Nature communications · 2025
A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Malic Lidija, Zhang Peter G Y, Plant Pamela J, Clime Liviu, Nassif Christina, Da Fonte Dillon, Haney Evan E, Moon Byeong-Ui, Sit Victor Mun-Sing, Brassard Daniel, Mounier Maxence, Churcher Eryn, Tsoporis James T, Falsafi Reza, Bains Manjeet, Baker Andrew, Trahtemberg Uriel, Lukic Ljuboje, Marshall John C, Geissler Matthias, Hancock Robert E W, Veres Teodor, Dos Santos Claudia C
Аннотация
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.