TATAA Research Spotlight
New paper from Nature.com – ‘Predicting sepsis using a combination of clinical information and molecular immune markers sampled in the ambulance’
(In this research, genes were analyzed using assays designed and validated by TATAA Biocenter. The qPCR was performed with TATAA SYBR®GrandMaster Mix.)
Abstract (Edited from Nature.com)
Sepsis, a life-threatening condition resulting from the body’s exaggerated response to an infection, affects millions people a year globally. For this reason, WHO has called for a global action on sepsis and early diagnosis is one crucial aspect to consider for improved care of the septic patient.
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.
In a prospective cohort study in Stockholm, Sweden between 2017 and 2018, researchers aimed to assess the additional predictive value of immunological molecular markers in sepsis.
The study involved 551 adult patients with suspected infection in ambulances. Initially, 74 molecules and 15 genes related to inflammation were evaluated, and later, 12 selected molecules were examined in combination with existing clinical parameter-based screening tools. Machine learning algorithms were used for prediction models, focusing on AUC. However, the inclusion of molecular variables alone did not significantly enhance predictive values, with AUCs ranging from 0.65 to 0.70. Notably, certain immune mediators like IL-1Ra, IL-17A, CCL19, CX3CL1, and TNF were significantly higher in septic patients.
Combining molecular immune markers with clinical parameters failed to significantly improve predictive values, likely due to multicollinearity. Some sepsis patients were consistently misclassified, indicating a need for stratification based on specific parameters to enhance early sepsis diagnosis.
Read the whole article here
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