Embracing complexity in sepsis
Abstract Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | Critical Care |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13054-023-04374-0 |
| _version_ | 1797864559663382528 |
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| author | Alex R. Schuurman Peter M. A. Sloot W. Joost Wiersinga Tom van der Poll |
| author_facet | Alex R. Schuurman Peter M. A. Sloot W. Joost Wiersinga Tom van der Poll |
| author_sort | Alex R. Schuurman |
| collection | DOAJ |
| description | Abstract Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally accepted that sepsis is very complex indeed, the concepts, approaches and methods that are necessary to understand this complexity remain underappreciated. In this perspective we view sepsis through the lens of complexity theory. We describe the concepts that support viewing sepsis as a state of a highly complex, non-linear and spatio-dynamic system. We argue that methods from the field of complex systems are pivotal for a fuller understanding of sepsis, and we highlight the progress that has been made over the last decades in this respect. Still, despite these considerable advancements, methods like computational modelling and network-based analyses continue to fly under the general scientific radar. We discuss what barriers contribute to this disconnect, and what we can do to embrace complexity with regards to measurements, research approaches and clinical applications. Specifically, we advocate a focus on longitudinal, more continuous biological data collection in sepsis. Understanding the complexity of sepsis will require a huge multidisciplinary effort, in which computational approaches derived from complex systems science must be supported by, and integrated with, biological data. Such integration could finetune computational models, guide validation experiments, and identify key pathways that could be targeted to modulate the system to the benefit of the host. We offer an example for immunological predictive modelling, which may inform agile trials that could be adjusted throughout the trajectory of disease. Overall, we argue that we should expand our current mental frameworks of sepsis, and embrace nonlinear, system-based thinking in order to move the field forward. |
| first_indexed | 2024-04-09T22:54:57Z |
| format | Article |
| id | doaj.art-65d359aab5d54c32a857bffb22079ad1 |
| institution | Directory Open Access Journal |
| issn | 1364-8535 |
| language | English |
| last_indexed | 2024-04-09T22:54:57Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Critical Care |
| spelling | doaj.art-65d359aab5d54c32a857bffb22079ad12023-03-22T11:20:59ZengBMCCritical Care1364-85352023-03-012711810.1186/s13054-023-04374-0Embracing complexity in sepsisAlex R. Schuurman0Peter M. A. Sloot1W. Joost Wiersinga2Tom van der Poll3Centre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of AmsterdamInstitute for Advanced Study, University of AmsterdamCentre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of AmsterdamCentre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of AmsterdamAbstract Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally accepted that sepsis is very complex indeed, the concepts, approaches and methods that are necessary to understand this complexity remain underappreciated. In this perspective we view sepsis through the lens of complexity theory. We describe the concepts that support viewing sepsis as a state of a highly complex, non-linear and spatio-dynamic system. We argue that methods from the field of complex systems are pivotal for a fuller understanding of sepsis, and we highlight the progress that has been made over the last decades in this respect. Still, despite these considerable advancements, methods like computational modelling and network-based analyses continue to fly under the general scientific radar. We discuss what barriers contribute to this disconnect, and what we can do to embrace complexity with regards to measurements, research approaches and clinical applications. Specifically, we advocate a focus on longitudinal, more continuous biological data collection in sepsis. Understanding the complexity of sepsis will require a huge multidisciplinary effort, in which computational approaches derived from complex systems science must be supported by, and integrated with, biological data. Such integration could finetune computational models, guide validation experiments, and identify key pathways that could be targeted to modulate the system to the benefit of the host. We offer an example for immunological predictive modelling, which may inform agile trials that could be adjusted throughout the trajectory of disease. Overall, we argue that we should expand our current mental frameworks of sepsis, and embrace nonlinear, system-based thinking in order to move the field forward.https://doi.org/10.1186/s13054-023-04374-0SepsisComplexityHost responseNon-linearityComputational models |
| spellingShingle | Alex R. Schuurman Peter M. A. Sloot W. Joost Wiersinga Tom van der Poll Embracing complexity in sepsis Critical Care Sepsis Complexity Host response Non-linearity Computational models |
| title | Embracing complexity in sepsis |
| title_full | Embracing complexity in sepsis |
| title_fullStr | Embracing complexity in sepsis |
| title_full_unstemmed | Embracing complexity in sepsis |
| title_short | Embracing complexity in sepsis |
| title_sort | embracing complexity in sepsis |
| topic | Sepsis Complexity Host response Non-linearity Computational models |
| url | https://doi.org/10.1186/s13054-023-04374-0 |
| work_keys_str_mv | AT alexrschuurman embracingcomplexityinsepsis AT petermasloot embracingcomplexityinsepsis AT wjoostwiersinga embracingcomplexityinsepsis AT tomvanderpoll embracingcomplexityinsepsis |