Thermoregulation Model High Altitude Change Scenario Results
27 Feedback • Thermal feedback on vascular resistance • Sweating response due to increase core temperature • Shivering response due to decreased core temperature • Metabolic effects on Cardiovascular and Respiratory systems during physical activity Actions • Exercise Conditions • Dehydration • Starvation Events • Fasciculation • Fatigue • Hyperthermia • Heat Stroke • Metabolic/Respiratory Acidosis & Alkalosis Assessments • None Energy Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 9 (100.0%) 0 (0.0%) 0 (0.0%) 9 Circuit Diagram Resting Validation Cold Water Submersion Scenario Results Exercise Submersion Scenario Results
28 Feedback • PK • Partition Coefficients • Clearance • PD • Drug Effects Actions • IV Fluid Administration • IV Drug Administration • IM Drug Administration Conditions • None Events • None Assessments • None Drugs Transport Schematic Morphine PD Effects PK Validation
29 Settings • General • State (on/off) • Ventilator Pressure • Respiratory Rate • Positive End Expired Pressure • Inspiratory Expiratory Ratio • Relief Valve Pressure • Ventilator Pressure • Oxygen Bottle One Volume • Oxygen Bottle Two Volume • Gas Inlet Settings • Inlet Flow • Oxygen Fraction • Primary Gas (Nitrogen/Air) • Oxygen Source (Bottle 1/Bottle 2/Wall) • Related Settings • Airway Mode (Free/Mask/Tube) • Substance Administration Actions • Expiratory/Inspiratory Valve Leaks/Obstructions • Soda Lime Failure • Mask/Tube Leak • Vaporizer Failure • Ventilator Pressure Loss • Oxygen Port/Tank Pressure Loss • Endotracheal Intubation • Esophageal Intubation Conditions • Environment Change Events • Oxygen Bottle Exhausted • Relief Valve Active Assessments • None Anesthesia Machine Circuit Diagram Pressure Controlled Model
30 Settings • Substance • Metered Dose • Nozzle Loss • Spacer Volume Actions • Actuation Conditions • None Events • None Assessments • None Inhaler Circuit Diagram Albuterol Administration Results
31 Feedback • Baroreceptors [Nervous] • Epinephrine and Norepinephrine release [Endocrine] • Produce Albumin [System Interactions / Hepatic] • Acid-Base balance (including O2 & CO2 saturation & pH) [System Interactions] • Gas exchange (Alveolar transfer) [System Interactions] Actions • Consume Meal [Gastrointestinal] Conditions • None Events • Irreversible state • Hypercapnia & Hypoxia [Blood Chemistry] • Brain & Myocardium Oxygen Deficit [Blood Chemistry] Assessments • Complete Blood Count [Blood Chemistry] • Comprehensive Metabolic Panel [Blood Chemistry] Remaining Systems Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 37 (84.1%) 2 (4.5%) 5 (11.4%) 44 Patients 85 (100.0%) 0 (0.0%) 0 (0.0%) 85 Assessments 16 (100.0%) 0 (0.0%) 0 (0.0%) 16 Resting Validation Hemorrhage Scenario Results
32 ARCHITECTURE AND TOOLS
33 • Common Data Model (CDM): Well-defined, intuitive, interchangeable format to standardize interfaces • Standardized inputs, outputs, units, and naming conventions to aid model additions and external model integrators • Application Programming Interface (API): Easy integration and interaction in any programming language • Data organized logically by Anatomy so that users are able to easily find and pull relevant data • Software Development Kit (SDK): Application examples and stand-alone execution • Tutorials, How-to’s, scenario examples Software Architecture
34 • Common data structures for modeling and simulation of the human body • Not specific to any methodology, including BioGears • Separates the physiological data from the physiological modeling methodology • Object Oriented Design of class structures providing a unified set of tools that promotes fast development, compatible data sets, and well-defined interfaces • Provides a well-defined data interchange format that disparate models can use for standardizing inputs and outputs between each other • Allows for specific extensions, but interfaces are defined by the CDM Common Data Model Overview Conceptual Data Model XSD Schema Files • Properties.xsd • System.xsd • Substance.xsd • Scenario.xsd • Circuit.xsd • Anatomy.xsd • Patient & Actions.xsd • Equipment & Actions.xsd • BioGears.xsd Data forms, variables, definitions, and relationships Data Model Binding Code Synthesis DLL • Properties.cxx • System.cxx • Substance.cxx • Scenario.cxx • Circuit.cxx • Anatomy.cxx • Patient & Actions.cxx • Equipment & Actions.cxx • BioGears.cxx Auto generated C++ Classes -Turnkey Serialization Support Common Data Model API C++ DLL • SEProperty.cpp, SEScalar.cpp, SEScalarVolume.cpp, etc. • SECardiovascular.cpp, SERespiratory.cpp, etc. • SECircuit.cpp, SECircuitNode.cpp, SECircuitPath.cpp, etc. • SEPatient.cpp, SESubstance.cpp, SEDrug.cpp, etc. • SESubstanceAdministration.cpp, SEHemorrhage.cpp, etc. Class library reflects the binding and utilizes serialization support • Unit Conversion • Logging (log4cpp) • Unit Test Framework • Generic Circuit Solver • Generic Circuit Transport • Generic Math • Pure Virtual Physiology Engine Interface • Scenario Definition and Execution Additional general algorithms
35 Setup/Modify Next Values Modified Nodal Analysis Calculate Fluxes Valves Pass? Set Node Quantities Advance Time Modify Valve States Yes No Calculate Quantities Preprocess: 1. Systems use “Current” values to setup/modify “Next” values via feedback mechanisms (outside of the solver) Process: 2. Perform numerical integration by using linearization (first order approximations) through Modified Nodal Analysis (Ax=b) a. Use KCL (total Flux at each node = 0) to Calculate the Jacobian matrix (A) and right- hand side vector (b) for each Node Potential and Potential Source Flux (x) b. Use the Eigen templated library linear solver (FullPivLU) to solve for x vector 3. Calculate unknown Fluxes – using Trapezoid Rule where applicable 4. Calculate Valves using assumed diode states (cannot be solved directly) – iterate as necessary 5. Calculate and increment Compliance Path Quantities – No other elements have dynamic Quantities (rigid pipes) 6. Set Node Quantities (based on Path Quantities) • Note: Transporter is called here Postprocess: 7. Advance time by moving “Next” to “Current” values Process Preprocess Postprocess 1 2 3 5 67 4 Circuit Solver • Fully dynamic Modified Nodal Analysis solver for any valid closed-loop circuit • Solves circuit types with any units: Electrical, Fluid, Thermal
36 • Common Data Model: • Substances move with the fluid to each node in the circuit • No particle deposition • Mass is updated as at each time step based on flow into and out of nodes • Concentration and partial pressure are updated after the mass • Systems Interactions: • Partial pressure driven diffusion moves substances between two nodes (O2, CO2, N2) • Perfusion limited diffusion moves substances across the blood/tissue barrier based on flow and partition coefficients (Drugs) • Systemic clearance removes substances from the vena cava to represent metabolic or other clearance mechanisms • Hepatic clearance removes substances from the liver to represent the ability of the liver to metabolize or remove substances Transporter Lung Vascular Lung Tissue Heart Kidney Tissue Kidney Vascular Lung Airway Liver Tissue Liver Vascular Other Tissue Other Vascular Bladder GI
37 Motivation • Data driven developers’ tool to demonstrate basic functionality • Test Engine without command line • Ready for testing out-of-the-box (no compiling) • Simple interface for creating, editing, and executing scenario file, and creating resulting plots of requested data Expectations • Only requirements are to allow editing and execution of scenarios – not a major focus • Not heavily QA’ed – some known bugs being continuously fixed Limitations • Clunky Java Swing for rapid prototyping via the API Developer GUI
38 COMING SOON
39 Current (hopefully deployed this summer with version 6.0.0): • Bug fixing and system refinement • Optimization and increased simulation speed • State serialization – saving and loading simulations • Modularity – more easily replace entire systems • Renal feedback updates • Acid-base balance – O2 & CO2 saturation modifications • Total body substance balance and new substances Near-Term (FY16): • Nervous system additions • Exocrine additions • Endocrine additions • Vascular fluid exchange • Pneumothorax updates • Gastrointestinal updates Long-Term (FY17): • Patient modifications (gender, body mass, etc) • Intoxications – Ketamine proof of concept • Airborne agents (Nerve/Pulmonary/Smoke/CO) and vaporization • Diuretics • Additions to blood assessments and pulmonary function test improvement High Level Near Term Tasks
40 • Use the software for any and all applications (please let us know) • Report problems • Submit code • Currently just email us (https://www.biogearsengine.com/workwithus) • Moving to a public repository – GitHub/BitBucket hosted • Post and respond to Forums (https://www.biogearsengine.com/forums) How to Contribute
41 Please Come See Our Posters! Showcases: Rodney Metoyer Renal: Dr. Austin Baird PK/PD: Dr. Rachel Clipp Energy: Cam Thames
