Using machine learning and centrifugal microfluidics at the point-of-need to predict clinical deterioration of patients with suspected sepsis within the first 24 h.

Par Conseil national de recherches du Canada

Téléchargement	Voir la version de l’auteur : Using machine learning and centrifugal microfluidics at the point-of-need to predict clinical deterioration of patients with suspected sepsis within the first 24 h. (PDF, 6.2 Mio)
DOI	Trouver le DOI : https://doi.org/10.1101/2024.10.08.24314844
Auteur	Rechercher : Malic, Lidija¹Identifiant ORCID : https://orcid.org/0000-0001-8594-9191; Rechercher : Zhang, Peter G. Y.Identifiant ORCID : https://orcid.org/0000-0003-0557-3572; Rechercher : Plant, Pamela Identifiant ORCID : https://orcid.org/0000-0002-3781-4337; Rechercher : Clime, Liviu¹Identifiant ORCID : https://orcid.org/0000-0002-1786-0803; Rechercher : Nassif, Christina¹; Rechercher : Da Fonte, Dillon¹; Rechercher : Haney, Evan E.; Rechercher : Moon, Byeong-Ui¹Identifiant ORCID : https://orcid.org/0000-0003-3791-7747; Rechercher : Sit, Victor¹; Rechercher : Brassard, Daniel¹Identifiant ORCID : https://orcid.org/0000-0001-5183-6455; Rechercher : Mournier, Maxence; Rechercher : Chircher, Eryn; Rechercher : Tsoporis, James T.; Rechercher : Falsafi, Reza; Rechercher : Bains, Manjeet; Rechercher : Baker, Andrew; Rechercher : Trahtemberg, Uriel Identifiant ORCID : https://orcid.org/0000-0001-9103-2494; Rechercher : Lukic, Ljuboje¹; Rechercher : Marshall, John C.; Rechercher : Geissler, Matthias¹Identifiant ORCID : https://orcid.org/0000-0002-8838-5528; Rechercher : Hancock, Robert E. W.Identifiant ORCID : https://orcid.org/0000-0001-5989-8503; Rechercher : Veres, Teodor¹Identifiant ORCID : https://orcid.org/0000-0002-9594-8042; Rechercher : dos Santos, Claudia C.Identifiant ORCID : https://orcid.org/0000-0002-6446-8791
Affiliation	Conseil national de recherches du Canada. Dispositifs médicaux
Format	Texte, Préimpression
Résumé	Sepsis is the bodys dysfunctional response to infection associated with organ failure. Delays in diagnosis have a substantial impact on survival. Herein, samples from 586 in-house patients were used in conjunction with machine learning and cross-validation to narrow a gene expression signature of immune cell reprogramming to predict clinical deterioration in patients with suspected sepsis within the first 24 hours (h) of clinical presentation using just six genes (Sepset). The accuracy of the test (~90% in early intensive care unit (ICU) and 70% in emergency room patients) was validated in 3,178 patients from existing independent cohorts. A real-time reverse transcriptase polymerase chain reaction (RT-PCR)-based test was shown to have a 98% sensitivity in >230 patients to predict worsening of the sequential organ failure scores or admission to the ICU within the first 24 h following Sepset detection. A stand-alone centrifugal microfluidic instrument that integrates the entire automated workflow for detection of the Sepset classifier in whole blood using digital droplet PCR was developed and tested. This PREcision meDIcine for CriTical care (PREDICT) system had a high sensitivity of 92%, specificity of 89%, and an overall accuracy of 88% in identifying the risk of imminent clinical deterioration in patients with suspected sepsis.
Date de publication	2024-10-28
Maison d’édition	medRxiv
Licence	Creative Commons, Attribution - Pas d'Œuvre dérivée 4.0 International (CC BY-ND 4.0) https://creativecommons.org/licenses/by-nd/4.0/deed.fr
Langue	anglais
Publications évaluées par des pairs	Non
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	cab9add5-0484-4ad2-a24d-534a111ce021
Enregistrement créé	2025-04-03
Enregistrement modifié	2025-04-10

Date de modification :: 2025-05-07