Volumetric preload measurement by thermodilution: a comparison with transoesophageal echocardiography

Background. End-diastolic volume indices determined by transpulmonary thermodilution and pulmonary artery thermodilution may give a better estimate of left ventricular preload than pulmonary capillary wedge pressure monitoring. The aim of this study was to compare volume preload monitoring using the two different thermodilution techniques with left ventricular preload assessment by transoesophageal echocardiography (TOE). Methods. Twenty patients undergoing elective cardiac surgery with preserved left-right ventricular function were studied after induction of anaesthesia. Conventional haemodynamic variables, global end-diastolic volume index using the pulse contour cardiac output (PiCCO) system (GEDVIPiCCO), continuous end-diastolic volume index (CEDVIPAC) measured by a modified pulmonary artery catheter (PAC), left ventricular end-diastolic area index (LVEDAI) using TOE and stroke volume indices (SVI) were recorded before and 20 and 40 min after fluid replacement therapy. Analysis of variance (Bonferroni-Dunn), Bland-Altman analysis and linear regression were performed. Results. GEDVIPiCCO, CEDVIPAC, LVEDAI and SVIPiCCO/PAC increased significantly after fluid load (P10% for GEDVIPiCCO and LVEDAI was observed in 85% and 90% of the patients compared with 45% for CEDVIPAC. Mean bias (2 sd) between percentage changes (Δ) in GEDVIPiCCO and ΔLVEDAI was −3.2 (17.6)% and between ΔCEDVIPAC and ΔLVEDAI −8.7 (30.0)%. The correlation coefficient (r2) for ΔGEDVIPiCCO vs ΔLVEDAI was 0.658 and for ΔCEDVIPAC vs ΔLVEDAI 0.161. The relationship between ΔGEDVIPiCCO and ΔSVIPiCCO was stronger (r2=0.576) than that between ΔCEDVIPAC and ΔSVIPAC (r2=0.267). Conclusion. GEDVI assessed by the PiCCO system gives a better reflection of echocardiographic changes in left ventricular preload, in response to fluid replacement therapy, than CEDVI measured by a modified PA

Volumetric preload measurement by thermodilution: a comparison with transoesophageal echocardiography

Background. End-diastolic volume indices determined by transpulmonary thermodilution and pulmonary artery thermodilution may give a better estimate of left ventricular preload than pulmonary capillary wedge pressure monitoring. The aim of this study was to compare volume preload monitoring using the two different thermodilution techniques with left ventricular preload assessment by transoesophageal echocardiography (TOE). Methods. Twenty patients undergoing elective cardiac surgery with preserved left-right ventricular function were studied after induction of anaesthesia. Conventional haemodynamic variables, global end-diastolic volume index using the pulse contour cardiac output (PiCCO) system (GEDVIPiCCO), continuous end-diastolic volume index (CEDVIPAC) measured by a modified pulmonary artery catheter (PAC), left ventricular end-diastolic area index (LVEDAI) using TOE and stroke volume indices (SVI) were recorded before and 20 and 40 min after fluid replacement therapy. Analysis of variance (Bonferroni-Dunn), Bland-Altman analysis and linear regression were performed. Results. GEDVIPiCCO, CEDVIPAC, LVEDAI and SVIPiCCO/PAC increased significantly after fluid load (P10% for GEDVIPiCCO and LVEDAI was observed in 85% and 90% of the patients compared with 45% for CEDVIPAC. Mean bias (2 sd) between percentage changes (Δ) in GEDVIPiCCO and ΔLVEDAI was −3.2 (17.6)% and between ΔCEDVIPAC and ΔLVEDAI −8.7 (30.0)%. The correlation coefficient (r2) for ΔGEDVIPiCCO vs ΔLVEDAI was 0.658 and for ΔCEDVIPAC vs ΔLVEDAI 0.161. The relationship between ΔGEDVIPiCCO and ΔSVIPiCCO was stronger (r2=0.576) than that between ΔCEDVIPAC and ΔSVIPAC (r2=0.267). Conclusion. GEDVI assessed by the PiCCO system gives a better reflection of echocardiographic changes in left ventricular preload, in response to fluid replacement therapy, than CEDVI measured by a modified PA

Implications of early respiratory support strategies on disease progression in critical COVID-19: a matched subanalysis of the prospective RISC-19-ICU cohort.

