Working group 3: Patient Modeling and Simulation

1. 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

2. 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

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

4. 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

5. 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

6. 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

7. 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

8. 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

9. Lesson 3 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

10. 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

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

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

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

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

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

16. 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

17. 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.

18. 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

19. Example: insulin pump device