Non-invasive breath monitoring with eNose does not improve glucose diagnostics in critically ill patients in comparison to continuous glucose monitoring in blood
- 7 April 2017
- journal article
- research article
- Published by IOP Publishing in Journal of Breath Research
- Vol. 11 (2) , 026002
Abstract
Continuous glucose monitoring (CGM) can be beneficial in critically ill patients. Current CGM devices rely on subcutaneous or blood plasma glucose measurements and consequently there is an increased risk of infections and the possibility of loss of blood with each measurement. A potential method to continuously and non-invasively measure blood glucose levels is using exhaled breath. A correlation between blood glucose levels and volatile organic compounds (VOCs) in the exhaled breath was already reported. VOCs can be analyzed continuously using a so-called electronic nose (eNose). We hypothesize that continuous exhaled breath analysis using an eNose can be used to accurately predict blood glucose levels in intubated, mechanically ventilated ICU-patients. Mechanically ventilated patients whose blood glucose concentration was monitored with a CGM device were eligible. An eNose with four metal oxide sensors was used to continuously measure changes in exhaled breath. After pre-processing the data, several regression models were trained, consisting of: (1) only eNose sensor values; (2) only the 1st and 2nd principal components (PC) of eNose values; (3) eNose sensor values and last known blood glucose value as random effect; (4) 1st and 2nd PC of eNose sensor values and CGM value of one minute ago as fixed effect; (5) CGM value of one minute ago as fixed effect. Model performance was measured using the R 2 value, the akaike information criterion and the Clarke error grid. Twenty-three patients were included in the study and 1165 hours of measurements were collected. Performance was low in models 1, 2 and 3 with a mean R 2 of 0.07 [95%-CI: 0.00–0.28], 0.10 [95%-CI: 0.00–0.40] and 0.30 [0.02–0.79], respectively. Performance in models 4 and 5 was better with a mean R 2 of 0.77 [0.02–1.00]. Subsequently, eNose data in model 4 had no added value over using CGM only in model 5. Continuous exhaled breath analysis using this eNose cannot be used to accurately predict blood glucose levels in intubated, mechanically ventilated ICU-patients.Keywords
This publication has 15 references indexed in Scilit:
- Sweet Spot: Glucose Control in the Intensive Care UnitSeminars in Respiratory and Critical Care Medicine, 2016
- Comparison of classification methods in breath analysis by electronic noseJournal of Breath Research, 2015
- Exhaled Molecular Fingerprinting in Diagnosis and Monitoring: Validating Volatile PromisesTrends in Molecular Medicine, 2015
- Glucose prediction by analysis of exhaled metabolites – a systematic reviewBMC Anesthesiology, 2014
- Intensive insulin therapy in the ICU—reconciling the evidenceNature Reviews Endocrinology, 2012
- Clinical review: Strict or loose glycemic control in critically ill patients - implementing best available evidence from randomized controlled trialsCritical Care, 2010
- Intensive versus Conventional Glucose Control in Critically Ill PatientsNew England Journal of Medicine, 2009
- Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study dataCanadian Medical Association Journal, 2009
- Admission Hyperglycemia Is Predictive of Outcome in Critically Ill Trauma Patients2005
- Intensive Insulin Therapy in Critically Ill PatientsNew England Journal of Medicine, 2001