Multiplexed host immune response biosensor for rapid sepsis stratification and endotyping at point-of-care

Biosensors and Bioelectronics: X

Subramaniam Krishnan¹  Deborah A. Striegel²  Kevin L. Schully²  Shalini Prasad²  Danielle V. Clark³  Abha Sardesai⁴  Ambalika S. Tanak⁴  Sriram Muthukumar⁵ 

[1] Corresponding author. 1813 Audubon Pond Way, Allen, TX, 75013, USA.;Austere Environments Consortium for Enhanced Sepsis Outcomes (ACESO), Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA;Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Ft. Detrick, MD, USA;Department of Bioengineering, University of Texas at Dallas, TX, USA;EnLiSense LLC, 1813 Audubon Pond Way, Allen, TX, 75013, USA;

关键词: Sepsis endotyping;    Point-of-care biosensor;    Electrochemical sensing;    Multiplexed detection;    Machine learning;   

DOI  :  

来源: DOAJ

【 摘 要 】

Disease progression of sepsis has been perceived as a multifaceted phenomenon, considering the temporal host inflammatory response within individuals which requires early diagnosis. Herein, we present a multicohort analysis through temporal inflammatory biomarker profiling using Direct Electrochemical Technique Targeting (DETecT) sepsis device that measures and quantify cytokines (IL-6, IL-8, IL-10), chemokines (TRAIL, IP-10), and well-established inflammatory biomarkers (PCT, CRP) with a sample turnaround time of <5 min in small volume (<40 μL) patient plasma samples. The DETecT device positively correlated (r > 0.97) with the Luminex reference standard during clinical evaluation for a total of 124 sepsis patient samples. Low mean bias for all the biomarkers in Bland- Altman analysis indicated good agreement between the standard LUMINEX method and the developed DETecT sepsis device. We used the combinatorial power of rapidly measuring a panel of seven biomarkers, paired with a machine learning model, to effectively predict the patient outcomes when given two-time points in the early stages of sepsis. The device could predict patient mortality and recovery with over 92% accuracy by applying decision tree analysis. We envision this work would facilitate personalized treatment based on biomarker stratification to represent exactly where the patient belongs within the sepsis continuum. Measurable empirical data with a fast turnaround time would facilitate the DETecT sepsis device as a potential enabling technology that can play a crucial role in understanding sepsis prognosis and be leveraged for personalized therapeutics anywhere.

【 授权许可】

Unknown