A comparison of volume clamp method-based continuous noninvasive cardiac output (CNCO) measurement versus intermittent pulmonary artery thermodilution in postoperative cardiothoracic surgery patients

The CNAP technology (CNSystems Medizintechnik AG, Graz, Austria) allows continuous noninvasive arterial pressure waveform recording based on the volume clamp method and estimation of cardiac output (CO) by pulse contour analysis. We compared CNAP-derived CO measurements (CNCO) with intermittent invasive CO measurements (pulmonary artery catheter; PAC-CO) in postoperative cardiothoracic surgery patients. In 51 intensive care unit patients after cardiothoracic surgery, we measured PAC-CO (criterion standard) and CNCO at three different time points. We conducted two separate comparative analyses: (1) CNCO auto-calibrated to biometric patient data (CNCO_bio) versus PAC-CO and (2) CNCO calibrated to the first simultaneously measured PAC-CO value (CNCO_cal) versus PAC-CO. The agreement between the two methods was statistically assessed by Bland–Altman analysis and the percentage error. In a subgroup of patients, a passive leg raising maneuver was performed for clinical indications and we present the changes in PAC-CO and CNCO in four-quadrant plots (exclusion zone 0.5 L/min) in order to evaluate the trending ability of CNCO. The mean difference between CNCO_bio and PAC-CO was +0.5 L/min (standard deviation?±?1.3 L/min; 95% limits of agreement ?1.9 to +3.0 L/min). The percentage error was 49%. The concordance rate was 100%. For CNCOcal, the mean difference was ?0.3 L/min (±0.5 L/min; ?1.2 to +0.7 L/min) with a percentage error of 19%. In this clinical study in cardiothoracic surgery patients, CNCO_cal showed good agreement when compared with PAC-CO. For CNCO_bio, we observed a higher percentage error and good trending ability (concordance rate 100%).     

Estimation of pulse pressure variation and cardiac output in patients having major abdominal surgery: a comparison between a mobile application for snapshot pulse wave analysis and invasive pulse wave analysis

Pulse pressure variation (PPV) and cardiac output (CO) can guide perioperative fluid management. Capstesia (Galenic App, Vitoria-Gasteiz, Spain) is a mobile application for snapshot pulse wave analysis (PWAsnap) and estimates PPV and CO using pulse wave analysis of a snapshot of the arterial blood pressure waveform displayed on any patient monitor. We evaluated the PPV and CO measurement performance of PWAsnap in adults having major abdominal surgery. In a prospective study, we simultaneously measured PPV and CO using PWAsnap installed on a tablet computer (PPV_PWAsnap, CO_PWAsnap) and using invasive internally calibrated pulse wave analysis (ProAQT; Pulsion Medical Systems, Feldkirchen, Germany; PPV_ProAQT, CO_ProAQT). We determined the diagnostic accuracy of PPV_PWAsnap in comparison to PPV_ProAQT according to three predefined PPV categories and by computing Cohen’s kappa coefficient. We compared CO_ProAQT and CO_PWAsnap using Bland-Altman analysis, the percentage error, and four quadrant plot/concordance rate analysis to determine trending ability. We analyzed 190 paired PPV and CO measurements from 38 patients. The overall diagnostic agreement between PPV_PWAsnap and PPV_ProAQT across the three predefined PPV categories was 64.7% with a Cohen’s kappa coefficient of 0.45. The mean (±?standard deviation) of the differences between CO_PWAsnap and CO_ProAQT was 0.6?±?1.3 L min^??1 (95% limits of agreement 3.1 to ??1.9 L min^??1) with a percentage error of 48.7% and a concordance rate of 45.1%. In adults having major abdominal surgery, PPV_PWAsnap moderately agrees with PPV_ProAQT. The absolute and trending agreement between CO_PWAsnap with CO_ProAQT is poor. Technical improvements are needed before PWAsnap can be recommended for hemodynamic monitoring.

