Oyelade, Tope;
(2024)
Network approach to prognosis in cirrhosis.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Definitive treatment following decompensation of cirrhosis is limited to liver transplantation based on prognosis. Although useful, current prognostic models do not consider the interaction between organ systems and treat them as isolated units. Approaches to improve these models through the addition of more biomarkers still does not resolve the limitations of the simplistic approach. This work tests the hypothesis that organ systems' functional connectivity is reduced in cirrhosis and may hold independent prognostic values. Indeed, the prognostic values of physiologically integrative approaches such as heart rate variability measures are well established. However, while integrative physiological indices such as HRV analysis have prognostic value in cirrhosis, they do not reveal details of organ systems network interactions potentially driving patient outcomes. To assess the functional connectivity of organs, I used a network physiology approach and developed a novel method for physiological network mapping at the individual patient’s level to assess the interaction between routine clinical/biochemical biomarkers. The method is known as Parenclitic network analysis and measures the deviation of a pair of patient variables from the expected relationship based on a model population (e.g., healthy, survivors, treatment responders, etc.). The results show that parenclitic network mapping can predict mortality independent of MELD (Model for End-stage Liver Disease) in two independent cohorts of patients with decompensated cirrhosis. Also, this novel method was found to predict response to targeted albumin therapy in a large, multicentre group of patients admitted to the hospital for decompensated cirrhosis. Finally, the Parenclitic network analysis predicted prognosis in paracetamol-induced acute liver failure patients independent of the sequential organ failure score (SOFA) as well as the King’s College Criteria (KCC). Importantly, the parenclitic network analysis is based on routine data and significantly improved the prognostic models currently used in cirrhosis and acute liver failure. In sum, this thesis shows that organ system decoupling is linked with survival and may predict response to therapy in patients with liver disease.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Network approach to prognosis in cirrhosis |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10197767 |
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