Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit
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 climat...
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Neurology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12883-023-03192-9 |
| _version_ | 1797840917038628864 |
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| author | Johnny Dang Amos Lal Amy Montgomery Laure Flurin John Litell Ognjen Gajic Alejandro Rabinstein on behalf of The Digital Twin Platform for education, research, and healthcare delivery investigator group |
| author_facet | Johnny Dang Amos Lal Amy Montgomery Laure Flurin John Litell Ognjen Gajic Alejandro Rabinstein on behalf of The Digital Twin Platform for education, research, and healthcare delivery investigator group |
| author_sort | Johnny Dang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T16:22:39Z |
| format | Article |
| id | doaj.art-425a43355c794e26be20aeaeb30cf00b |
| institution | Directory Open Access Journal |
| issn | 1471-2377 |
| language | English |
| last_indexed | 2024-04-09T16:22:39Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Neurology |
| spelling | doaj.art-425a43355c794e26be20aeaeb30cf00b2023-04-23T11:21:03ZengBMCBMC Neurology1471-23772023-04-0123111210.1186/s12883-023-03192-9Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unitJohnny Dang0Amos Lal1Amy Montgomery2Laure Flurin3John Litell4Ognjen Gajic5Alejandro Rabinstein6on behalf of The Digital Twin Platform for education, research, and healthcare delivery investigator groupDepartment of Neurology, Cleveland ClinicDivision of Pulmonary and Critical Care Medicine, Mayo ClinicDepartment of Medicine, Mayo ClinicInfectious Diseases Research Laboratory, Mayo ClinicAbbott Northwestern Emergency Critical CareDivision of Pulmonary and Critical Care Medicine, Mayo ClinicDepartment of Neurology, Mayo ClinicAbstract 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.https://doi.org/10.1186/s12883-023-03192-9Neuro Critical CareAIDELPHIExpert ConsensusAcute Ischemic StrokeDigital Twin |
| spellingShingle | Johnny Dang Amos Lal Amy Montgomery Laure Flurin John Litell Ognjen Gajic Alejandro Rabinstein on behalf of The Digital Twin Platform for education, research, and healthcare delivery investigator group Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit BMC Neurology Neuro Critical Care AI DELPHI Expert Consensus Acute Ischemic Stroke Digital Twin |
| title | Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit |
| title_full | Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit |
| title_fullStr | Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit |
| title_full_unstemmed | Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit |
| title_short | Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit |
| title_sort | developing delphi expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit |
| topic | Neuro Critical Care AI DELPHI Expert Consensus Acute Ischemic Stroke Digital Twin |
| url | https://doi.org/10.1186/s12883-023-03192-9 |
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