     

Journal of clinical monitoring and computing 2016 end of year summary: monitoring cerebral oxygenation and autoregulation

In the perioperative and critical care setting, monitoring of cerebral oxygenation (ScO₂) and cerebral autoregulation enjoy increasing popularity in recent years, particularly in patients undergoing cardiac surgery. Monitoring ScO₂ is based on near infrared spectroscopy, and attempts to early detect cerebral hypoperfusion and thereby prevent cerebral dysfunction and postoperative neurologic complications. Autoregulation of cerebral blood flow provides a steady flow of blood towards the brain despite variations in mean arterial blood pressure (MAP) and cerebral perfusion pressure, and is effective in a MAP range between approximately 50–150 mmHg. This range of intact autoregulation may, however, vary considerably between individuals, and shifts to higher thresholds have been observed in elderly and hypertensive patients. As a consequence, intraoperative hypotension will be poorly tolerated, and might cause ischemic events and postoperative neurological complications. This article summarizes research investigating technologies for the assessment of ScO₂ and cerebral autoregulation published in the Journal of Clinical Monitoring and Computing in 2016.     

Severe hyperlactatemia,lactate clearance and mortality in unselected critically ill patients

Purpose

Hyperlactatemia may occur for a variety of reasons and is a predictor of poor clinical outcome. However, only limited data are available on the underlying causes of hyperlactatemia and the mortality rates associated with severe hyperlactatemia in critically ill patients. We therefore aimed to evaluate the etiology of severe hyperlactatemia (defined as a lactate level >10 mmol/L) in a large cohort of unselected ICU patients. We also aimed to evaluate the association between severe hyperlactatemia and lactate clearance with ICU mortality.

Methods

In this retrospective, observational study at an University hospital department with 11 ICUs during the study period between 1 April 2011 and 28 February 2013, we screened 14,040 ICU patients for severe hyperlactatemia (lactate >10 mmol/L).

Results

Overall mortality in the 14,040 ICU patients was 9.8 %. Of these, 400 patients had severe hyperlactatemia and ICU mortality in this group was 78.2 %. Hyperlactatemia was associated with death in the ICU [odds ratio 1.35 (95 % CI 1.23; 1.49; p < 0.001)]. The main etiology for severe hyperlactatemia was sepsis (34.0 %), followed by cardiogenic shock (19.3 %), and cardiopulmonary resuscitation (13.8 %). Patients developing severe hyperlactatemia >24 h of ICU treatment had a significantly higher ICU mortality (89.1 %, 155 of 174 patients) than patients developing severe hyperlactatemia ≤24 h of ICU treatment (69.9 %, 158 of 226 patients; p < 0.0001). Lactate clearance after 12 h showed a receiver-operating-characteristics area under the curve (ROC-AUC) value of 0.91 to predict ICU mortality (cut-off showing highest sensitivity and specifity was a 12 h lactate clearance of 32.8 %, Youden Index 0.72). In 268 patients having a 12-h lactate clearance <32.8 % ICU mortality was 96.6 %.

Conclusions

Severe hyperlactatemia (>10 mmol/L) is associated with extremely high ICU mortality especially when there is no marked lactate clearance within 12 h. In such situations, the benefit of continued ICU therapy should be evaluated.

     

Evaluation of a dosing regimen for continuous vancomycin infusion in critically ill patients: An observational study in intensive care unit patients

Purpose

We aimed to evaluate a dosing algorithm for continuous vancomycin administration in intensive care unit patients.

Materials and Methods

This observational study was conducted in a medical intensive care unit (German university hospital; June 2012-February 2013). Following a loading dose of 20 mg per kg actual body weight, vancomycin was administered continuously (20 or 30 mg of vancomycin per kg actual body weight over 24 hours depending on renal function). The vancomycin infusion rate was adjusted to achieve a target serum vancomycin concentration of 20-30 mg/L.

Results

Vancomycin was administered for a median (interquartile range) of 7 (5-9) days. The median vancomycin dose given as an initial bolus was 1750 (1400-2000) mg. The median daily vancomycin dose ranged from 480 (180–960) mg (day 6) to 3.120 (2596-3980) mg (day 1). Altogether, the achieved median serum vancomycin concentration was 29.0 (25.2-33.2) mg/L. On treatment days 1 to 7, we observed target serum vancomycin levels (20-30 mg/L) in 48%, 39%, 33%, 26%, 43%, 57%, and 69% of patients. Supra-therapeutic serum vancomycin concentrations (> 30 mg/L) were observed in 36%, 52%, 61%, 63%, 39%, 19%, and 15% of patients on treatment days 1 to 7.

