| Title: | Non-invasive breath monitoring with eNose does not improve glucose diagnostics in critically ill patients in comparison to continuous glucose monitoring in blood |
| Author(s): | Leopold JH; Bos LDJ; Colombo C; Sterk PJ; Schultz MJ; Abu-Hanna A; |
| Address: | "Department of Intensive Care, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands" |
| ISSN/ISBN: | 1752-7163 (Electronic) 1752-7155 (Linking) |
| 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: | "Blood Glucose/*analysis Breath Tests/*methods *Critical Illness *Electronic Nose Exhalation Humans Male Monitoring, Physiologic/*methods Multivariate Analysis Volatile Organic Compounds/analysis;" |
| Notes: | "MedlineLeopold, Jan Hendrik Bos, Lieuwe D J Colombo, Camilla Sterk, Peter J Schultz, Marcus J Abu-Hanna, Ameen eng Comparative Study England 2017/03/07 J Breath Res. 2017 Apr 7; 11(2):026002. doi: 10.1088/1752-7163/aa6488" |