42 Jeff Webb Principal Investigator jwebb@ara.com 919-582-3435 Rachel Clipp, PhD Lead Physiology Modeler rclipp@ara.com 919-582-3330 Aaron Bray Lead Software Architect abray@ara.com 919-582-3333 Contact www.biogearsengine.com/workwithus
43 BACKUP
44 Current System Capabilities Systems Acute Insults & Interventions Chronic Conditions Events Cardiovascular & Blood Chemistry Cardiac Arrest CPR Hemorrhage Pericardial Effusion Anemia Arrhythmia Bradycardia Tachycardia Heart Failure Pericardial Effusion Asystole Bradycardia & Tachycardia Bradypnea & Tachypnea Brain & Myocardium Oxygen Deficit Cardiac Arrest Hypercapnia & Hypoxia Hyperglycemia & Hypoglycemia Hypovolemic Shock Pulseless Rhythm Respiratory Airway Obstruction Bronchoconstriction Asthma Attack COPD Bronchitis Intubation Pneumothorax Conscious Respiration Occlusive Dressing Needle Decompression COPD Lobar Pneumonia Energy & Environment Exercise Environment Changes Thermal Application Dehydration Starvation Environment Changes Fasciculation Fatigue Hyperthermia Heat Stroke Metabolic/Respiratory Acidosis & Alkalosis Renal & GI Urinate Consume Meal Renal Stenosis Diuresis & Antidiuresis Natriuresis Dehydration Functional Incontinence Drugs & Substances & Inhaler IV Fluid Administration IV Drug Administration IM Drug Administration Inhaler Drug Administration Anesthesia Machine Configuration Expiratory/Inspiratory Valve Leaks/Obstructions Soda Lime Failure Mask/Tube Leak Vaporizer Failure Ventilator Pressure Loss Oxygen Port/Tank Pressure Loss Endotracheal Intubation Esophageal Intubation Oxygen Bottle Exhausted Relief Valve Active Note: More to come
45 Provided Data Examples Systems System Vital Examples (Hundreds Total) Compartment Examples (Thousands Total) Assessments (Exhaustive) Cardiovascular Heart Rate Cardiac Output Mean Arterial Pressure Blood Volume Pulmonary Flow Brain Pressure Heart Volumes Substance Concentrations Complete Blood Count Comprehensive Metabolic Panel Blood Chemistry Blood pH Oxygen Saturation Shunt Fraction Hemoglobin Content Respiratory Respiration Rate Tidal Volume Total Lung Volume Pulmonary Resistance Lung Volumes Lung Pressures Air Flow Substance Volume Fractions Pulmonary Function Test Energy Respiratory Quotient Total Metabolic Rate Skin Temp Heat Transfer Rate Environment Ambient Temperature Clothing Resistance Renal Glomerular Filtration Rate Urine Specific Gravity Renal Blood Flow Bladder Substance Concentrations Urinalysis GI Digestion Rate Stomach Contents Drugs & Substances Partition Coefficients Anesthesia Level Plasma Concentration Tissue Concentration Anesthesia Machine Oxygen Bottle Volume Ventilator Pressure Vaporizer substance fractions Tube flows Note: These are only example outputs – there are many, many more
46 Other Included BioGears Tools Unit Converter Unit/Feature Testing • Validate individual tools • Verify individual feedback • Alerts user to introduced bugs Verification Testing • Full scenario suite to test all patient files, patient actions, substance effects, and equipment performance • Each scenario indicates the physiologic outputs for comparison and generates error plots Validation Testing • Spreadsheet with referenced baselines • Color coded error tables automatically generated for all System and relevant compartment data Developer GUI VentilatorPressureLossScenarioResultsReport 1 1 21.258999824523926 YpieceDisconnectScenarioResultsReport 1 1 23.98900008201599 AirwayInsultObstructionResultsReport 0 1 14.930999994277954 AtropineScenarioResultsReport 0 1 13.121000051498413 BasicScenario1ResultsReport 0 1 18.195000171661377 Verification Results Example Developer GUI
47 • Virtual Heroes developed ‘Combat Medic’, for RDECOM STTC • Uses BioGears for live physiology and after action data • Intended to train Combat Medics on the top three preventable causes of death on the battlefield: Hemorrhage, Airway Obstruction and Tension Pneumothorax • The prototype was tested at Fort Bliss, TX by Army combat medics Combat Medic using BioGears Images courtesy of Combat Medic project funded by Army RDECOM-STTC Hemorrhage Airway Obstruction Tension Pneumothorax Link to Video
48 • Runs via the APIInternally Funded Demo GUI
49 Documentation and Tutorials • The website includes detailed documentation for each physiology system and software components (e.g., CDM, Toolkit, SDK, Source Code) • This includes text and tables that explain: system background, model limitations, equations used, and validation data sources and matrices • https://www.biogearsengine.com Expand
50 Envisioned User Groups Adds or replaces systems to extend the functionality Ex. Physiology Modelers BioGears Contributor Use the engine as is via the API Ex. Game Developers Engine Integrator Uses/extends CDM Runs BioGears engine and/or other engine(s) Ex. Mannequin Builder External Model/ Engine Developer Creates custom input to BioGears engine for research or instruction Ex. Teaching Assistant Researcher/ Educator • The physiology engine has been designed and implemented with 4 user groups in mind. • Engine functionality, fidelity and extensibility critical design decisions made to make BioGears user friendly • Scope: • Providing an API with open source libraries • BioGears is not a ‘game’ – it will power immersive training content and other M&S tools
51 • Worked with subcontractor UNC Eshelman School of Pharmacy • Pharmacodynamics also validated through scenario validation • All drugs validated in this manor PK/Clearance Validation Examples Bolus InfusionBolus Bolus
52 Using BioGears • Canned or Dynamic Scenarios • Training and Simulation Scenarios • Physiology and Modeling Classroom Education • Data Analysis • Physiologic Response Scenarios Use-Case Options • Integration of New Models, Systems, Actions, Conditions, and Events • New Patients, Substances, and Drugs • New Initialization Parameters (blood tests, lab results) • Validation and Verification • Data Analysis • Injury Assessment Scoring Input Development Future 1. Needs and Requirements Assessment 2. Validation and Calibration Data Determination 3. Model Design and Implementation 4. Model Verification – Unit Tests to Verify Functionality 5. Model Calibration – Tuning Parameters to Meet Initial Data 6. Model Validation – Use of Model/Feature in Combination To Validate Functionality Model/Feature Development Steps
53 Implementation Setup/Modify Next Values Modified Nodal Analysis Calculate Fluxes Valves Pass? Set Node Quantities Advance Time Modify Valve States Yes No Calculate Quantities Preprocess: 1. Systems use “Current” values to setup/modify “Next” values via feedback mechanisms (outside of the solver) Process: 2. Perform numerical integration by using linearization (first order approximations) through Modified Nodal Analysis (Ax=b) a. Use KCL (total Flux at each node = 0) to Calculate the Jacobian matrix (A) and right- hand side vector (b) for each Node Potential and Potential Source Flux (x) b. Use the Eigen templated library linear solver (FullPivLU) to solve for x vector 3. Calculate unknown Fluxes – using Trapezoid Rule where applicable 4. Calculate Valves using assumed diode states (cannot be solved directly) – iterate as necessary 5. Calculate and increment Compliance Path Quantities – No other elements have dynamic Quantities (rigid pipes) 6. Set Node Quantities (based on Path Quantities) • Note: Transporter is called here Postprocess: 7. Advance time by moving “Next” to “Current” values Process Preprocess Postprocess 1 2 3 5 67 4
54 • Provides a medium for substance transport through the human body • Feedback related to insults/interventions propagated through entire body BioGears System Example: Cardiovascular Interaction Cardiovascular Renal TissueGastrointestinal Respiratory Alveolar Transfer Digestion Clearance Tissue Diffusion
55 Example Scenario: Combat Multitrauma Showcase + Environment Change Hemorrhage & Tension Pneumothorax Needle Decompression Hemorrhage Reduced (Manual Pressure) Tourniquet (Hemorrhage Stopped) & IV (Saline) Morphine High Altitude Environment (End of Current Validated Scenario) 12 min 60 min Note: Does not include sympathetic nervous system – we are currently designing
56 PHARMACOKINETIC AND PHARMACODYNAMIC MODELING
57 Pharmacokinetic Diffusion • Substances are either perfusion-limited or permeability-limited during diffusion. All current drugs in the BioGears engine are perfusion-limited. Perfusion Limited Diffusion 𝑉𝑇 ∗ ∆𝐶 𝑇 = 𝑄 𝑇 ∗ 𝐶 𝑉 − 𝑄 𝑇 ∗ 𝐶 𝑇 𝐾 𝑃 ∆𝑀 𝑇 = 𝑄 𝑇 ∗ 𝐶 𝑉 − 𝑄 𝑇 ∗ 𝐶 𝑇 𝐾 𝑃 Ref. Khalil and Laer, 2011 𝑄 𝑇, 𝐶 𝑉 𝑉𝑇, 𝐶 𝑇, 𝐾 𝑃 𝑄 𝑇, 𝐶 𝑉𝑒𝑛 ∆𝑀 𝑇: 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑚𝑎𝑠𝑠 𝑜𝑓 𝑠𝑢𝑏𝑠𝑡𝑎𝑛𝑐𝑒 𝑉𝑇: 𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑡𝑖𝑠𝑠𝑢𝑒 𝑄 𝑇: 𝑉𝑎𝑠𝑐𝑢𝑙𝑎𝑟 𝐵𝑙𝑜𝑜𝑑 𝐹𝑙𝑜𝑤 𝐶 𝑇: 𝑆𝑢𝑏𝑠𝑡𝑎𝑛𝑐𝑒 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑡ℎ𝑒 𝑡𝑖𝑠𝑠𝑢𝑒 𝐶 𝑉: 𝑆𝑢𝑏𝑠𝑡𝑎𝑛𝑐𝑒 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑣𝑎𝑠𝑐𝑢𝑙𝑎𝑟𝑡𝑢𝑟𝑒 𝐾 𝑃: 𝑃𝑙𝑎𝑠𝑚𝑎: 𝑇𝑖𝑠𝑠𝑢𝑒 𝑝𝑎𝑟𝑡𝑖𝑡𝑖𝑜𝑛 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡
58 Pharmacokinetics – Partition Coefficients • An example of the PK properties from the substance files are shown below. • Partition coefficients can be directly input into the substance file, bypassing the engine calculation.
