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.

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. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/24314844v2_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@82f577org.highwire.dtl.DTLVardef@1c18921org.highwire.dtl.DTLVardef@111f119org.highwire.dtl.DTLVardef@ebbb87_HPS_FORMAT_FIGEXP M_FIG Description of Graphic AbstractFeature reduction and development of a gene classifier that predicts deterioration-risk-groups in patients starts with in-house RNA sequencing data from patient collected from a heterogenous cohort of patients with suspected sepsis (top left) to reduce our original published gene signature down to 6-genes (Sepset), for which expression could be related to 2 housekeeping genes. Feature selection was performed using machine learning (ML) and AI and the classifier validated in samples from published transcriptomic studies. Molecular assay is then developed by designing and testing primer/probe sequences specific to the target genes using digital droplet PCR. In parallel, sample-to-answer microfluidic platform and cartridges are developed (bottom right) and analytical performance of multiplex quantitative assay is tested. Prognostic enrichment is obtained by analyzing the results using ML algorithm to determine the percent likelihood of significant clinical deterioration within the immediate next 24 h. The deployment of PREDICT platform (center) at the point-of-care is anticipated to aid in triage and management of prospective sepsis within the first 3 h of clinical presentation. C_FIG