Near-Infrared Spectroscopy Usefulness in Validation of Hyperventilation Test.

Background: The hyperventilation test is used in clinical practice for diagnosis and therapeutic purposes; however, in the absence of a standardized protocol, the procedure varies significantly, predisposing tested subjects to risks such as cerebral hypoxia and ischemia. Near-infrared spectroscopy (NIRS), a noninvasive technique performed for cerebral oximetry monitoring, was used in the present study to identify the minimum decrease in the end-tidal CO2 (ETCO2) during hyperventilation necessary to induce changes on NIRS. Materials and Methods: We recruited 46 volunteers with no preexisting medical conditions. Each subject was asked to breathe at a baseline rate (8−14 breaths/min) for 2 min and then to hyperventilate at a double respiratory rate for the next 4 min. The parameters recorded during the procedure were the regional cerebral oxyhemoglobin and deoxyhemoglobin concentrations via NIRS, ETCO2, and the respiratory rate. Results: During hyperventilation, ETCO2 values dropped (31.4 ± 12.2%) vs. baseline in all subjects. Changes in cerebral oximetry were observed only in those subjects (n = 30) who registered a decrease (%) in ETCO2 of 37.58 ± 10.34%, but not in the subjects (n = 16) for which the decrease in ETCO2 was 20.31 ± 5.6%. According to AUC-ROC analysis, a cutoff value of ETCO2 decrease >26% was found to predict changes in oximetry (AUC-ROC = 0.93, p < 0.0001). Seven subjects reported symptoms, such as dizziness, vertigo, and numbness, throughout the procedure. Conclusions: The rise in the respiratory rate alone cannot effectively predict the occurrence of a cerebral vasoconstrictor response induced by hyperventilation, and synchronous ETCO2 and cerebral oximetry monitoring could be used to validate this clinical test. NIRS seems to be a useful tool in predicting vasoconstriction following hyperventilation.

Model-driven gas exchange monitoring in the critically ill.

Understanding pulmonary gas exchange performance is a dynamic process which, depending on clinical context, exhibits different levels of complexity. Global tools such as tension-based indexes yield clinically crucial information under very specific conditions. Yet, accurate mechanistic insight can only originate in model-based tools. One-parameter models such as shunt or dead space are well established in clinical practice whilst two or three-parameter models have just been advanced and their role is yet to be delineated. Although the latter provide superior accuracy, this comes at the cost of increased complexity and possibly the need for invasive data sets. Modelling gas exchange enables a quantitative and physiologically-driven management of patients with lung failure. Assumptions are inherent to each tool and can clinically mislead if not accounted for. Thorough understanding of their subjacent theoretical construct is a prerequisite for their judicious use. This manuscript aims to describe current gas exchange monitoring tools, with special reference to their mathematical framework and constituent pitfalls. A unifying perspective on their clinical role is proposed.