A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis

Lidija Malic, Peter G. Y. Zhang, Pamela J. Plant, Liviu Clime, Christina Nassif, Dillon Fonte, Evan E. Haney, Byeong-Ui Moon, Victor Mun-Sing Sit, Daniel Brassard, Maxence Mounier, Eryn Churcher, James T. Tsoporis, Reza Falsafi, Manjeet Bains, Andrew Baker, Uriel Trahtemberg, Ljuboje Lukic, John C. Marshall, Matthias Geissler, Robert E. W. Hancock, Teodor Veres and Claudia C. Santos ()
Additional contact information
Lidija Malic: 75 de Mortagne Boulevard
Peter G. Y. Zhang: 420 – 730 View St
Pamela J. Plant: 30 Bond Street
Liviu Clime: 75 de Mortagne Boulevard
Christina Nassif: 75 de Mortagne Boulevard
Dillon Fonte: 75 de Mortagne Boulevard
Evan E. Haney: 420 – 730 View St
Byeong-Ui Moon: 75 de Mortagne Boulevard
Victor Mun-Sing Sit: 75 de Mortagne Boulevard
Daniel Brassard: 75 de Mortagne Boulevard
Maxence Mounier: 75 de Mortagne Boulevard
Eryn Churcher: 30 Bond Street
James T. Tsoporis: 30 Bond Street
Reza Falsafi: 232-2259 Lower Mall
Manjeet Bains: 232-2259 Lower Mall
Andrew Baker: 30 Bond Street
Uriel Trahtemberg: 30 Bond Street
Ljuboje Lukic: 75 de Mortagne Boulevard
John C. Marshall: 30 Bond Street
Matthias Geissler: 75 de Mortagne Boulevard
Robert E. W. Hancock: 420 – 730 View St
Teodor Veres: 75 de Mortagne Boulevard
Claudia C. Santos: 5 King’s College Rd

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays in diagnosis have substantial impact on survival. Herein, blood samples from 586 in-house patients with suspected sepsis are used in conjunction with machine learning and cross-validation to define a six-gene expression signature of immune cell reprogramming, termed Sepset, to predict clinical deterioration within the first 24 h (h) of clinical presentation. Prediction accuracy (~90% in early intensive care unit (ICU) and 70% in emergency room patients) is validated in 3178 patients from existing independent cohorts. A RT-PCR-based Sepset detection test shows a 94% sensitivity in 248 patients to predict worsening of the sequential organ failure assessment scores within the first 24 h. A stand-alone centrifugal microfluidic instrument that automates whole-blood Sepset classifier detection is tested, showing a sensitivity of 92%, and specificity of 89% in identifying the risk of clinical deterioration in patients with suspected sepsis.

Date: 2025
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DOI: 10.1038/s41467-025-59227-x

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