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Predicting sepsis using a combination of clinical information and molecular immune markers sampled in the ambulance
Örebro University, School of Medical Sciences.
Örebro University, School of Medical Sciences.ORCID iD: 0000-0002-6045-4800
Gustavsbergs Vårdcentral, Gustavsberg, Stockholm, Sweden.
Örebro University, School of Medical Sciences.ORCID iD: 0009-0008-1571-0044
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2023 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 14917Article in journal (Refereed) Published
Abstract [en]

Sepsis is a time dependent condition. Screening tools based on clinical parameters have been shown to increase the identification of sepsis. The aim of current study was to evaluate the additional predictive value of immunological molecular markers to our previously developed prehospital screening tools. This is a prospective cohort study of 551 adult patients with suspected infection in the ambulance setting of Stockholm, Sweden between 2017 and 2018. Initially, 74 molecules and 15 genes related to inflammation were evaluated in a screening cohort of 46 patients with outcome sepsis and 50 patients with outcome infection no sepsis. Next, 12 selected molecules, as potentially synergistic predictors, were evaluated in combination with our previously developed screening tools based on clinical parameters in a prediction cohort (n = 455). Seven different algorithms with nested cross-validation were used in the machine learning of the prediction models. Model performances were compared using posterior distributions of average area under the receiver operating characteristic (ROC) curve (AUC) and difference in AUCs. Model variable importance was assessed by permutation of variable values, scoring loss of classification as metric and with model-specific weights when applicable. When comparing the screening tools with and without added molecular variables, and their interactions, the molecules per se did not increase the predictive values. Prediction models based on the molecular variables alone showed a performance in terms of AUCs between 0.65 and 0.70. Among the molecular variables, IL-1Ra, IL-17A, CCL19, CX3CL1 and TNF were significantly higher in septic patients compared to the infection non-sepsis group. Combing immunological molecular markers with clinical parameters did not increase the predictive values of the screening tools, most likely due to the high multicollinearity of temperature and some of the markers. A group of sepsis patients was consistently miss-classified in our prediction models, due to milder symptoms as well as lower expression levels of the investigated immune mediators. This indicates a need of stratifying septic patients with a priori knowledge of certain clinical and molecular parameters in order to improve prediction for early sepsis diagnosis.Trial registration: NCT03249597. Registered 15 August 2017.

Place, publisher, year, edition, pages
Nature Portfolio , 2023. Vol. 13, no 1, article id 14917
National Category
Infectious Medicine
Identifiers
URN: urn:nbn:se:oru:diva-108188DOI: 10.1038/s41598-023-42081-6ISI: 001066443900002PubMedID: 37691028Scopus ID: 2-s2.0-85170487168OAI: oai:DiVA.org:oru-108188DiVA, id: diva2:1795746
Funder
Örebro UniversityNyckelfondenLaerdal Foundation for Acute MedicineKnowledge Foundation, 20160044 20200017
Note

This study was supported by grants from Nyckelfonden, Laerdal, Falck Foundation, Knowledge Foundation (20160044, 20200017), the Emergency Department of Södersjukhuset, Stockholm, and Örebro University.

Available from: 2023-09-11 Created: 2023-09-11 Last updated: 2024-01-16Bibliographically approved

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Tuerxun, KayaEklund, DanielDannenberg, KatharinaKruse, RobertSärndahl, EvaKurland, Lisa

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CiteExportLink to record
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