59 Pharmacokinetics - Clearance 𝑉𝑇 Volume mL 𝐶 𝑇 Concentration ug/mL 𝑄 𝑇 Flow mL/s 𝐾 𝑃 Partition Coefficient 𝑑𝑇 Time Step s ∆𝑀 𝑇 Mass Increment ug 𝐶𝑙 𝑅 Renal Clearance mL/s/kg 𝐵𝑊 Body Weight kg 𝑓𝑢 Fraction Unbound 𝐶𝑙𝐼 Intrinsic Clearance mL/s/kg 𝐶𝑙 𝐻 Hepatic Clearance mL/s/kg 𝐶𝑙 𝑆 Systemic Clearance mL/s/kg 𝑉𝐶𝑅 Blood Volume Cleared Renal mL 𝑉𝐶𝐿 Blood Volume Cleared Liver mL 𝑉𝐶𝑆 Blood Volume Cleared Systemic mL • Renal Clearance • 𝑉𝐶𝑅 = 𝐶𝑙 𝑅 ∗ 𝐵𝑊 ∗ 𝑑𝑇/2 • ∆𝑀 𝑇 = 𝑉𝐶𝑅 ∗ 𝐶 𝑇 • Hepatic Clearance • 𝐶𝑙 𝐻 = 𝑄 𝐿∗𝑓𝑢∗𝐶𝑙 𝐼∗𝐵𝑊 𝑄 𝐿+𝑓𝑢∗𝐶𝑙 𝐼∗𝐵𝑊 • 𝑉𝐶𝐿 = 𝐶𝑙 𝐻 ∗ 𝐵𝑊 ∗ 𝑑𝑇 • ∆𝑀 𝑇 = 𝑉𝐶𝐻 ∗ 𝐶 𝑇 • Systemic Clearance • 𝑉𝐶𝑆 = (𝐶𝑙 𝑆∗ 𝐵𝑊 ∗ 𝑑𝑇) − 𝑉𝐶𝑅 − 𝑉𝐶𝐿 • ∆𝑀 𝑇 = 𝑉𝐶𝑆 ∗ 𝐶 𝑇
60 Pharmacodynamics ∆𝐸 = 𝐸 𝑚𝑎𝑥 ∗ 𝐶 𝑝 𝐸𝐶50 + 𝐶 𝑝 𝐸𝐶50 = 𝐶 𝑚𝑎𝑥 32 • Drug effects are calculated based on the plasma concentration, the drug effect, and the concentration at 50% effect. • The EC50 value was not readily available for the majority of drugs in question, so the EC50 value was calculated from the max concentration. • The Cmax is shown circled on the example plot.
61 • The following clinical effects were calculated: • Heart Rate • Diastolic and Systolic Pressure • Respiratory Rate • Tidal Volume • Bronchodilation Level • Sedation Level • Neuromuscular Block Level Pharmacodynamics
62 Agent Threat Example Scenario • Cortexiphan is a fictitious threat agent administered through the air. • The substance parameters were modified in the agent file and a scenario was created changing the ambient air to be 9.5% cortexiphan. Time(s) Cortexiphan-PlasmaConcentration(ug/mL) 50 75 100 125 150 175 200 225 250 275 300 0 50 100 150 200 250 300 350 400 450 Plasma concentration increases with increased exposure time. Time(s) RespirationRate(1/min) 0 30 60 90 120 150 180 210 240 270 300 14 16 18 20 22 24 26 28 Respiration rate increases as calculated by PK/PD response model.
63 API
64 • Common Data Model • Overview • Class Introduction • Physiology Engine Interface • Static vs. Dynamic Engine Execution • Inputs • Conditions • Actions • Outputs • Systems • Compartments • Assessments • We will not go over the entire code base, just the forward facing classes available for application developers Application Programming Interface Overview
65 • Property – Scalar, Functions, Enums, etc. • Scalar is a double with a unit, with embedded unit converter • System • Physiology – Cardiovascular, Respiratory, Drugs, etc. • Equipment • Anesthesia Machine – Generic Machine • ECG – Generic Waveform reader • Inhaler – Optional spacer • Environment – Properties external patient • Environmental Conditions – Meteorology, Heat/Cool, ChemBio, etc. • Patient – Body characteristics, Baselines, State • Compartment – Specific anatomical and machine dynamics • Flow, Volume, Pressure, • Substance Mass/Concentration or Volume/Volume Fraction CDM Classes (SE prefix)
66 • Substance – Anything being circulated in the blood or pulmonary systems, including drugs • Configuration – Engine specific data that does not fit anywhere else • Time step, Stabilization, Coefficients, etc. • Circuit – Generic circuit solving library • Scenario – Engine execution instructions • Utils • Unit Converter – Versatile conversion engine • General Math – Generic saturation, Kelman equation, etc. • Logging – Logging classes built on log4cpp • Data Tracking – Write data to file each time step as the engine runs • Physiology Engine Interface • Generic interface for physiology methodology based on CDM CDM Classes (SE prefix)
67 • Static execution of the engine based on a scenario file • Specify a patient file • Request data to be output in a column tab delimited txt file • Conditions • Actions • Dynamic execution of the engine • Initialize Engine with a patient, any conditions, and an optional results file • Time Controls • GetTimeStep, GetTime, AdvanceTime • Provide Actions • GetCompartments() • GetSystems • GetAssessment() • GetSubstances() Static vs. Dynamic Physiology Engine Interface
68 • Set up the engine to start at a specific state bool InitializeEngine(const SEPatient& patient, const std::vector<const SECondition*>* conditions=nullptr) • Patient • Anemia, Bradycardia, COPD, Pulmonary Shunt, Renal Stenosis, Tachycardia, Heart Failure, Meal, Lobar Pneumonia, Pericardial Effusion • Environmental • Initial Environment Conditions • We have tested each condition individually, but you do have the option to stack conditions, we have not testing all combinations • Test that the engine can converge on stacked conditions Conditions (Input)
69 • Instruct the engine to change its state in some way void AdvanceModelTime()// Single Time Step void AdvanceModelTime(double time, const std::shared_ptr<CCompoundUnit>& unit) bool ProcessAction(const SEAction& action) • Action Types • Patient – Various Insults and Interventions • Environment – Configuration and Application • Anesthesia Machine – Configuration and Insults • Inhaler - Configuration Actions (Input)
70 • Physiological state data • Respiratory Rate, Heart Rate, Metabolic Rate, etc. • Equipment and Environment state data • Oxygen Tank Volume, Nozzle Loss, Heater Power, etc. • Engine implementation does not have to provide every output, although BioGears does const SEEnvironment* GetEnvironment() const SEBloodChemistrySystem* GetBloodChemistrySystem() … (much more physiology) … const SEAnesthesiaMachine* GetAnesthesiaMachine() const SEElectroCardioGram* GetElectroCardioGram() Systems (Output)
71 • Compartments are a generic interface for the fluid dynamics data of the body, such as Volumes, Pressures, In Flows, and Out Flows • A compartment can represent various fidelities of data • Skin, Right Arm, Liver, Left Heart • Anesthesia Machine Ventilator, Mask, etc. • Multiple compartment types to represent different fluid dynamic types • Gas (Pulmonary), Liquid (Blood, Chyme, Urine), Thermal (Body Heat), Tissue (Extravascular) • Compartments have a parent/child hierarchy • There can be multiple compartments associated with anatomy • Substance quantities for each substance is also provided in the compartment • A vascular compartment includes the substance masses and concentration • A pulmonary compartment will contain the volumes and volume fractions of all substances in that compartment const SEAnatomyCompartments* GetAnatomyCompartments() const SEInhalerCompartments* GetInhalerCompartments() const SEAnesthesiaMachineCompartments* GetAnesthesiaMachineCompartments() Compartments (Output)
72 • Assessments are physiology data formed into various medical tests and panels intended for clinicians • Some calculation may apply • Complete Blood Panel, Complete Metabolic Panel, Pulmonary Function Test, and Urnialysis • Provide an assessment object to the Engine for it to fill out SEPulmonaryFunctionTest pft(bg->GetLogger()); bg->GetPatientAssessment(pft); • Various states on the patient or equipment can change during execution and state flags can be polled for these changes • Antidiuresis, Asystole, CardiacArrest, …, RespiratoryAlkalosis, RightMainStemIntubation, Tachycardia, Tachypnea • OxygenBottleExhausted, RefliefValveActive, etc. GetPatient().IsEventActive(CDM::enumPatientEvent::CardiacArrest) Patient Assessments and Events (Output)
73 • Event Callbacks • Along with polling for events, you can provide a callback object that the engine will call when any event is triggered • Exceptions • Any time an engine gets out of its designed boundary conditions, it will throw an exception of or derived from CommonDataModelException • Data Track • You can have the engine write out any system or compartment scalar into tab delimited file at each time step. Callbacks, Error handling and Data Tracks
74 • Each engine can have its own log and will log the following types of data • The Logger has the option to be given a LoggerForward class that an end user provides. The logger will call methods on this class whenever it logs giving the application a way to programmatically react to the engine Logs Type Description Debug Detailed information about engine execution Info General information of what the engine is doing Warning The engine received something or is doing something that may or may not invalidate results Error The engine has performed a calculation it was not designed for but will keep running, results are most likely invalid Fatal The engine has entered a state it was not designed for and will stop execution immediately. BioGears will follow this by throwing an exception
75 • Each engine can have its own log and will log the following types of data • Debug – Detailed information about engine execution • Info– General information of what the engine is doing • Warning – The engine received something or is doing something that may or may not invalidate results • Error– The engine has performed a calculation it was not designed for but will keep running, results are most likely invalid • Fatal– The engine has entered a state it was not designed for and will stop execution immediately. BioGears will follow this by throwing an exception. • The Logger has the option to be given a LoggerForward class that an end user provides. The logger will call methods on this class whenever it logs giving the application a way to programmatically react to the engine Logs
76 • The engine executes out of a bin directory, this is a break down of the directories and its files required for BioGears • config – Stabilization parameters, you may need to tweak these files if you combine conditions • ecg – The waveform set to use for the ecg • environments* – a set of canned environments for the engine that the environment can initialize to • nutrition* – a set of canned nutrition files that can be used with the ConsumeMeal condition and ConsumeNutrition action • patients – a set of tested and validated patient files • substances – the set of substance files for BioGears • UCEDefs.txt – Unit conversion configuration file • BioGearsConfiguration.xml – Override any BioGears configuration property. By default this file is empty. *Not required by the engine OnDisk Breakdown

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MMVR BioGears Overview

  • 1.
