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

Author

Listed:
  • Lidija Malic

    (75 de Mortagne Boulevard
    5 King’s College Rd
    Suite 316)

  • 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
    Galilee Medical Center
    Bar Ilan University)

  • 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
    232-2259 Lower Mall)

  • Teodor Veres

    (75 de Mortagne Boulevard
    5 King’s College Rd
    5 King’s College Road)

  • Claudia C. Santos

    (5 King’s College Rd
    420 – 730 View St)

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.

Suggested Citation

  • 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 Chu, 2025. "A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59227-x
    DOI: 10.1038/s41467-025-59227-x
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    References listed on IDEAS

    as
    1. Timothy E. Sweeney & Thanneer M. Perumal & Ricardo Henao & Marshall Nichols & Judith A. Howrylak & Augustine M. Choi & Jesús F. Bermejo-Martin & Raquel Almansa & Eduardo Tamayo & Emma E. Davenport & K, 2018. "A community approach to mortality prediction in sepsis via gene expression analysis," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    2. Dirk Janasek & Joachim Franzke & Andreas Manz, 2006. "Scaling and the design of miniaturized chemical-analysis systems," Nature, Nature, vol. 442(7101), pages 374-380, July.
    3. Claire L. Smith & Paul Dickinson & Thorsten Forster & Marie Craigon & Alan Ross & Mizanur R. Khondoker & Rebecca France & Alasdair Ivens & David J. Lynn & Judith Orme & Allan Jackson & Paul Lacaze & K, 2014. "Identification of a human neonatal immune-metabolic network associated with bacterial infection," Nature Communications, Nature, vol. 5(1), pages 1-15, December.
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