Working group 3: Patient Modeling and Simulation Ruzena Bajcsy—UC Berkeley Scott L. Bartow—Senatra Home Care Services Amit Bose—Tyco Healthcare M.Cenk Cavusoglu—Case Western Reserve Univ. Robert C. Kircher—Dose Safety Company Douglas Rosendale—VA Charles Taylor—Stanford Univ. Russ Taylor—Johns Hopkins Harvey Rubin—Univ. of Pennsylvania David Arney–Univ. of Pennsylvania

Why develop patient models? Improved health care—outcomes, quality Better utilization of health care costs –Prevention, intervention, maximal value of EHR More efficient device development –Human studies are expensive –Device manufacturers need models –High entry barriers to developing specific models More effective procedure execution –Planning, monitoring, and control Training/professional certification Patient education and guidance in clinical decision making Research

Convincing successes in other fields confirm the value of modeling product development safety cost effectiveness regulatory approval examples: aerospace industry chemical plants automotive

Lessons learned Lesson 1 1 a.Models exist at 5 levels of spatial scale: Biochemical/genetic Cell Organs Whole body In society 1.b Each model evolves on temporal scale 1. c At each scale the models involve hetergeneous structures and physical processes

Examples of “tools” Biochemistry/Genes/Cells Physiome project, DARPA BioComp Organs/whole body ITK open source NIH funded image processing toolkit. “digital astronaut” in planning stage DARPA Virtual Soldier

Lesson 1 continued.. 1.d Models are incomplete Incomplete or non-existing mathematical models for physiological processes Insufficient parameters for most biological processes Incomplete data sets: e.g. quantitative postoperative data not collected 1.d Models must be accessible to the community of practioners—large and heterogeneous to the community of investigators to the community of device developers to the community of regulators 1.e Models must accommodate “uniqueness” of each patient but also must permit aggregation of populations

Lesson 2 Convincing preliminary data show that image based modeling is effective at procedural level—training, outcomes (seizure focus ablation, arrhythmia focus ablation, interventional radiology-image guided biopsies, radiation therapy mapping) clinically cost effective at commercial level—some systems are already in use

Lesson 2.a Convincing preliminary data show that physiology based modeling is effective critical care intra-operative home care Convincing preliminary data show that patient-in-society based modeling is effective home care institutional care vaccine strategies

Mechanisms to share data, models, tools, results are necessary Challenges: 2.a Interoperability 2.b Institutional barriers to sharing data, tools 2.c Maintenance of Privacy 2.d Academic reward system 2.e Commercial reward system Lesson 3

Demonstration cases: (2-5 yr*) Create "Knowledge Portal" Build a foundation for open source environment ontology links to available models, data and device sources protocols for validation Build and distribute anatomical atlases data exists—VA may be best source combine information from multiple patients generate coordinate system to “place” patient searchable generate statistical analysis predict outcomes based on individual characteristics and statistical outcomes device companies can project scales and sizes Create protocol manual detailed written descriptions of specific interventions metrics for evaluation

Statistical Atlases of Patient Anatomy Training Data Sets Segmentation Multiple resolution models Statistical Analysis Average model + variation modes Anatomical Labels Biomechanics General Surgical Plans Outcome data Electronic Anatomical Atlas R. Taylor & J. Yao APPLICATIONS Treatment planning, outcomes analysis, basic research, …

One Application: Bootstrapping Atlas Training Data Sets Segmentation Multiple resolution models Statistical Analysis Average model + variation modes Electronic Anatomical Atlas R. Taylor & J. Yao APPLICATIONS Treatment planning, outcomes analysis, basic research, … Atlas-assisted segmentation

Statistical Atlases of Physiology Training Data Sets Signal processing Analytical models Statistical Analysis Average model + variation modes Signal features Biology info Lab data Outcome data Electronic Atlas R. Taylor & J. Yao APPLICATIONS Device design, treatment monitoring, planning, outcomes analysis, basic research, …

Fused Statistical Atlases Training Data Sets Segmentation Multiple resolution models Statistical Analysis Average model + variation modes Anatomical Labels Lab data General Surgical Plans Outcome data Fused Atlas R. Taylor & J. Yao APPLICATIONS Treatment planning, outcomes analysis, basic research, device design, control, …

Another Application: Filling in information Patient-specific model Fused Electronic Atlas R. Taylor & J. Yao APPLICATIONS Treatment planning, outcomes analysis, basic research, … Atlas-assisted segmentation Patient-specific images Augmented models

Research needs Understand abstraction –domain specific –technical fix Improved techniques for assessing clinically relevant variability in measurements Experimental validation of models using: ex vivo and bio-mimetic materials and systems animal models clinical data Policy—privacy, security, legal, regulatory

Specific recommendations (2 yr*) common ontologies descriptions of blood vessel branching for predicting cardiovascular surgery outcomes descriptions of activities of daily living for safe performance in the home by the elderly (5 yr*) Statistical/analytical tools— “on the fly” analysis of randomized trials risk analysis – procedure/outcome, statistical methods for characterizing variability, abnormality, anatomical variance.

Specific recommendation (2-5 yrs*) Build teams for the production of high confidence medical devices: work plan- 1) multidisciplinary academic and industry teams develops model 2) team does trials to validate model, publishes studies 3) FDA approves model for medical device validation 4) team maintains model 5) device manufacturer uses model for FDA submissions Example: SRI / Stanford consortium with 7 medical device manufacturers to develop model of femoral artery stent. Consortium does data acquisition and modeling. Consortium publishes work, can use for certification, companies buy in and get pre-publication data. Data generated a redesign of stent testing methods and FDA using results in regulatory process Other examples: Diabetes—insulin pump design Chemotherapy-infusion/intralesional design Pacemaker—control and validation Long term oxygen therapy—delivery systems and monitoring Recomendation: FDA, NSF, NIH, NIST, encourage public/private partnerships academic/industry/government

Example: insulin pump device