    1 Agenda (1:30pm –5:00 pm) 1. Engine Overview [~50min] • Bio Break [10min] 2. Using the Graphical User Interface (GUI) [~50min] • Bio Break [10min] 3. Interacting with the Application Program Interface (API) [~50min] 4. Questions and Help [until 5pm] Recommended Preparation • If you would like to follow along using the GUI (item 2), please download and unzip the Toolkit from: https://biogearsengine.com/download BioGears: An OpenSource Human Physiology Engine
  • 2.
    2 Presentation for MMVR Presenters:Jeff Webb, Rachel Clipp, PhD, Aaron Bray Applied Research Associates, Inc. (ARA) 7 April 2016 BioGears Overview
  • 3.
    3 • Program Overview •Modeling Approach • Verification and Validation • Systems Review • Architecture and Tools • Coming Soon BioGears Overview Agenda
  • 4.
  • 5.
    5 • Organization: AppliedResearch Associates, Inc. (ARA) • Telemedicine & Advanced Technology Research Center (TATRC) Award #: W81XWH-13-2-0068 • Principal Investigator: Mr. Jeff Webb • Amount: $6,959,593 • Period of Performance: Sept 2013 – Sept 2018 • License: Apache 2.0 permissive free software • Disclaimer: This work is supported by the US Army Medical Research and Materiel Command. The views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as an official Department of the Army position, policy, or decision unless so designated by other documentation. Project Information
  • 6.
    6 High Level Objectives •Create a publicly available physiology research platform that enables accurate and consistent simulated physiology across training applications • Lower the barrier to create medical training content • Engage the community to develop and extend physiology models • Meet the training needs of the military • Expand the body of knowledge regarding the use of simulated physiology for medical education
  • 7.
    7 Organs & Tissues Extravascular& Intravascular Homeostatic Feedback • Baroreceptors & Osmoreceptors • Chemoreceptors • Local Autoregulation Physics Based Approach • Fluid Mechanics – Gases & Fluids • Thermodynamics – Heat Transfer Substances Transport, Diffusion, & Clearance • Gases • Hemoglobin • Ions Patients External Systems Internal Systems Equipment Features Inputs • Parameter Setting • Chronic Conditions • Insults • Interventions Outputs • Acute Events • Clinical Assessments • System Vitals • Compartment Data Scenarios • Static and Repeatable and/or • Real-Time Dynamic Personalized Configuration Interfaces Drugs PK/PD Environment Substances & Thermal Nervous Sympathetic & Parasym. Anesthesia Machine Inhaler System Interactions Cardiovascular Hemodynamics & ECG Respiratory Gas Exchange Blood Chemistry Quantities & Panels Renal Blood Filtering Endocrine Hormones Gastrointestinal Ingestion Energy Thermal & Metabolic • Hormones • Nutrients • Drugs Physiology Engine Overview
  • 8.
    8 Milestones 2015 Link to VersionHistory FY13 September: project kick-off FY14 FY15 October: Alpha Build 1.0.0 release and website launch March: 2.0.0 release July: 3.0.0 release FY16 October: Beta Build 4.0.0 release and users conference December: 5.0.0 release March: 5.1.0 release Summer: planned Release Candidate 6.0.0 • System updates • Serialization, modularity, and optimization FY17 Several releases throughout • New systems/features FY18 October: Final Contractual release Maintenance only We are here
  • 9.
    9 Current Collaborators andIntegrators • 2,700+ downloads of the engine and related files since the Alpha Build Release (October 2014) • 1,400+ pages of physiology modeling methodology and software documentation, and validation data available to our user community • Used by the government/military, academic institutions, and commercial businesses • Let us know if/when you use BioGears and we can add you to the home page of our website!
  • 10.
  • 11.
    11 Engine Overview Computation Approach: •Time-stepping transient analysis for linearization of differential equations • Currently 90Hz for 2x real-time simulation on standard laptops • Dynamically change/add/remove elements to represent physiological mechanisms • Stabilization analysis for initialization and implementation of conditions • Designed with low computational overhead • Faster than real-time on typical PC, multiple instances on single or multicore processors • Build Targets include Windows, Mac, Linux, and Raspberry Pi Modeling Approach: • Top-down approach to model development with bottom-up hooks for engine expansion • Multi-scale for varying fidelity, allowing integration of models from any level
  • 12.
    12 • Discrete entitiesthat approximate the behavior of a distributed system • Electronic- Hydraulic/Thermal Analogy: body system fluid dynamics and thermodynamics modeled using electrical circuit math • Generalized definitions of Nodes, Paths, and Elements for simple understanding, implementation, and modification Background: Lumped Parameter Modeling Lumped Parameter Modeling of Fluids P=Pressure, F=Flow, R=Resistance, C=Compliance, I=Inertance
  • 13.
    13 • Data model •Generic and reusable node and path definitions • Uses the same equations/code with native units • Other element types not in table: • Switch • Valve/diode • Polarized elements Lumped Parameter Approach Circuit Types and Elements Definitions
  • 14.
    14 Connection types: • Directcircuit connection – e.g. Anesthesia Machine and Respiratory • Feedback – e.g. Nervous and Endocrine • Substance exchange – e.g. Respiratory and Cardiovascular gas exchange Physiology System Interaction Environment PK/PD Cardiovascular Blood Tissue Extravascular Respiratory Anesthesia Machine Renal Gastrointestinal Energy Nervous Endocrine Inhaler
  • 15.
    15 System Data Flowand Modeling Approach Advantages • Modular and extensible • Model fidelity easily modified by adding/removing nodes and elements to circuit • Fully dynamic physics based mechanistic models (rather than state based) – cascading effects • Unlimited stacking/combining of conditions, insults, interventions, interfaces, etc. • Homeostasis based modeling with pathophysiology actions • Able to integrate existing/new models • Not necessarily lumped parameter • Mixed fidelity • Able to simultaneously run any number of instances/patients Preprocess Uses feedback mechanisms to modify elements for the next time step. Process Calculates the entire state of the system for the next time step Assessments Called on demand to calculate and set assessment specific data Reset Initializes the system Initialization Resting: Functionality specific to resting patient stabilization Conditions: Functionality specific to applying conditions PostProcess Advances time by moving the next time step to current
  • 16.
    16 Engine Initialization Resting ConditionsRunning Stabilization Simulation Time = 0 • Dynamic stabilization drives towards patient homeostasis • Each step the engine executes until all specified stabilization criteria are satisfied Step 1: Patient initialization • Systems feedback modifies values to achieve specified patient parameters (e.g. baseline mean arterial pressure) • Standard environment used Step 2: Condition initialization • Conditions applied to represent new patient homeostatic state • Environment changes applied to simulate long term acclimation Simulation begins • Acute insults, interventions, and parameter modification applied instantaneously through actions Step 3: Feedback Mechanisms • Feedback mechanisms that would interfere with chronic conditions (e.g. baroreceptors) are activated Feedback
  • 17.
    17 Engine Data Library TidalVolume varies with weight Circuit is modified to vary arterial pressure Driver is modified to vary heart rate 1 2 3 Patient File Parameters Gender FunctionalResidualCapacity Age 2: HeartRateBasline 1: Weight 3: MeanArterialPressureBaseline Height RespirationRateBaseline BodyFatFraction RightLungRatio CarinaToTeethDistance TotalBloodVolumeBaseline Contractility Patient Modification Example Patients • Properties modify system setup, circuit values, and feedback parameters Substances • Physical and transport related properties • E.g. MolarMass, IonicState, Sedation Compounds • Concentrations of multiple substances • E.g. Saline, Blood Environments • Surrounding properties • E.g. AmbientTemperature, ClothingResistance Nutrition • Meal composition (masses) • E.g. Protein, Calcium Stabilization • Percent difference criteria for stabilization – resting and conditions
  • 18.
