PSSS – Personalized Swiss Sepsis Study: Detection and modelling of sepsis using machine learning to analyse continuous ICU monitoring, laboratory, microbiology, and -omics data for personalized sepsis management
Bacterial infection progressing to sepsis is associated with high morbidity, mortality, reduction of quality of life in survivors and health care costs. The course and outcome of sepsis is highly heterogeneous and depends on the causative pathogen and varies from patient to patient. The individual outcome is significantly influenced by various complex host-and pathogen-related factors. Increasing rates of multi-drug resistant bacteria further complicate the diagnostic process and clinical management and may lead to treatment failure. Therefore, patients with sepsis would greatly benefit from personalized diagnostic assessment and treatment strategies evaluating and integrating the host and the pathogen.
The PSSS Driver project aims to build an interoperable infrastructure among the intensive care units of the Swiss university hospitals and several research groups, to gather complex information on the host and pathogen during the entire course of a sepsis. The integration of continuous monitoring data from intensive care units will result in digital biomarkers. Combined with the molecular data from bacterial pathogens (metagenomics and whole genome sequencing) and from the host (metabolomics, immunophenotyping, genotyping), new avenues for sepsis research will emerge. These very comprehensive and complex data will be combined via the SPHN data hubs to enable multi-dimensional analyses through machine learning. The goal is to recognize a bacterial sepsis earlier and to predict its course more precisely than currently possible for an individual patient.
Date: Wednesday, 30 September 2020