The smell of sepsis: Electronic nose measurements improve early recognition of sepsis in the ED
Aim: To investigate whether eNose technology can identify sepsis among patients with suspected infection in the ED
Take home message: eNose measurements can identify sepsis AUC 0.78 (95 % CI: 0.69–0.87) and AUC 0.83 (95 % CI 0.71-0.94) in a validation cohort. eNose technology shows promise as a non-invasive decision support tool for early sepsis detection at the ED bed-side.
Introduction
Early recognition of sepsis is essential for timely initiation of adequate care. However, this is challenging as signs and symptoms may be absent or nonspecific. The cascade of events leading to organ failure in sepsis is characterized by immune-metabolic alterations. Volatile organic compounds (VOCs) are metabolic byproducts released in expired air. We hypothesize that measuring the VOC profile using electronic nose technology (eNose) could improve early recognition of sepsis.
Methods
In this cohort study, bedside eNose measurements were collected prospectively from ED patients with suspected infections. Sepsis diagnosis was retrospectively defined based on Sepsis-3 criteria. Breath analysis was conducted using the SpiroNose®, following a standardized maneuver. eNose sensor data were used in a discriminant analysis to evaluate the predictive performance for early sepsis recognition. The dataset was randomly split into training (67%) and validation (33%) subsets. The derived discriminant function from the training subset was then applied to classify new observations in the validation subset. Model performance was evaluated using receiver operating characteristic (ROC) curves and predictive values.
Results
A total of 160 eNose measurements were available for analysis. eNose analysis of exhaled breath achieved an area under the ROC (AUROC) of 0.78 (95 % CI: 0.69–0.87) for diagnosing sepsis, with a sensitivity of 72%, specificity of 73%, and an overall accuracy of 73%. Testing this model in an independent cohort resulted in an AUC of 0.83 (95 % CI: 0.71–0.94), sensitivity of 71%, specificity of 83%, and an accuracy of 80%.
Discussion
This study highlights the potential of eNose technology to detect sepsis in patients presenting to the emergency department (ED) with a suspected infection, using bedside VOC measurements. This new diagnostic model outperformed existing sepsis screening tools, suggesting that this approach could significantly improve early sepsis detection in the ED setting. Further research is needed to evaluate the broader applicability of eNose technology across diverse patient populations and clinical environments.