    18 • Anatomical Compartmentsdefined by sub- circuits and allow access via an anatomy tree • Several overlapping compartment types • Fluid • Liquid • Vascular • Urine • Chyme • Gas • Pulmonary • Tissue • Thermal • Electrical • Compartment properties are combined from children (liquid example) • Volume is a sum • InFlow is a sum • OutFlow is a sum • Pressure comes from an assigned child node (sum does not make sense) • Substance quantities (mass, concentration, etc.) are calculated on demand for any level in hierarchy • There are thousands of compartment data values that are updated each time-step Left Kidney Left Renal Artery Left Renal Vein Left Nephron Left Afferent Arteriole Left Glomerular Capillaries Left Efferent Arteriole Left Peritubular Capillaries Left Bowmans Capsules Left Tubules Left Ureter Compartment Example: Kidney Definition Key: Vascular Urine
  • 19.
  • 20.
    20 1. Full ScenarioSuite: BioGears test suite includes scenarios to test all patient files, patient actions, substance effects, and equipment performance. Completed for 3 circuit unit tests and 80+ scenarios. 2. Circuit Verification: Verify that the BioGears circuit solver is producing comparable results to established circuit solvers. Completed for 100+ circuit elements/combinations. 3. Timing: Suite is executed by team members throughout development. It is also automatically executed when the code base is altered in the repository. 4. Output: Each scenario indicates the physiologic outputs for validation. Each output is tested to ensure it is within 2% of the results in the code repository. Comparison and error plots (shown) are generated. 5. Full Results: An email is sent to all team members when verification is complete. Each scenario either Passes (green) or Fails (red). Failures must be addressed by the team. Verification Source of Failure VentilatorPressureLossScenarioResultsReport 1 1 21.258999824523926 YpieceDisconnectScenarioResultsReport 1 1 23.98900008201599 AirwayInsultObstructionResultsReport 0 1 14.930999994277954 AtropineScenarioResultsReport 0 1 13.121000051498413 BasicScenario1ResultsReport 0 1 18.195000171661377 Scenario Verification Circuit Verification
  • 21.
    21 1. Verification: Unittests ensure correct implementation and sound physics principles for all tools 2. System Level Validation: All major systems (cardiovascular, respiratory, blood chemistry, etc.) are validated for clinical output level data 3. Compartment Level Calibration: Individual organs (kidney, liver, etc.) or functional units (trachea, alveoli, etc.) are validated wherever possible 4. Scenario Calibration & Validation: Every insult, intervention, and assessment includes a matrix with validation data for whole body combined effects from multiple systems 5. Combined Scenario Validation: All four showcases and several other scenarios validated for combined effects – heavily leveraged SME consultants Bryan Bergeron MD and Nicholas Moss PhD Showcase Scenario Combined Effects Validation Calibration and Validation Scenario Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% Combat Multitrauma 59 (64.8%) 10 (11.0%) 22 (24.2%) 91 Asthma Attack 26 (65.0%) 7 (17.5%) 7 (17.5%) 40 Heat Stroke 52 (76.5%) 10 (14.7%) 6 (8.8%) 68 Exposure 26 (86.7%) 2 (6.7%) 2 (6.7%) 30
  • 22.
  • 23.
    23 Feedback • Heart driver– variable compliance Actions • Cardiac Arrest • CPR • Hemorrhage • Pericardial Effusion Conditions • Anemia • Arrhythmia • Bradycardia • Tachycardia • Heart Failure • Pericardial Effusion Events • Asystole • Bradycardia & Tachycardia • Cardiac Arrest • Hypovolemic Shock Assessments • None Cardiovascular Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 18 (75.0%) 4 (16.7%) 2 (8.3%) 24 Compartment 54 (84.4%) 4 (6.3%) 6 (9.4%) 64 Circuit Diagram Resting Validation ECG Outputs Tachycardia Bradycardia Heart Elastance and Compliance Heart Pressure-Volume Curve
  • 24.
    24 Feedback • Respiratory driver– from aorta O2 and CO2 • Pleural compliance – from lung volume Actions • Airway Obstruction • Bronchoconstriction • Asthma Attack • COPD Bronchitis • Intubation • Pneumothorax • Conscious Respiration • Occlusive Dressing • Needle Decompression Conditions • COPD (Bronchitis and Emphysema) • Lobar Pneumonia • Respiratory Acidosis & Alkalosis • Max Pulmonary Ventilation Rate • Right Mainstem Intubation Events • Bradypnea & Tachypnea Assessments • Pulmonary Function Test Respiratory Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 10 (83.3%) 0 (0.0%) 2 (16.7%) 12 Compartment 31 (86.1%) 2 (5.6%) 3 (8.3%) 36 Circuit Diagram Resting Validation Lung Volume and Pressures vs Accepted Variable Compliance vs Accepted
  • 25.
    25 Feedback • Ultrafiltration –from substance molecular weight/radius and charge • Colloid osmotic pressure – from local Albumin concentration • Tubuloglomerular – from tubules sodium delivery • Osmoreceptor – from blood osmolarity • Active and Passive Reabsorption – from ratio to fluid flow • Gluconeogenesis • Drug clearance Actions • Urinate Conditions • Renal Stenosis Events • Diuresis & Antidiuresis • Natriuresis • Dehydration • Functional Incontinence Assessments • Urinalysis Renal Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 28 (56.0%) 7 (14.0%) 15 (30.0%) 50 Compartment 25 (55.6%) 12 (26.7%) 8 (17.8%) 45 Substance Params 15 (50.0%) 6 (20.0%) 9 (30.0%) 30 Assessment 6 (100.0%) 0 (0.0%) 0 (0.0%) 6 Left Kidney Circuit Diagram Resting Validation
  • 26.
    26 Settings • Air Density •Air Velocity • Ambient Temperature • Atmospheric Pressure • Clothing Resistance • Emissivity • Mean Radiant Temperature • Relative Humidity • Respiration Ambient Temperature • Ambient Substance Actions • Environment Change • Thermal Application • Active Heating • Active Cooling • Applied Temperature Conditions • Environment Change Events • None Assessments • None Environment Circuit Diagram Thermoregulation Model High Altitude Change Scenario Results
  • 27.
    27 Feedback • Thermal feedbackon vascular resistance • Sweating response due to increase core temperature • Shivering response due to decreased core temperature • Metabolic effects on Cardiovascular and Respiratory systems during physical activity Actions • Exercise Conditions • Dehydration • Starvation Events • Fasciculation • Fatigue • Hyperthermia • Heat Stroke • Metabolic/Respiratory Acidosis & Alkalosis Assessments • None Energy Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 9 (100.0%) 0 (0.0%) 0 (0.0%) 9 Circuit Diagram Resting Validation Cold Water Submersion Scenario Results Exercise Submersion Scenario Results
  • 28.
    28 Feedback • PK • Partition Coefficients •Clearance • PD • Drug Effects Actions • IV Fluid Administration • IV Drug Administration • IM Drug Administration Conditions • None Events • None Assessments • None Drugs Transport Schematic Morphine PD Effects PK Validation
  • 29.
    29 Settings • General • State(on/off) • Ventilator Pressure • Respiratory Rate • Positive End Expired Pressure • Inspiratory Expiratory Ratio • Relief Valve Pressure • Ventilator Pressure • Oxygen Bottle One Volume • Oxygen Bottle Two Volume • Gas Inlet Settings • Inlet Flow • Oxygen Fraction • Primary Gas (Nitrogen/Air) • Oxygen Source (Bottle 1/Bottle 2/Wall) • Related Settings • Airway Mode (Free/Mask/Tube) • Substance Administration Actions • Expiratory/Inspiratory Valve Leaks/Obstructions • Soda Lime Failure • Mask/Tube Leak • Vaporizer Failure • Ventilator Pressure Loss • Oxygen Port/Tank Pressure Loss • Endotracheal Intubation • Esophageal Intubation Conditions • Environment Change Events • Oxygen Bottle Exhausted • Relief Valve Active Assessments • None Anesthesia Machine Circuit Diagram Pressure Controlled Model
  • 30.
    30 Settings • Substance • MeteredDose • Nozzle Loss • Spacer Volume Actions • Actuation Conditions • None Events • None Assessments • None Inhaler Circuit Diagram Albuterol Administration Results
  • 31.
    31 Feedback • Baroreceptors [Nervous] •Epinephrine and Norepinephrine release [Endocrine] • Produce Albumin [System Interactions / Hepatic] • Acid-Base balance (including O2 & CO2 saturation & pH) [System Interactions] • Gas exchange (Alveolar transfer) [System Interactions] Actions • Consume Meal [Gastrointestinal] Conditions • None Events • Irreversible state • Hypercapnia & Hypoxia [Blood Chemistry] • Brain & Myocardium Oxygen Deficit [Blood Chemistry] Assessments • Complete Blood Count [Blood Chemistry] • Comprehensive Metabolic Panel [Blood Chemistry] Remaining Systems Data Type Number (%) of Validation Measures in Deviation Category Total < 10% 10 – 30% > 30% System 37 (84.1%) 2 (4.5%) 5 (11.4%) 44 Patients 85 (100.0%) 0 (0.0%) 0 (0.0%) 85 Assessments 16 (100.0%) 0 (0.0%) 0 (0.0%) 16 Resting Validation Hemorrhage Scenario Results
  • 32.
  • 33.
