Abstract
Introduction: Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group’s existing digital twin model for the treatment of sepsis. Methods: The authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 (“agree”) or 7 (“strongly agree”). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model. Results: After the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements. Conclusion: This descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology.
| Original language | English (US) |
|---|---|
| Article number | 161 |
| Journal | BMC Neurology |
| Volume | 23 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2023 |
Funding
Author AL was supported by CTSA Grant Number UL1TR002377 from the National Center for Advancing Translational Science (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. This study was performed on behalf of the Digital Twin Platform for education, research, and healthcare delivery investigator group. Collaborative Author List: Anna Cervantes-Arslanian, MD, Boston University Chris Marcellino, MD, Mayo Clinic – Rochester Chris Robinson, DO, MS, University of Florida Christopher L. Kramer, MD, University of Chicago David W Freeman, MD, Mayo Clinic – Florida David Y. Hwang, MD, FAAN, FCCM, FNCS, Yale-New Haven Edward Manno, MD, Northwestern Eelco Wijdicks, MD/PhD, Mayo Clinic – Rochester Jason Siegel, MD, Mayo Clinic – Florida Jennifer Fugate, DO, Mayo Clinic – Rochester Joao A. Gomes MD FCCM FAHA, Cleveland Clinic – Main Campus Joseph Burns, MD, Boston Medical Center Kevin Gobeske, MD/PhD, Yale Maximiliano Hawkes, MD, University of Nebraska Philippe Couillard MD, University of Calgary Sara Hocker, MD, Mayo Clinic – Rochester Sudhir Datar, MBBS, Wake Forest Tia Chakraborty, MD, Spectrum Health
Keywords
- AI
- Acute Ischemic Stroke
- DELPHI
- Digital Twin
- Expert Consensus
- Neuro Critical Care
ASJC Scopus subject areas
- Clinical Neurology
