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<p>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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月27日

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  • Lidija Malic,
  • Peter G. Y. Zhang,
  • Pamela J. Plant,
  • Liviu Clime,
  • Christina Nassif,
  • Dillon Da 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,
  • Claudia C. dos Santos

摘要

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.