Microvascular effects of oxygen and carbon dioxide measured by vascular occlusion test in healthy volunteers.

BACKGROUND: Changes in near-infrared spectroscopy-derived regional tissue oxygen saturation (StO2) during a vascular occlusion test (VOT; ischemic provocation of microcirculation by rapid inflation and deflation of a tourniquet) allow estimating peripheral tissue O2 consumption (desaturation slope; DS), vascular reactivity (recovery slope; RS) and post-ischemic hyperperfusion (AUC-H). The effects of isolated alterations in the inspiratory fraction of O2 (FiO2) and changes in expiratory CO2 remain to be elucidated. Therefore, in this secondary analysis we determined the effects of standardized isolated instances of hypoxia, hyperoxia, hypocapnia and hypercapnia on the VOT-induced StO2 changes in healthy volunteers (n = 20) to establish reference values for future physiological studies. METHODS: StO2 was measured on the thenar muscle. Multiple VOTs were performed in a standardized manner: i.e. at room air (baseline), during hyperoxia (FiO2 1.0), mild hypoxia (FiO2 ≈ 0.11), and after a second baseline, during hypocapnia (end-tidal CO2 (etCO2) 2.5-3.0 vol%) and hypercapnia (etCO2 7.0-7.5 vol%) at room air. Differences in DS, RS, and AUC-H were tested using repeated-measures ANOVA. RESULTS: DS and RS remained constant during all applied conditions. AUC-H after hypoxia was smaller compared to hyperoxia (963 %*sec vs hyperoxia 1702 %*sec, P = 0.005), while there was no difference in AUC-H duration between hypoxia and baseline. The StO2 peak (after tourniquet deflation) during hypoxia was lower compared to baseline and hyperoxia (92 % vs 94 % and 98 %, P < 0.001). CONCLUSION: We conclude that in healthy volunteers at rest, common situations observed during anesthesia and intensive care such as exposure to hypoxia, hyperoxia, hypocapnia, or hypercapnia, did not affect peripheral tissue O2 consumption and vascular reactivity as assessed by VOT-induced changes in StO2. These observations may serve as reference values for future physiological studies. TRIAL REGISTRATION: This study represents a secondary analysis of an original study which has been registered at ClinicalTrials.gov nr: NCT02561052.

Continuous noninvasive pulse wave analysis using finger cuff technologies for arterial blood pressure and cardiac output monitoring in perioperative and intensive care medicine: a systematic review and meta-analysis.

BACKGROUND: Finger cuff technologies allow continuous noninvasive arterial blood pressure (AP) and cardiac output/index (CO/CI) monitoring. METHODS: We performed a meta-analysis of studies comparing finger cuff-derived AP and CO/CI measurements with invasive measurements in surgical or critically ill patients. We calculated overall random effects model-derived pooled estimates of the mean of the differences and of the percentage error (PE; CO/CI studies) with 95%-confidence intervals (95%-CI), pooled 95%-limits of agreement (95%-LOA), Cochran's Q and I2 (for heterogeneity). RESULTS: The pooled mean of the differences (95%-CI) was 4.2 (2.8 to 5.62) mm Hg with pooled 95%-LOA of -14.0 to 22.5 mm Hg for mean AP (Q=230.4 [P<0.001], I2=91%). For mean AP, the mean of the differences between finger cuff technologies and the reference method was ≤5±8 mm Hg in 9/27 data sets (33%). The pooled mean of the differences (95%-CI) was -0.13 (-0.43 to 0.18) L min-1 with pooled 95%-LOA of -2.56 to 2.23 L min-1 for CO (Q=66.7 [P<0.001], I2=90%) and 0.07 (0.01 to 0.13) L min-1 m-2 with pooled 95%-LOA of -1.20 to 1.15 L min-1 m-2 for CI (Q=5.8 [P=0.326], I2=0%). The overall random effects model-derived pooled estimate of the PE (95%-CI) was 43 (37 to 49)% (Q=48.6 [P<0.001], I2=63%). In 4/19 data sets (21%) the PE was ≤30%, and in 10/19 data sets (53%) it was ≤45%. CONCLUSIONS: Study heterogeneity was high. Several studies showed interchangeability between AP and CO/CI measurements using finger cuff technologies and reference methods. However, the pooled results of this meta-analysis indicate that AP and CO/CI measurements using finger cuff technologies and reference methods are not interchangeable in surgical or critically ill patients. CLINICAL TRIAL NUMBER: PROSPERO registration number: CRD42019119266.

Intraoperative hypotension and its prediction.

Intraoperative hypotension (IOH) very commonly accompanies general anaesthesia in patients undergoing major surgical procedures. The development of IOH is unwanted, since it is associated with adverse outcomes such as acute kidney injury and myocardial injury, stroke and mortality. Although the definition of IOH is variable, harm starts to occur below a mean arterial pressure (MAP) threshold of 65 mmHg. The odds of adverse outcome increase for increasing duration and/or magnitude of IOH below this threshold, and even short periods of IOH seem to be associated with adverse outcomes. Therefore, reducing the hypotensive burden by predicting and preventing IOH through proactive appropriate treatment may potentially improve patient outcome. In this review article, we summarise the current state of the prediction of IOH by the use of so-called machine-learning algorithms. Machine-learning algorithms that use high-fidelity data from the arterial pressure waveform, may be used to reveal 'traits' that are unseen by the human eye and are associated with the later development of IOH. These algorithms can use large datasets for 'training', and can subsequently be used by clinicians for haemodynamic monitoring and guiding therapy. A first clinically available application, the hypotension prediction index (HPI), is aimed to predict an impending hypotensive event, and additionally, to guide appropriate treatment by calculated secondary variables to asses preload (dynamic preload variables), contractility (dP/dtmax), and afterload (dynamic arterial elastance, Eadyn). In this narrative review, we summarise the current state of the prediction of hypotension using such novel, automated algorithms and we will highlight HPI and the secondary variables provided to identify the probable origin of the (impending) hypotensive event.

Minimally invasive cardiac output technologies in the ICU: putting it all together.

PURPOSE OF REVIEW: Haemodynamic monitoring is a cornerstone in the diagnosis and evaluation of treatment in critically ill patients in circulatory distress. The interest in using minimally invasive cardiac output monitors is growing. The purpose of this review is to discuss the currently available devices to provide an overview of their validation studies in order to answer the question whether these devices are ready for implementation in clinical practice. RECENT FINDINGS: Current evidence shows that minimally invasive cardiac output monitoring devices are not yet interchangeable with (trans)pulmonary thermodilution in measuring cardiac output. However, validation studies are generally single centre, are based on small sample sizes in heterogeneous groups, and differ in the statistical methods used. SUMMARY: Minimally and noninvasive monitoring devices may not be sufficiently accurate to replace (trans)pulmonary thermodilution in estimating cardiac output. The current paradigm shift to explore trending ability rather than investigating agreement of absolute values alone is to be applauded. Future research should focus on the effectiveness of these devices in the context of (functional) haemodynamic monitoring before adoption into clinical practice can be recommended.