A comparison of third-generation semi-invasive arterial waveform analysis with thermodilution in patients undergoing coronary surgery

Uncalibrated semi-invasive continous monitoring of cardiac index (CI) has recently gained increasing interest. The aim of the present study was to compare the accuracy of CI determination based on arterial waveform analysis with transpulmonary thermodilution. Fifty patients scheduled for elective coronary surgery were studied after induction of anaesthesia and before and after cardiopulmonary bypass (CPB), respectively. Each patient was monitored with a central venous line, the PiCCO system, and the FloTrac/Vigileo-system. Measurements included CI derived by transpulmonary thermodilution and uncalibrated semi-invasive pulse contour analysis. Percentage changes of CI were calculated. There was a moderate, but significant correlation between pulse contour CI and thermodilution CI both before (r(2) = 0.72, P < 0.0001) and after (r(2) = 0.62, P < 0.0001) CPB, with a percentage error of 31% and 25%, respectively. Changes in pulse contour CI showed a significant correlation with changes in thermodilution CI both before (r(2) = 0.52, P < 0.0001) and after (r(2) = 0.67, P < 0.0001) CPB. Our findings demonstrated that uncalibrated semi-invasive monitoring system was able to reliably measure CI compared with transpulmonary thermodilution in patients undergoing elective coronary surgery. Furthermore, the semi-invasive monitoring device was able to track haemodynamic changes and trends

Dynamic and volumetric variables reliably predict fluid responsiveness in a porcine model with pleural effusion

Background: The ability of stroke volume variation (SVV), pulse pressure variation (PPV) and global end-diastolic volume (GEDV) for prediction of fluid responsiveness in presence of pleural effusion is unknown. The aim of the present study was to challenge the ability of SVV, PPV and GEDV to predict fluid responsiveness in a porcine model with pleural effusions. Methods: Pigs were studied at baseline and after fluid loading with 8 ml kg−1 6% hydroxyethyl starch. After withdrawal of 8 ml kg−1 blood and induction of pleural effusion up to 50 ml kg−1 on either side, measurements at baseline and after fluid loading were repeated. Cardiac output, stroke volume, central venous pressure (CVP) and pulmonary occlusion pressure (PAOP) were obtained by pulmonary thermodilution, whereas GEDV was determined by transpulmonary thermodilution. SVV and PPV were monitored continuously by pulse contour analysis. Results: Pleural effusion was associated with significant changes in lung compliance, peak airway pressure and stroke volume in both responders and non-responders. At baseline, SVV, PPV and GEDV reliably predicted fluid responsiveness (area under the curve 0.85 (p<0.001), 0.88 (p<0.001), 0.77 (p = 0.007). After induction of pleural effusion the ability of SVV, PPV and GEDV to predict fluid responsiveness was well preserved and also PAOP was predictive. Threshold values for SVV and PPV increased in presence of pleural effusion. Conclusions: In this porcine model, bilateral pleural effusion did not affect the ability of SVV, PPV and GEDV to predict fluid responsiveness

Processing and characterization of monolithic passive-matrix GaN-based microLED arrays with pixel sizes from 5 to 50 µm

MicroLED arrays with the capability of switching each pixel separately with high frequency can serve as structured micro-illumination light engines for applications in sensing, optogenetics, microscopy and many others. We describe a scalable chip process chain for the fabrication of passive-matrix microLED arrays, which were integrated with PCB-based driving electronics. The arrays were produced by deep-etching of conventional planar LED structures on sapphire, followed by filling and planarization steps. The pixel resolution lies in the range of 254 to 2540 pixels-per-inch (ppi), the arrays consist of 32 x 32 pixels. Optical output powers up to 50 μW per pixel were measured. In comparison to CMOS-based approaches, the presented technology is a simplified strategy to producemicroLED arrays with high pixel counts

Area under the Receiver Operating Characteristic Curve showing the ability of preload variables to predict an increase in stroke volume generated by pulmonary thermodilution ≥15% at baseline and during pleural effusion.

<p>BL, baseline; PLE, pleural effusion; AUC, area under the curve; 95% CI, 95% confidence interval; CVP, central venous pressure; PAOP, pulmonary artery occlusion pressure; GEDV, global end-diastolic volume; PPV, pulse pressure variation; SVV, stroke volume variation; n.a., not assessed.</p

After induction of anesthesia and preparation, measurement of hemodynamics was performed before and after fluid loading.

<p>Following blood withdrawal and induction of pleural effusion, hemodynamics before and after fluid loading were determined again.</p

Hemodynamic and respiratory variables in Responder and Non-Responder before and after fluid loading during baseline and pleural effusion.

<p>BL - NV, baseline normovolemia; PLE - NV, pleural effusion normovolemia; BL - FL, baseline fluid loading; PLE - FL, pleural effusion fluid loading; HR, heart rate; MAP, mean arterial pressure; SVR, systemic vascular resistance; C_L, lung compliance; C_CW, chest wall compliance; dP_es, delta esophageal pressure; P_tp,es, transpulmonary pressure measured with an esophageal balloon; P_{AW peak}, end-inspiratory airway pressure; P_{AW mean}, mean airway pressure; V_T, tidal volume; PEEP, positive end-expiratory pressure; CO_PAC, cardiac output derived from pulmonary thermodilution; SV_PAC, stroke volume derived from pulmonary thermodilution; CVP, central venous pressure; PAOP, pulmonary artery occlusion pressure; PPV, pulse pressure variation; SVV, stroke volume variation; GEDV, global end-diastolic volume; Values are given as mean ±SD; Responder:</p>a<p>p<0.05 (vs. BL - NV);</p>b<p>p<0.05 (vs. BL - FL);</p>c<p>p<0.05 (vs. PLE – NV); Non-Responder:</p>*<p>p<0.05 (vs. BL - NV);</p>#<p>p<0.05 (vs. BL - FL);</p>Δ<p>p<0.05 (vs. PLE – NV).</p

Correlation between dynamic and volumetric variables with percentage changes in stroke volume measured by pulmonary thermodilution (ΔSV_PAC%) after fluid loading at baseline and during pleural effusion.

<p>CVP, central venous pressure; PAOP, pulmonary artery occlusion pressure; PPV, pulse pressure variation; SVV, stroke volume variation; GEDV, global end-diastolic volume.</p