A Design Specification for Critical Illness Digital Twins (CIDTs) to Cure Sepsis: Responding to the National Academies of Sciences, Engineering and Medicine Report "Foundational Research Gaps and Future Directions for Digital Twins"

Overview

Journal ArXiv

Date 2024 May 20

PMID 38764598

Authors

Gary An

Chase Cockrell

Affiliations

Soon will be listed here.

Abstract

On December 15, 2023, The National Academies of Sciences, Engineering and Medicine (NASEM) released a report entitled: "Foundational Research Gaps and Future Directions for Digital Twins." The ostensible purpose of this report was to bring some structure to the burgeoning field of digital twins by providing a working definition and a series of research challenges that need to be addressed to allow this technology to fulfill its full potential. In the work presented herein we focus on five specific findings from the NASEM Report: 1) definition of a Digital Twin, 2) using "fit-for-purpose" guidance, 3) developing novel approaches to Verification, Validation and Uncertainty Quantification (VVUQ) of Digital Twins, 4) incorporating control as an explicit purpose for a Digital Twin and 5) using a Digital Twin to guide data collection and sensor development, and describe how these findings are addressed through the design specifications for a Critical Illness Digital Twin (CIDT) aimed at curing sepsis.

References

Cockrell C, An G . Utilizing the Heterogeneity of Clinical Data for Model Refinement and Rule Discovery Through the Application of Genetic Algorithms to Calibrate a High-Dimensional Agent-Based Model of Systemic Inflammation. Front Physiol. 2021; 12:662845. PMC: 8172123. DOI: 10.3389/fphys.2021.662845. View

Cockrell C, Ozik J, Collier N, An G . Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models. Simulation. 2021; 97(4):287-296. PMC: 8570577. DOI: 10.1177/0037549720975075. View

Lvovschi V, Arnaud L, Parizot C, Freund Y, Juillien G, Ghillani-Dalbin P . Cytokine profiles in sepsis have limited relevance for stratifying patients in the emergency department: a prospective observational study. PLoS One. 2012; 6(12):e28870. PMC: 3248412. DOI: 10.1371/journal.pone.0028870. View

Downs A, Plaxco K . Real-Time, Molecular Monitoring Using Electrochemical Aptamer Based Sensors: Opportunities and Challenges. ACS Sens. 2022; 7(10):2823-2832. PMC: 9840907. DOI: 10.1021/acssensors.2c01428. View

Moingeon P . Artificial intelligence-driven drug development against autoimmune diseases. Trends Pharmacol Sci. 2023; 44(7):411-424. DOI: 10.1016/j.tips.2023.04.005. View

Cheng Y, Straube R, Alnaif A, Huang L, Leil T, Schmidt B . Virtual Populations for Quantitative Systems Pharmacology Models. Methods Mol Biol. 2022; 2486:129-179. DOI: 10.1007/978-1-0716-2265-0_8. View

Larrick J, Wright S . Native cytokine antagonists. Baillieres Clin Haematol. 1992; 5(3):681-702. DOI: 10.1016/s0950-3536(11)80012-0. View

Aldo P, Marusov G, Svancara D, David J, Mor G . Simple Plex(™) : A Novel Multi-Analyte, Automated Microfluidic Immunoassay Platform for the Detection of Human and Mouse Cytokines and Chemokines. Am J Reprod Immunol. 2016; 75(6):678-93. PMC: 5084752. DOI: 10.1111/aji.12512. View

Petersen B, Yang J, Grathwohl W, Cockrell C, Santiago C, An G . Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. J Comput Biol. 2019; 26(6):597-604. PMC: 6590719. DOI: 10.1089/cmb.2018.0168. View

10.

Lord J, Midwinter M, Chen Y, Belli A, Brohi K, Kovacs E . The systemic immune response to trauma: an overview of pathophysiology and treatment. Lancet. 2014; 384(9952):1455-65. PMC: 4729362. DOI: 10.1016/S0140-6736(14)60687-5. View

11.

Angus D . The search for effective therapy for sepsis: back to the drawing board?. JAMA. 2011; 306(23):2614-5. DOI: 10.1001/jama.2011.1853. View

12.

Cockrell R, An G . Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation. PLoS Comput Biol. 2018; 14(2):e1005876. PMC: 5813897. DOI: 10.1371/journal.pcbi.1005876. View

13.

Baudin F, Pouzot-Nevoret C, Louzier V, Goy-Thollot I, Barthelemy A, Junot S . Animal ICU… Why not also use the existing veterinary ICUs?. Ann Intensive Care. 2019; 9(1):93. PMC: 6702538. DOI: 10.1186/s13613-019-0568-x. View

14.

Unwin H, Kopczynska M, Pugh R, Tan L, Subbe C, Ellis G . The Use of Different Sepsis Risk Stratification Tools on the Wards and in Emergency Departments Uncovers Different Mortality Risks: Results of the Three Welsh National Multicenter Point-Prevalence Studies. Crit Care Explor. 2021; 3(10):e0558. PMC: 8542169. DOI: 10.1097/CCE.0000000000000558. View

15.

Nathan C, Sporn M . Cytokines in context. J Cell Biol. 1991; 113(5):981-6. PMC: 2289009. DOI: 10.1083/jcb.113.5.981. View

16.

Vinyals O, Babuschkin I, Czarnecki W, Mathieu M, Dudzik A, Chung J . Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature. 2019; 575(7782):350-354. DOI: 10.1038/s41586-019-1724-z. View

17.

Huang S, Jin R, Zhou Z . Active Learning by Querying Informative and Representative Examples. IEEE Trans Pattern Anal Mach Intell. 2015; 36(10):1936-49. DOI: 10.1109/TPAMI.2014.2307881. View

18.

Buchman T, Billiar T, Elster E, Kirk A, Rimawi R, Vodovotz Y . Precision Medicine for Critical Illness and Injury. Crit Care Med. 2016; 44(9):1635-8. DOI: 10.1097/CCM.0000000000002028. View

19.

Stanski N, Wong H . Prognostic and predictive enrichment in sepsis. Nat Rev Nephrol. 2019; 16(1):20-31. PMC: 7097452. DOI: 10.1038/s41581-019-0199-3. View

20.

Angus D, Lidicker J, Clermont G, Carcillo J, Pinsky M . Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001; 29(7):1303-10. DOI: 10.1097/00003246-200107000-00002. View