Conclusions

The evaluated vancomycin dosing regimen for continuous infusion allowed rapid achievement of sufficient vancomycin serum levels. However, we frequently observed supra-therapeutic serum vancomycin concentrations in the first days of vancomycin treatment.     

Prediction of fluid responsiveness in patients admitted to the medical intensive care unit

Purpose

Accurate prediction of fluid responsiveness is of importance in the treatment of patients admitted to the intensive care unit (ICU). We investigated whether physical examination, central venous pressure (CVP), central venous oxygen saturation (ScvO₂), passive leg raising (PLR) test, and transpulmonary thermodilution (TPTD)–derived parameters can predict volume responsiveness in patients admitted to the ICU.

Materials and Methods

In this prospective study, structured clinical examination, measurement of CVP and ScvO₂, a PLR test, and TPTD measurements were performed in 31 patients. A fluid challenge test was performed in 24 patients (fluid responsiveness was defined as a cardiac index [CI] increase of ≥ 15%).

Results

Physical examination, CVP, ScvO₂, the PLR test, and the TPTD-derived volumetric preload parameter global end-diastolic volume index showed poor prognostic capabilities regarding prediction of fluid responsiveness. Twenty-nine percent of patients were fluid responsive. There was a statistically significant correlation between the fluid challenge–induced increase in CI and changes in global end-diastolic volume index (r = 0.666, P < .001). In only 17% of patients, CI did not increase after fluid loading.

Conclusions

Prediction of fluid responsiveness is difficult using physical examination, CVP, ScvO₂, PLR maneuver, or TPTD-derived variables in critically ill patients. A volume challenge test should be considered for the assessment of fluid responsiveness in critically ill patients admitted to the ICU.     

Continuous noninvasive cardiac output determination using the CNAP system: evaluation of a cardiac output algorithm for the analysis of volume clamp method-derived pulse contour

The CNAP system (CNSystems Medizintechnik AG, Graz, Austria) provides noninvasive continuous arterial pressure measurements by using the volume clamp method. Recently, an algorithm for the determination of cardiac output by pulse contour analysis of the arterial waveform recorded with the CNAP system became available. We evaluated the agreement of the continuous noninvasive cardiac output (CNCO) measurements by CNAP in comparison with cardiac output measurements invasively obtained using transpulmonary thermodilution (TDCO). In this proof-of-concept analysis we studied 38 intensive care unit patients from a previously set up database containing CNAP-derived arterial pressure data and TDCO values obtained with the PiCCO system (Pulsion Medical Systems SE, Feldkirchen, Germany). We applied the new CNCO algorithm retrospectively to the arterial pressure waveforms recorded with CNAP and compared CNCO with the corresponding TDCO values (criterion standard). Analyses were performed separately for (1) CNCO calibrated to the first TDCO (CNCO-cal) and (2) CNCO autocalibrated to biometric patient data (CNCO-auto). We did not perform an analysis of trending capabilities because the patients were hemodynamically stable. The median age and APACHE II score of the 22 male and 16 female patients was 63 years and 18 points, respectively. 18 % were mechanically ventilated and in 29 % vasopressors were administered. Mean ± standard deviation for CNCO-cal, CNCO-auto, and TDCO was 8.1 ± 2.7, 6.4 ± 1.9, and 7.8 ± 2.4 L/min, respectively. For CNCO-cal versus TDCO, Bland–Altman analysis demonstrated a mean difference of +0.2 L/min (standard deviation 1.0 L/min; 95 % limits of agreement ?1.7 to +2.2 L/min, percentage error 25 %). For CNCO-auto versus TDCO, the mean difference was ?1.4 L/min (standard deviation 1.8 L/min; 95 % limits of agreement ?4.9 to +2.1 L/min, percentage error 45 %). This pilot analysis shows that CNCO determination is feasible in critically ill patients. A percentage error of 25 % indicates acceptable agreement between CNCO-cal and TDCO. The mean difference, the standard deviation, and the percentage error between CNCO-auto and TDCO were higher than between CNCO-cal and TDCO. A hyperdynamic cardiocirculatory state in a substantial number of patients and the hemodynamic stability making trending analysis impossible are main limitations of our study.