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/dt(max)), and afterload (dynamic arterial elastance, Ea(dyn)). 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

"Cartesian light": unconventional propagation of light in a 3D superlattice of coupled cavities within a 3D photonic band gap

We explore the unconventional propagation of light in a three-dimensional (3D) superlattice of coupled resonant cavities in a 3D photonic band gap crystal. Such a 3D cavity superlattice is the photonic analogue of the Anderson model for spins and electrons in the limit of zero disorder. Using the plane-wave expansion method, we calculate the dispersion relations of the 3D cavity superlattice with the cubic inverse woodpile structure that reveal five coupled-cavity bands, typical of quadrupole-like resonances. For three out of five bands, we observe that the dispersion bandwidth is significantly larger in the $(k_x, k_z)$-diagonal directions than in other directions. To explain the directionality of the dispersion bandwidth, we employ the tight-binding method from which we derive coupling coefficients in 3D. For all converged coupled-cavity bands, we find that light hops predominantly in a few high-symmetry directions including the Cartesian $(x, y, z)$ directions, therefore we propose the name "Cartesian light". Such 3D Cartesian hopping of light in a band gap yields propagation as superlattice Bloch modes that differ fundamentally from the conventional 3D spatially-extended Bloch wave propagation in crystals, from light tunneling through a band gap, from coupled-resonator optical waveguiding, and also from light diffusing at the edge of a gap

Do alterations in pulmonary vascular tone result in changes in central blood volumes? An experimental study:An experimental study

BACKGROUND: The effects of selective pulmonary vascular tone alterations on cardiac preload have not been previously examined. Therefore, we evaluated whether changing pulmonary vascular tone either by hypoxia or the inhalation of aerosolized prostacyclin (PGI(2)) altered intrathoracic or pulmonary blood volume (ITBV, PBV, respectively), both as surrogate for left ventricular preload. Additionally, the mean systemic filling pressure analogue (Pmsa) and pressure for venous return (Pvr) were calculated as surrogate of right ventricular preload. METHODS: In a randomized controlled animal study in 6 spontaneously breathing dogs, pulmonary vascular tone was increased by controlled moderate hypoxia (FiO(2) about 0.10) and decreased by aerosolized PGI(2). Also, inhalation of PGI(2) was instituted to induce pulmonary vasodilation during normoxia and hypoxia. PBV, ITBV and circulating blood volume (Vd(circ)) were measured using transpulmonary thermo-dye dilution. Pmsa and Pvr were calculated post hoc. Either the Wilcoxon-signed rank test or Friedman ANOVA test was performed. RESULTS: During hypoxia, mean pulmonary artery pressure (PAP) increased from median [IQR] 12 [8–15] to 19 [17–25] mmHg (p < 0.05). ITBV, PBV and their ratio with Vd(circ) remained unaltered, which was also true for Pmsa, Pvr and cardiac output. PGI(2) co-inhalation during hypoxia normalized mean PAP to 13 (12–16) mmHg (p < 0.05), but left cardiac preload surrogates unaltered. PGI(2) inhalation during normoxia further decreased mean PAP to 10 (9–13) mmHg (p < 0.05) without changing any of the other investigated hemodynamic variables. CONCLUSIONS: In spontaneously breathing dogs, changes in pulmonary vascular tone altered PAP but had no effect on cardiac output, central blood volumes or their relation to circulating blood volume, nor on Pmsa and Pvr. These observations suggest that cardiac preload is preserved despite substantial alterations in right ventricular afterload

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

Unsupervised Machine Learning to Classify the Confinement of Waves in Periodic Superstructures

We employ unsupervised machine learning to enhance the accuracy of our recently presented scaling method for wave confinement analysis [1]. We employ the standard k-means++ algorithm as well as our own model-based algorithm. We investigate cluster validity indices as a means to find the correct number of confinement dimensionalities to be used as an input to the clustering algorithms. Subsequently, we analyze the performance of the two clustering algorithms when compared to the direct application of the scaling method without clustering. We find that the clustering approach provides more physically meaningful results, but may struggle with identifying the correct set of confinement dimensionalities. We conclude that the most accurate outcome is obtained by first applying the direct scaling to find the correct set of confinement dimensionalities and subsequently employing clustering to refine the results. Moreover, our model-based algorithm outperforms the standard k-means++ clustering.Comment: 24 pages, 11 figure

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

Mitral Valve Coaptation Reserve Index:A Model to Localize Individual Resistance to Mitral Regurgitation Caused by Annular Dilation

Objectives: The objective of this study was to develop a mathematical model for mitral annular dilatation simulation and determine its effects on the individualized mitral valve (MV) coaptation reserve index (CRI). Design: A retrospective analysis of intraoperative transesophageal 3-dimensionalechocardiographic MV datasets was performed. A mathematical model was created to assess the mitral CRI for each leaflet segment (A1-P1, A2-P2, A3-P3). Mitral CRI was defined as the ratio between the coaptation reserve (measured coaptation length along the closure line) and an individualized correction factor. Indexing was chosen to correct for MV sphericity and area of largest valve opening. Mathematical models were created to simulate progressive mitral annular dilatation and to predict the effect on the individual mitral CRI. Setting: At a single-center academic hospital. Participants: Twenty-five patients with normally functioning MVs undergoing cardiac surgery. Interventions: None. Measurements and Main Results: Direct measurement of leaflet coaptation along the closure line showed the lowest amount of coaptation (reserve) near the commissures (A1-P1 0.21 ± 0.05 cm and A3-P3 0.22 ± 0.06 cm), and the highest amount of coaptation (reserve) at region A2 to P2 0.25 ± 0.06 cm. After indexing, the A2-to-P2 region was the area with the lowest CRI in the majority of patients, and also the area with the least resistance to mitral regurgitation (MR) occurrence after simulation of progressive annular dilation. Conclusions: Quantification and indexing of mitral coaptation reserve along the closure line are feasible. Indexing and mathematical simulation of progressive annular dilatation consistently showed that indexed coaptation reserve was lowest in the A2-to-P2 region. These results may explain why this area is prone to lose coaptation and is often affected in MR