Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response during the First 24 Hours of Sepsis

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dc.contributor.authorLal, Amos
dc.contributor.authorLi, Guangxi
dc.contributor.authorCubro, Edin
dc.contributor.authorChalmers, Sarah
dc.contributor.authorLi, Heyi
dc.contributor.authorHerasevich, Vitaly
dc.contributor.authorDong, Yue
dc.date.accessioned2025-02-06T18:01:20Z
dc.date.available2025-02-06T18:01:20Z
dc.date.issued2020
dc.departmentAltınbaş Üniversitesien_US
dc.description.abstractObjectives: To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis. Design: Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin. Setting: Medical ICU of a large quaternary-care academic medical center in the United States. Patients or Subjects: Adult (> 18 year yr old), medical ICU patients were included in the study. Interventions: No additional interventions were made beyond the standard of care for this study. Measurements and Main Results: During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54-66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0-14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%). Conclusions: We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients. © 2020 Authors. All rights reserved.en_US
dc.description.sponsorshipMayo Clinicen_US
dc.identifier.doi10.1097/CCE.0000000000000249
dc.identifier.issn2639-8028
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85171786118
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpageE0249en_US
dc.identifier.urihttps://doi.org/10.1097/CCE.0000000000000249
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5328
dc.identifier.volume2en_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherLippincott Williams and Wilkinsen_US
dc.relation.ispartofCritical Care Explorationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_Scopus_20250206
dc.subjectartificial intelligenceen_US
dc.subjectcritical careen_US
dc.subjectdigital twinen_US
dc.subjectdirected acyclic graphen_US
dc.subjectorgan failureen_US
dc.titleDevelopment and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response during the First 24 Hours of Sepsisen_US
dc.typeArticleen_US

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