Summary
The ARTEMIs project aims to consolidate existing computational mechanistic and machine-learning models at different scales to deliver ‘virtual twins’ embedded in a clinical decision support system (CDSS). The CDSS will provide clinically meaningful information to clinicians, for a more personalised management of the whole spectrum of Metabolic Associated Fatty Liver Disease (MAFLD). MAFLD, with an estimated prevalence of about 25%, goes from an undetected sleeping disease, to inflammation (hepatitis), to fibrosis development (cirrhosis) and/or hepatocellular carcinoma (HCC), decompensated cirrhosis and HCC being the final stages of the disease. However, many MAFLD patients do not die from the liver disease itself, but from cardiovascular comorbidities or complications.
The ARTEMIs will contribute to the earlier management of MAFLD patients, by prognosing the development of more advanced forms of the disease and cardiovascular comorbidities, promoting active surveillance of patients at risk. The system will predict the impact of novel drug treatments or procedures, or simply better life habits. The system will therefore not only serve as a clinical decision aid tool, but also as an educational tool for patients, to promote better nutritional and lifestyle behaviors.
In more advanced forms of the disease, therapeutic interventions include TIPPS to manage portal hypertension, partial hepatectomy, partial or complete liver transplant. ARTEMIs will contribute to predict per- or post-intervention heart failure, building on existing microcirculation hemodynamics models.
The model developers will benefit from a large distributed patient cohort and data exploration environment to identify patterns in data, draw new theories on the liver-heart metabolic axis and validate the performance of their models.
The project includes a proof-of-concept feasibility study assessing the utility of the integrated virtual twins and CDSS in the clinical context.
The ARTEMIs will contribute to the earlier management of MAFLD patients, by prognosing the development of more advanced forms of the disease and cardiovascular comorbidities, promoting active surveillance of patients at risk. The system will predict the impact of novel drug treatments or procedures, or simply better life habits. The system will therefore not only serve as a clinical decision aid tool, but also as an educational tool for patients, to promote better nutritional and lifestyle behaviors.
In more advanced forms of the disease, therapeutic interventions include TIPPS to manage portal hypertension, partial hepatectomy, partial or complete liver transplant. ARTEMIs will contribute to predict per- or post-intervention heart failure, building on existing microcirculation hemodynamics models.
The model developers will benefit from a large distributed patient cohort and data exploration environment to identify patterns in data, draw new theories on the liver-heart metabolic axis and validate the performance of their models.
The project includes a proof-of-concept feasibility study assessing the utility of the integrated virtual twins and CDSS in the clinical context.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101136299 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2027 |
| Total budget - Public funding: | 9 365 096,00 Euro - 9 365 095,00 Euro |
Cordis data
Original description
The ARTEMIs project aims to consolidate existing computational mechanistic and machine-learning models at different scales to deliver ‘virtual twins’ embedded in a clinical decision support system (CDSS). The CDSS will provide clinically meaningful information to clinicians, for a more personalised management of the whole spectrum of Metabolic Associated Fatty Liver Disease (MAFLD). MAFLD, with an estimated prevalence of about 25%, goes from an undetected sleeping disease, to inflammation (hepatitis), to fibrosis development (cirrhosis) and/or hepatocellular carcinoma (HCC), decompensated cirrhosis and HCC being the final stages of the disease. However, many MAFLD patients do not die from the liver disease itself, but from cardiovascular comorbidities or complications.The ARTEMIs will contribute to the earlier management of MAFLD patients, by prognosing the development of more advanced forms of the disease and cardiovascular comorbidities, promoting active surveillance of patients at risk. The system will predict the impact of novel drug treatments or procedures, or simply better life habits. The system will therefore not only serve as a clinical decision aid tool, but also as an educational tool for patients, to promote better nutritional and lifestyle behaviors.
In more advanced forms of the disease, therapeutic interventions include TIPPS to manage portal hypertension, partial hepatectomy, partial or complete liver transplant. ARTEMIs will contribute to predict per- or post-intervention heart failure, building on existing microcirculation hemodynamics models.
The model developers will benefit from a large distributed patient cohort and data exploration environment to identify patterns in data, draw new theories on the liver-heart metabolic axis and validate the performance of their models.
The project includes a proof-of-concept feasibility study assessing the utility of the integrated virtual twins and CDSS in the clinical context.
Status
SIGNEDCall topic
HORIZON-HLTH-2023-TOOL-05-03Update Date
12-03-2024
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Organisations
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| MATICAL INNOVATION SL (Coördinator)
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| ALBERT-LUDWIGS-UNIVERSITAET FREIBURG
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| ASSISTANCE PUBLIQUE HOPITAUX DE PARIS
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| BETTHERA S.R.O.
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| BOURNEMOUTH UNIVERSITY
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| CHARITE - UNIVERSITAETSMEDIZIN BERLIN
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| CLINIQUE UNIVERSITAIRE SAINT-LUC ASBL
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| DEUTSCHES KREBSFORSCHUNGSZENTRUM HEIDELBERG
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| EUROPEAN LIVER PATIENTS ASSOCIATION
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| FUNDACIO HOSPITAL UNIVERSITARI VALL D'HEBRON - INSTITUT DE RECERCA
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| FUNDACION PARA LA INVESTIGACION DEL HOSPITAL UNIVERSITARIO LA FE DE LA COMUNIDAD VALENCIANA
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| IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
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| INST CARDIOMETABOLISME NUTRITION ICAN
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| INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
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| MEDEXPRIM