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

Abstract Introduction: Sepsis is the body's dysfunctional response to infection associated with organ failure. Delays in diagnosis have a substantial impact on survival. Rationale: Lab-on-a-chip (LOC) technologies have the potential to advance care. We have previously shown extraction and detection of molecular markers using a centrifugal-based LOC system. In parallel, a 99 gene signature able to predict clinical deterioration based on the emergence of a cellular reprogramming profile associated with the inability of cells to respond to pathogens has been published. This signature was pathogen agnostic, predictive of immunological endotypes and severity outcomes, but too large for point of care (POC) testing.Methods: Here, we hypothesized that a reduced signature, would retain the ability to predict clinical deterioration in patients with suspected sepsis. Prospective samples from 586 patients were used in conjunction with machine learning and cross-validation to narrow the 99-gene expression signature to just six genes (Sepset). The ability of the signature to identify patients with sepsis was validated in nine published studies. The ability of the signature to predict clinical deterioration in patients with suspected sepsis within the first 24 hours (h) of clinical presentation was determined by routine semi-quantitative PCR (N=248). A compact prototype instrument, the PREcision meDIcine for CriTical care (PREDICT) device, that detects the Sepset classifier using digital droplet PCR in less than 3 h using 50 µL of whole blood was developed and its analytical performance tested in an independent cohort of label-free patients with suspected sepsis (N=30).Results: The accuracy of the Sepset signature (∼90% in early intensive care unit (ICU) and 70% in emergency room patients) was validated in 3,178 patients from existing independent cohorts. RT-PCR showed 98% sensitivity to predict worsening of the sequential organ failure scores or admission to the ICU within the first 24 h following Sepset detection in 248 independent patients. The PREDICT prototype was developed as a stand-alone centrifugal microfluidic instrument that integrates the automated workflow for detection of the Sepset classifier in whole blood using digital droplet PCR. PREDICT 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.Conclusions: The current study describes the development of a molecular risk classifier of clinical deterioration and onset of sepsis, and a novel POC device to measure this at the bedside, as well as the proof-of-concept demonstration using real-world patient samples.

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