Breath VOCs could help with early detection of Hepatic Encephalopathy

Earlier detection can prevent liver disease progression and hospitalization.

Publication information: R. P. Arasaradnam et al. Breathomics—exhaled volatile organic compound analysis
to detect hepatic encephalopathy: a pilot study, J. Breath Res. 10 (2016) 016012; DOI: 10.1088/1752-7155/10/1/016012

Disease Area: Hepatic Encephalopathy

Application: Early Detection

Sample medium: Breath

Products: Lonestar VOC Analyzer

Analysis approach: FAIMS


  • Diagnosing hepatic encephalopathy earlier could prevent liver disease progression and hospitalization.
  • Breath samples were captured from 42 patients (including 20 controls) and analysed using FAIMS. 
  • FAIMS was able to classify HE patients from controls with an AUROC of 0.84 and so shows clear potential as a a diagnostic aid.


Hepatic encephalopathy (HE) is a neuropsychiatric condition which occurs when the liver cannot adequately remove toxins from the blood. It often occurs suddenly in people with acute liver failure but is more often seen in those with chronic liver disease. The prevalence of minimal HE (the mildest detectable class of HE under the West Haven criteria) is reported in 30 – 84% of patients with liver cirrhosis. This has profound effects on daily functioning and nearly 50% of minimal HE patients may be unfit to maintain employment. The condition can be treated effectively using Rifaximin, and earlier detection can prevent liver disease progression and hospitalization.

In this pilot study, the chemical fingerprint of breath samples from patients with HE and controls was analysed using a Lonestar FAIMS (field asymmetric ion mobility spectrometry) instrument fitted with an ultra violet ionisation source. 42 patients were recruited; 22 patients with HE (13 covert and 9 with overt HE) and 20 healthy controls.

Extracted data was analysed using a pipeline based on a 2D wavelet transform and threshold to remove background noise. This was followed by feature selection to identify key variables (using a Wilcoxon rank-sum test applied separately to each feature), with the resultant feature set used to separately train four classifiers (sparse logistic regression, Random Forest, Support Vector Machine and Gaussian Process).

FAIMS analysis of exhaled VOCs was able to classify HE patients from controls with a sensitivity and specificity of 0.88 (0.73–0.95) and 0.68 (0.51–0.81) respectively, AUROC 0.84 (0.75–0.93).

This pilot study provides evidence that breath VOC biomarker analysis has potential as a diagnostic aid in distinguishing HE of all grades from healthy control subjects. It may also have potential as an aid to distinguish covert HE from overt HE.

Receiver operator curve for distinguishing HE from controls
ROC plot for distinguishing HE from controls


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