    33 • Common DataModel (CDM): Well-defined, intuitive, interchangeable format to standardize interfaces • Standardized inputs, outputs, units, and naming conventions to aid model additions and external model integrators • Application Programming Interface (API): Easy integration and interaction in any programming language • Data organized logically by Anatomy so that users are able to easily find and pull relevant data • Software Development Kit (SDK): Application examples and stand-alone execution • Tutorials, How-to’s, scenario examples Software Architecture
  • 34.
    34 • Common datastructures for modeling and simulation of the human body • Not specific to any methodology, including BioGears • Separates the physiological data from the physiological modeling methodology • Object Oriented Design of class structures providing a unified set of tools that promotes fast development, compatible data sets, and well-defined interfaces • Provides a well-defined data interchange format that disparate models can use for standardizing inputs and outputs between each other • Allows for specific extensions, but interfaces are defined by the CDM Common Data Model Overview Conceptual Data Model XSD Schema Files • Properties.xsd • System.xsd • Substance.xsd • Scenario.xsd • Circuit.xsd • Anatomy.xsd • Patient & Actions.xsd • Equipment & Actions.xsd • BioGears.xsd Data forms, variables, definitions, and relationships Data Model Binding Code Synthesis DLL • Properties.cxx • System.cxx • Substance.cxx • Scenario.cxx • Circuit.cxx • Anatomy.cxx • Patient & Actions.cxx • Equipment & Actions.cxx • BioGears.cxx Auto generated C++ Classes -Turnkey Serialization Support Common Data Model API C++ DLL • SEProperty.cpp, SEScalar.cpp, SEScalarVolume.cpp, etc. • SECardiovascular.cpp, SERespiratory.cpp, etc. • SECircuit.cpp, SECircuitNode.cpp, SECircuitPath.cpp, etc. • SEPatient.cpp, SESubstance.cpp, SEDrug.cpp, etc. • SESubstanceAdministration.cpp, SEHemorrhage.cpp, etc. Class library reflects the binding and utilizes serialization support • Unit Conversion • Logging (log4cpp) • Unit Test Framework • Generic Circuit Solver • Generic Circuit Transport • Generic Math • Pure Virtual Physiology Engine Interface • Scenario Definition and Execution Additional general algorithms
  • 35.
    35 Setup/Modify Next Values Modified Nodal Analysis Calculate Fluxes Valves Pass? SetNode Quantities Advance Time Modify Valve States Yes No Calculate Quantities Preprocess: 1. Systems use “Current” values to setup/modify “Next” values via feedback mechanisms (outside of the solver) Process: 2. Perform numerical integration by using linearization (first order approximations) through Modified Nodal Analysis (Ax=b) a. Use KCL (total Flux at each node = 0) to Calculate the Jacobian matrix (A) and right- hand side vector (b) for each Node Potential and Potential Source Flux (x) b. Use the Eigen templated library linear solver (FullPivLU) to solve for x vector 3. Calculate unknown Fluxes – using Trapezoid Rule where applicable 4. Calculate Valves using assumed diode states (cannot be solved directly) – iterate as necessary 5. Calculate and increment Compliance Path Quantities – No other elements have dynamic Quantities (rigid pipes) 6. Set Node Quantities (based on Path Quantities) • Note: Transporter is called here Postprocess: 7. Advance time by moving “Next” to “Current” values Process Preprocess Postprocess 1 2 3 5 67 4 Circuit Solver • Fully dynamic Modified Nodal Analysis solver for any valid closed-loop circuit • Solves circuit types with any units: Electrical, Fluid, Thermal
  • 36.
    36 • Common DataModel: • Substances move with the fluid to each node in the circuit • No particle deposition • Mass is updated as at each time step based on flow into and out of nodes • Concentration and partial pressure are updated after the mass • Systems Interactions: • Partial pressure driven diffusion moves substances between two nodes (O2, CO2, N2) • Perfusion limited diffusion moves substances across the blood/tissue barrier based on flow and partition coefficients (Drugs) • Systemic clearance removes substances from the vena cava to represent metabolic or other clearance mechanisms • Hepatic clearance removes substances from the liver to represent the ability of the liver to metabolize or remove substances Transporter Lung Vascular Lung Tissue Heart Kidney Tissue Kidney Vascular Lung Airway Liver Tissue Liver Vascular Other Tissue Other Vascular Bladder GI
  • 37.
    37 Motivation • Data drivendevelopers’ tool to demonstrate basic functionality • Test Engine without command line • Ready for testing out-of-the-box (no compiling) • Simple interface for creating, editing, and executing scenario file, and creating resulting plots of requested data Expectations • Only requirements are to allow editing and execution of scenarios – not a major focus • Not heavily QA’ed – some known bugs being continuously fixed Limitations • Clunky Java Swing for rapid prototyping via the API Developer GUI
  • 38.
  • 39.
    39 Current (hopefully deployedthis summer with version 6.0.0): • Bug fixing and system refinement • Optimization and increased simulation speed • State serialization – saving and loading simulations • Modularity – more easily replace entire systems • Renal feedback updates • Acid-base balance – O2 & CO2 saturation modifications • Total body substance balance and new substances Near-Term (FY16): • Nervous system additions • Exocrine additions • Endocrine additions • Vascular fluid exchange • Pneumothorax updates • Gastrointestinal updates Long-Term (FY17): • Patient modifications (gender, body mass, etc) • Intoxications – Ketamine proof of concept • Airborne agents (Nerve/Pulmonary/Smoke/CO) and vaporization • Diuretics • Additions to blood assessments and pulmonary function test improvement High Level Near Term Tasks
  • 40.
    40 • Use thesoftware for any and all applications (please let us know) • Report problems • Submit code • Currently just email us (https://www.biogearsengine.com/workwithus) • Moving to a public repository – GitHub/BitBucket hosted • Post and respond to Forums (https://www.biogearsengine.com/forums) How to Contribute
  • 41.
    41 Please Come SeeOur Posters! Showcases: Rodney Metoyer Renal: Dr. Austin Baird PK/PD: Dr. Rachel Clipp Energy: Cam Thames
  • 42.
    42 Jeff Webb Principal Investigator jwebb@ara.com 919-582-3435 RachelClipp, PhD Lead Physiology Modeler rclipp@ara.com 919-582-3330 Aaron Bray Lead Software Architect abray@ara.com 919-582-3333 Contact www.biogearsengine.com/workwithus
  • 43.
  • 44.
    44 Current System Capabilities SystemsAcute Insults & Interventions Chronic Conditions Events Cardiovascular & Blood Chemistry Cardiac Arrest CPR Hemorrhage Pericardial Effusion Anemia Arrhythmia Bradycardia Tachycardia Heart Failure Pericardial Effusion Asystole Bradycardia & Tachycardia Bradypnea & Tachypnea Brain & Myocardium Oxygen Deficit Cardiac Arrest Hypercapnia & Hypoxia Hyperglycemia & Hypoglycemia Hypovolemic Shock Pulseless Rhythm Respiratory Airway Obstruction Bronchoconstriction Asthma Attack COPD Bronchitis Intubation Pneumothorax Conscious Respiration Occlusive Dressing Needle Decompression COPD Lobar Pneumonia Energy & Environment Exercise Environment Changes Thermal Application Dehydration Starvation Environment Changes Fasciculation Fatigue Hyperthermia Heat Stroke Metabolic/Respiratory Acidosis & Alkalosis Renal & GI Urinate Consume Meal Renal Stenosis Diuresis & Antidiuresis Natriuresis Dehydration Functional Incontinence Drugs & Substances & Inhaler IV Fluid Administration IV Drug Administration IM Drug Administration Inhaler Drug Administration Anesthesia Machine Configuration Expiratory/Inspiratory Valve Leaks/Obstructions Soda Lime Failure Mask/Tube Leak Vaporizer Failure Ventilator Pressure Loss Oxygen Port/Tank Pressure Loss Endotracheal Intubation Esophageal Intubation Oxygen Bottle Exhausted Relief Valve Active Note: More to come
  • 45.
    45 Provided Data Examples SystemsSystem Vital Examples (Hundreds Total) Compartment Examples (Thousands Total) Assessments (Exhaustive) Cardiovascular Heart Rate Cardiac Output Mean Arterial Pressure Blood Volume Pulmonary Flow Brain Pressure Heart Volumes Substance Concentrations Complete Blood Count Comprehensive Metabolic Panel Blood Chemistry Blood pH Oxygen Saturation Shunt Fraction Hemoglobin Content Respiratory Respiration Rate Tidal Volume Total Lung Volume Pulmonary Resistance Lung Volumes Lung Pressures Air Flow Substance Volume Fractions Pulmonary Function Test Energy Respiratory Quotient Total Metabolic Rate Skin Temp Heat Transfer Rate Environment Ambient Temperature Clothing Resistance Renal Glomerular Filtration Rate Urine Specific Gravity Renal Blood Flow Bladder Substance Concentrations Urinalysis GI Digestion Rate Stomach Contents Drugs & Substances Partition Coefficients Anesthesia Level Plasma Concentration Tissue Concentration Anesthesia Machine Oxygen Bottle Volume Ventilator Pressure Vaporizer substance fractions Tube flows Note: These are only example outputs – there are many, many more
  • 46.
    46 Other Included BioGearsTools Unit Converter Unit/Feature Testing • Validate individual tools • Verify individual feedback • Alerts user to introduced bugs Verification Testing • Full scenario suite to test all patient files, patient actions, substance effects, and equipment performance • Each scenario indicates the physiologic outputs for comparison and generates error plots Validation Testing • Spreadsheet with referenced baselines • Color coded error tables automatically generated for all System and relevant compartment data Developer GUI VentilatorPressureLossScenarioResultsReport 1 1 21.258999824523926 YpieceDisconnectScenarioResultsReport 1 1 23.98900008201599 AirwayInsultObstructionResultsReport 0 1 14.930999994277954 AtropineScenarioResultsReport 0 1 13.121000051498413 BasicScenario1ResultsReport 0 1 18.195000171661377 Verification Results Example Developer GUI
  • 47.
    47 • Virtual Heroesdeveloped ‘Combat Medic’, for RDECOM STTC • Uses BioGears for live physiology and after action data • Intended to train Combat Medics on the top three preventable causes of death on the battlefield: Hemorrhage, Airway Obstruction and Tension Pneumothorax • The prototype was tested at Fort Bliss, TX by Army combat medics Combat Medic using BioGears Images courtesy of Combat Medic project funded by Army RDECOM-STTC Hemorrhage Airway Obstruction Tension Pneumothorax Link to Video
  • 48.
    48 • Runs viathe APIInternally Funded Demo GUI
  • 49.
    49 Documentation and Tutorials •The website includes detailed documentation for each physiology system and software components (e.g., CDM, Toolkit, SDK, Source Code) • This includes text and tables that explain: system background, model limitations, equations used, and validation data sources and matrices • https://www.biogearsengine.com Expand
  • 50.
    50 Envisioned User Groups Addsor replaces systems to extend the functionality Ex. Physiology Modelers BioGears Contributor Use the engine as is via the API Ex. Game Developers Engine Integrator Uses/extends CDM Runs BioGears engine and/or other engine(s) Ex. Mannequin Builder External Model/ Engine Developer Creates custom input to BioGears engine for research or instruction Ex. Teaching Assistant Researcher/ Educator • The physiology engine has been designed and implemented with 4 user groups in mind. • Engine functionality, fidelity and extensibility critical design decisions made to make BioGears user friendly • Scope: • Providing an API with open source libraries • BioGears is not a ‘game’ – it will power immersive training content and other M&S tools
  • 51.
    51 • Worked withsubcontractor UNC Eshelman School of Pharmacy • Pharmacodynamics also validated through scenario validation • All drugs validated in this manor PK/Clearance Validation Examples Bolus InfusionBolus Bolus
  • 52.
    52 Using BioGears • Cannedor Dynamic Scenarios • Training and Simulation Scenarios • Physiology and Modeling Classroom Education • Data Analysis • Physiologic Response Scenarios Use-Case Options • Integration of New Models, Systems, Actions, Conditions, and Events • New Patients, Substances, and Drugs • New Initialization Parameters (blood tests, lab results) • Validation and Verification • Data Analysis • Injury Assessment Scoring Input Development Future 1. Needs and Requirements Assessment 2. Validation and Calibration Data Determination 3. Model Design and Implementation 4. Model Verification – Unit Tests to Verify Functionality 5. Model Calibration – Tuning Parameters to Meet Initial Data 6. Model Validation – Use of Model/Feature in Combination To Validate Functionality Model/Feature Development Steps
  • 53.
    53 Implementation Setup/Modify Next Values Modified Nodal Analysis Calculate Fluxes Valves Pass? SetNode Quantities Advance Time Modify Valve States Yes No Calculate Quantities Preprocess: 1. Systems use “Current” values to setup/modify “Next” values via feedback mechanisms (outside of the solver) Process: 2. Perform numerical integration by using linearization (first order approximations) through Modified Nodal Analysis (Ax=b) a. Use KCL (total Flux at each node = 0) to Calculate the Jacobian matrix (A) and right- hand side vector (b) for each Node Potential and Potential Source Flux (x) b. Use the Eigen templated library linear solver (FullPivLU) to solve for x vector 3. Calculate unknown Fluxes – using Trapezoid Rule where applicable 4. Calculate Valves using assumed diode states (cannot be solved directly) – iterate as necessary 5. Calculate and increment Compliance Path Quantities – No other elements have dynamic Quantities (rigid pipes) 6. Set Node Quantities (based on Path Quantities) • Note: Transporter is called here Postprocess: 7. Advance time by moving “Next” to “Current” values Process Preprocess Postprocess 1 2 3 5 67 4
  • 54.
    54 • Provides amedium for substance transport through the human body • Feedback related to insults/interventions propagated through entire body BioGears System Example: Cardiovascular Interaction Cardiovascular Renal TissueGastrointestinal Respiratory Alveolar Transfer Digestion Clearance Tissue Diffusion
  • 55.
    55 Example Scenario: CombatMultitrauma Showcase + Environment Change Hemorrhage & Tension Pneumothorax Needle Decompression Hemorrhage Reduced (Manual Pressure) Tourniquet (Hemorrhage Stopped) & IV (Saline) Morphine High Altitude Environment (End of Current Validated Scenario) 12 min 60 min Note: Does not include sympathetic nervous system – we are currently designing
  • 56.
  • 57.
    57 Pharmacokinetic Diffusion • Substancesare either perfusion-limited or permeability-limited during diffusion. All current drugs in the BioGears engine are perfusion-limited. Perfusion Limited Diffusion 𝑉𝑇 ∗ ∆𝐶 𝑇 = 𝑄 𝑇 ∗ 𝐶 𝑉 − 𝑄 𝑇 ∗ 𝐶 𝑇 𝐾 𝑃 ∆𝑀 𝑇 = 𝑄 𝑇 ∗ 𝐶 𝑉 − 𝑄 𝑇 ∗ 𝐶 𝑇 𝐾 𝑃 Ref. Khalil and Laer, 2011 𝑄 𝑇, 𝐶 𝑉 𝑉𝑇, 𝐶 𝑇, 𝐾 𝑃 𝑄 𝑇, 𝐶 𝑉𝑒𝑛 ∆𝑀 𝑇: 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑚𝑎𝑠𝑠 𝑜𝑓 𝑠𝑢𝑏𝑠𝑡𝑎𝑛𝑐𝑒 𝑉𝑇: 𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑡𝑖𝑠𝑠𝑢𝑒 𝑄 𝑇: 𝑉𝑎𝑠𝑐𝑢𝑙𝑎𝑟 𝐵𝑙𝑜𝑜𝑑 𝐹𝑙𝑜𝑤 𝐶 𝑇: 𝑆𝑢𝑏𝑠𝑡𝑎𝑛𝑐𝑒 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑡ℎ𝑒 𝑡𝑖𝑠𝑠𝑢𝑒 𝐶 𝑉: 𝑆𝑢𝑏𝑠𝑡𝑎𝑛𝑐𝑒 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑣𝑎𝑠𝑐𝑢𝑙𝑎𝑟𝑡𝑢𝑟𝑒 𝐾 𝑃: 𝑃𝑙𝑎𝑠𝑚𝑎: 𝑇𝑖𝑠𝑠𝑢𝑒 𝑝𝑎𝑟𝑡𝑖𝑡𝑖𝑜𝑛 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡
  • 58.
    58 Pharmacokinetics – PartitionCoefficients • An example of the PK properties from the substance files are shown below. • Partition coefficients can be directly input into the substance file, bypassing the engine calculation.
  • 59.
    59 Pharmacokinetics - Clearance 𝑉𝑇Volume mL 𝐶 𝑇 Concentration ug/mL 𝑄 𝑇 Flow mL/s 𝐾 𝑃 Partition Coefficient 𝑑𝑇 Time Step s ∆𝑀 𝑇 Mass Increment ug 𝐶𝑙 𝑅 Renal Clearance mL/s/kg 𝐵𝑊 Body Weight kg 𝑓𝑢 Fraction Unbound 𝐶𝑙𝐼 Intrinsic Clearance mL/s/kg 𝐶𝑙 𝐻 Hepatic Clearance mL/s/kg 𝐶𝑙 𝑆 Systemic Clearance mL/s/kg 𝑉𝐶𝑅 Blood Volume Cleared Renal mL 𝑉𝐶𝐿 Blood Volume Cleared Liver mL 𝑉𝐶𝑆 Blood Volume Cleared Systemic mL • Renal Clearance • 𝑉𝐶𝑅 = 𝐶𝑙 𝑅 ∗ 𝐵𝑊 ∗ 𝑑𝑇/2 • ∆𝑀 𝑇 = 𝑉𝐶𝑅 ∗ 𝐶 𝑇 • Hepatic Clearance • 𝐶𝑙 𝐻 = 𝑄 𝐿∗𝑓𝑢∗𝐶𝑙 𝐼∗𝐵𝑊 𝑄 𝐿+𝑓𝑢∗𝐶𝑙 𝐼∗𝐵𝑊 • 𝑉𝐶𝐿 = 𝐶𝑙 𝐻 ∗ 𝐵𝑊 ∗ 𝑑𝑇 • ∆𝑀 𝑇 = 𝑉𝐶𝐻 ∗ 𝐶 𝑇 • Systemic Clearance • 𝑉𝐶𝑆 = (𝐶𝑙 𝑆∗ 𝐵𝑊 ∗ 𝑑𝑇) − 𝑉𝐶𝑅 − 𝑉𝐶𝐿 • ∆𝑀 𝑇 = 𝑉𝐶𝑆 ∗ 𝐶 𝑇
  • 60.
    60 Pharmacodynamics ∆𝐸 = 𝐸 𝑚𝑎𝑥∗ 𝐶 𝑝 𝐸𝐶50 + 𝐶 𝑝 𝐸𝐶50 = 𝐶 𝑚𝑎𝑥 32 • Drug effects are calculated based on the plasma concentration, the drug effect, and the concentration at 50% effect. • The EC50 value was not readily available for the majority of drugs in question, so the EC50 value was calculated from the max concentration. • The Cmax is shown circled on the example plot.
  • 61.
    61 • The followingclinical effects were calculated: • Heart Rate • Diastolic and Systolic Pressure • Respiratory Rate • Tidal Volume • Bronchodilation Level • Sedation Level • Neuromuscular Block Level Pharmacodynamics
  • 62.
    62 Agent Threat ExampleScenario • Cortexiphan is a fictitious threat agent administered through the air. • The substance parameters were modified in the agent file and a scenario was created changing the ambient air to be 9.5% cortexiphan. Time(s) Cortexiphan-PlasmaConcentration(ug/mL) 50 75 100 125 150 175 200 225 250 275 300 0 50 100 150 200 250 300 350 400 450 Plasma concentration increases with increased exposure time. Time(s) RespirationRate(1/min) 0 30 60 90 120 150 180 210 240 270 300 14 16 18 20 22 24 26 28 Respiration rate increases as calculated by PK/PD response model.
  • 63.
  • 64.
    64 • Common DataModel • Overview • Class Introduction • Physiology Engine Interface • Static vs. Dynamic Engine Execution • Inputs • Conditions • Actions • Outputs • Systems • Compartments • Assessments • We will not go over the entire code base, just the forward facing classes available for application developers Application Programming Interface Overview
  • 65.
    65 • Property –Scalar, Functions, Enums, etc. • Scalar is a double with a unit, with embedded unit converter • System • Physiology – Cardiovascular, Respiratory, Drugs, etc. • Equipment • Anesthesia Machine – Generic Machine • ECG – Generic Waveform reader • Inhaler – Optional spacer • Environment – Properties external patient • Environmental Conditions – Meteorology, Heat/Cool, ChemBio, etc. • Patient – Body characteristics, Baselines, State • Compartment – Specific anatomical and machine dynamics • Flow, Volume, Pressure, • Substance Mass/Concentration or Volume/Volume Fraction CDM Classes (SE prefix)
  • 66.
    66 • Substance –Anything being circulated in the blood or pulmonary systems, including drugs • Configuration – Engine specific data that does not fit anywhere else • Time step, Stabilization, Coefficients, etc. • Circuit – Generic circuit solving library • Scenario – Engine execution instructions • Utils • Unit Converter – Versatile conversion engine • General Math – Generic saturation, Kelman equation, etc. • Logging – Logging classes built on log4cpp • Data Tracking – Write data to file each time step as the engine runs • Physiology Engine Interface • Generic interface for physiology methodology based on CDM CDM Classes (SE prefix)
  • 67.
    67 • Static executionof the engine based on a scenario file • Specify a patient file • Request data to be output in a column tab delimited txt file • Conditions • Actions • Dynamic execution of the engine • Initialize Engine with a patient, any conditions, and an optional results file • Time Controls • GetTimeStep, GetTime, AdvanceTime • Provide Actions • GetCompartments() • GetSystems • GetAssessment() • GetSubstances() Static vs. Dynamic Physiology Engine Interface
  • 68.
    68 • Set upthe engine to start at a specific state bool InitializeEngine(const SEPatient& patient, const std::vector<const SECondition*>* conditions=nullptr) • Patient • Anemia, Bradycardia, COPD, Pulmonary Shunt, Renal Stenosis, Tachycardia, Heart Failure, Meal, Lobar Pneumonia, Pericardial Effusion • Environmental • Initial Environment Conditions • We have tested each condition individually, but you do have the option to stack conditions, we have not testing all combinations • Test that the engine can converge on stacked conditions Conditions (Input)
  • 69.
    69 • Instruct theengine to change its state in some way void AdvanceModelTime()// Single Time Step void AdvanceModelTime(double time, const std::shared_ptr<CCompoundUnit>& unit) bool ProcessAction(const SEAction& action) • Action Types • Patient – Various Insults and Interventions • Environment – Configuration and Application • Anesthesia Machine – Configuration and Insults • Inhaler - Configuration Actions (Input)
  • 70.
    70 • Physiological statedata • Respiratory Rate, Heart Rate, Metabolic Rate, etc. • Equipment and Environment state data • Oxygen Tank Volume, Nozzle Loss, Heater Power, etc. • Engine implementation does not have to provide every output, although BioGears does const SEEnvironment* GetEnvironment() const SEBloodChemistrySystem* GetBloodChemistrySystem() … (much more physiology) … const SEAnesthesiaMachine* GetAnesthesiaMachine() const SEElectroCardioGram* GetElectroCardioGram() Systems (Output)
  • 71.
    71 • Compartments area generic interface for the fluid dynamics data of the body, such as Volumes, Pressures, In Flows, and Out Flows • A compartment can represent various fidelities of data • Skin, Right Arm, Liver, Left Heart • Anesthesia Machine Ventilator, Mask, etc. • Multiple compartment types to represent different fluid dynamic types • Gas (Pulmonary), Liquid (Blood, Chyme, Urine), Thermal (Body Heat), Tissue (Extravascular) • Compartments have a parent/child hierarchy • There can be multiple compartments associated with anatomy • Substance quantities for each substance is also provided in the compartment • A vascular compartment includes the substance masses and concentration • A pulmonary compartment will contain the volumes and volume fractions of all substances in that compartment const SEAnatomyCompartments* GetAnatomyCompartments() const SEInhalerCompartments* GetInhalerCompartments() const SEAnesthesiaMachineCompartments* GetAnesthesiaMachineCompartments() Compartments (Output)
  • 72.
    72 • Assessments arephysiology data formed into various medical tests and panels intended for clinicians • Some calculation may apply • Complete Blood Panel, Complete Metabolic Panel, Pulmonary Function Test, and Urnialysis • Provide an assessment object to the Engine for it to fill out SEPulmonaryFunctionTest pft(bg->GetLogger()); bg->GetPatientAssessment(pft); • Various states on the patient or equipment can change during execution and state flags can be polled for these changes • Antidiuresis, Asystole, CardiacArrest, …, RespiratoryAlkalosis, RightMainStemIntubation, Tachycardia, Tachypnea • OxygenBottleExhausted, RefliefValveActive, etc. GetPatient().IsEventActive(CDM::enumPatientEvent::CardiacArrest) Patient Assessments and Events (Output)
  • 73.
    73 • Event Callbacks •Along with polling for events, you can provide a callback object that the engine will call when any event is triggered • Exceptions • Any time an engine gets out of its designed boundary conditions, it will throw an exception of or derived from CommonDataModelException • Data Track • You can have the engine write out any system or compartment scalar into tab delimited file at each time step. Callbacks, Error handling and Data Tracks
  • 74.
    74 • Each enginecan have its own log and will log the following types of data • The Logger has the option to be given a LoggerForward class that an end user provides. The logger will call methods on this class whenever it logs giving the application a way to programmatically react to the engine Logs Type Description Debug Detailed information about engine execution Info General information of what the engine is doing Warning The engine received something or is doing something that may or may not invalidate results Error The engine has performed a calculation it was not designed for but will keep running, results are most likely invalid Fatal The engine has entered a state it was not designed for and will stop execution immediately. BioGears will follow this by throwing an exception
  • 75.
    75 • Each enginecan have its own log and will log the following types of data • Debug – Detailed information about engine execution • Info– General information of what the engine is doing • Warning – The engine received something or is doing something that may or may not invalidate results • Error– The engine has performed a calculation it was not designed for but will keep running, results are most likely invalid • Fatal– The engine has entered a state it was not designed for and will stop execution immediately. BioGears will follow this by throwing an exception. • The Logger has the option to be given a LoggerForward class that an end user provides. The logger will call methods on this class whenever it logs giving the application a way to programmatically react to the engine Logs
  • 76.
    76 • The engineexecutes out of a bin directory, this is a break down of the directories and its files required for BioGears • config – Stabilization parameters, you may need to tweak these files if you combine conditions • ecg – The waveform set to use for the ecg • environments* – a set of canned environments for the engine that the environment can initialize to • nutrition* – a set of canned nutrition files that can be used with the ConsumeMeal condition and ConsumeNutrition action • patients – a set of tested and validated patient files • substances – the set of substance files for BioGears • UCEDefs.txt – Unit conversion configuration file • BioGearsConfiguration.xml – Override any BioGears configuration property. By default this file is empty. *Not required by the engine OnDisk Breakdown

Editor's Notes

  • #34 Common physiology system level model for modeling and simulation of the human body Provides a well-defined data interchange format that disparate models can use for standardizing inputs and outputs between each other Object Oriented Design of class structures providing a unified set of tools that promotes fast development, compatible data sets, and well-defined interfaces Separates the physiological data from the physiological modeling methodology Prevents “engineer” code mixing with data organization and design Strongly typed design that is intended to grow via community adoption and involvement Extension allowed for model specific extensions, but interfaces are defined by the common data model
  • #48 Important to discuss that our delivery to combat medic team shows that biogears CAN be used by target audience Integration with Unreal-based game is part of SOW requirement Biogears engine powers all physiology, dynamically reacting to user actions
  • #51 Mention UNC-CH and UT using in cirriculums – potential for student projects – ARA’s excellent intern program We are developing these systems with 4 main user groups in mind Design decisions and functionality are geared towards their use
  • #58 Perfusion-limited is diffusion primarily driven by the flow to the space Permeability-limited diffusion is primarily driven by the ability of the substance to be taken into the tissue. Not flow.