Uncertainty about the optimal respiratory support strategies in critically ill COVID-19 patients is widespread. While the risks and benefits of noninvasive techniques versus early invasive mechanical ventilation (IMV) are intensely debated, actual evidence is lacking. We sought to assess the risks and benefits of different respiratory support strategies, employed in intensive care units during the first months of the COVID-19 pandemic on intubation and intensive care unit (ICU) mortality rates. Subanalysis of a prospective, multinational registry of critically ill COVID-19 patients. Patients were subclassified into standard oxygen therapy ≥10 L/min (SOT), high-flow oxygen therapy (HFNC), noninvasive positive-pressure ventilation (NIV), and early IMV, according to the respiratory support strategy employed at the day of admission to ICU. Propensity score matching was performed to ensure comparability between groups. Initially, 1421 patients were assessed for possible study inclusion. Of these, 351 patients (85 SOT, 87 HFNC, 87 NIV, and 92 IMV) remained eligible for full analysis after propensity score matching. 55% of patients initially receiving noninvasive respiratory support required IMV. The intubation rate was lower in patients initially ventilated with HFNC and NIV compared to those who received SOT (SOT: 64%, HFNC: 52%, NIV: 49%, p = 0.025). Compared to the other respiratory support strategies, NIV was associated with a higher overall ICU mortality (SOT: 18%, HFNC: 20%, NIV: 37%, IMV: 25%, p = 0.016). In this cohort of critically ill patients with COVID-19, a trial of HFNC appeared to be the most balanced initial respiratory support strategy, given the reduced intubation rate and comparable ICU mortality rate. Nonetheless, considering the uncertainty and stress associated with the COVID-19 pandemic, SOT and early IMV represented safe initial respiratory support strategies. The presented findings, in agreement with classic ARDS literature, suggest that NIV should be avoided whenever possible due to the elevated ICU mortality risk

Túnel de Donnerbühl, en Berna (Suiza)

This article describes the railway tunnel and its construction by the shield method. It summarises the measurements and observations that were taken after its completion by R. Desponds, the chief engineer of the Works Division of the 1st district of Federal Railways of Switzerland, and by K. Ensner, dipl. engineer. Chief of the Measurements Section of the General Direction of the Swiss Federal Railways, of Berne.El artículo trata de la construcción de un túnel ferroviario por el método de escudo y resume las medidas y observaciones hechas, después de la construcción, por R. Desponds, ingeniero jefe de la División de trabajos del primer distrito de los ferrocarriles federales suizos, en Lausana, y K. Ensner, ingeniero diplomado, jefe de la Sección de Medidas de la Dirección General de los ferrocarriles federales suizos, en Berna

Polymyxin-B hemoperfusion in septic patients: analysis of a multicenter registry

Background: In 2010, the EUPHAS 2 collaborative group created a registry with the purpose of recording data from critically ill patients suffering from severe sepsis and septic shock treated with polymyxin-B hemoperfusion (PMX-HP) for endotoxin removal. The aim of the registry was to verify the application of PMX-HP in the daily clinical practice. Methods: The EUPHAS 2 registry involved 57 centers between January 2010 and December 2014, collecting retrospective data of 357 patients (297 in Europe and 60 in Asia) suffering from severe sepsis and septic shock caused by proved or suspected infection related to Gram negative bacteria. All patients received atleast one cycle of extracorporeal endotoxin removal by PMX-HP. Results: Septic shock was diagnosed in 305 (85.4\ua0%) patients. The most common source of infection was abdominal (44.0\ua0%) followed by pulmonary (17.6\ua0%). Gram negative bacteria represented 60.6\ua0% of the pathogens responsible of infection. After 72\ua0h from the first cycle of PMX-HP, some of the SOFA score components significantly improved with respect to baseline: cardiovascular (2.16\ua0\ub1\ua01.77 from 3.32\ua0\ub1\ua01.29, p\ua0<\ua00.0001), respiratory (1.95\ua0\ub1\ua00.95 from 2.40\ua0\ub1\ua01.06, p\ua0<\ua00.001) and renal (1.84\ua0\ub1\ua01.77 from 2.23\ua0\ub1\ua01.62, p\ua0=\ua00.013). Overall 28-day survival rate was 54.5\ua0% (60.4\ua0% in abdominal and 47.5\ua0% in pulmonary infection). Patients with abdominal infection treated with PMX-HP within 24\ua0h from the diagnosis of septic shock had a 28-day survival rate of 64.5\ua0%. Patients showing a significantly cardiovascular improvement after PMX-HP had a 28-survival rate of 75\ua0% in comparison to the 39\ua0% of patients who did not (p\ua0<\ua00.001). Cox regression analysis found the variation of cardiovascular, respiratory and coagulation SOFA to be independent covariates for 28-day survival. In European patients were observed a higher 28-day (58.8 vs. 34.5\ua0%, p\ua0=\ua00.003), ICU (59 vs. 36.7\ua0%, p\ua0=\ua00.006) and hospital survival rate (53.2 vs. 35\ua0%, p\ua0=\ua00.02) than in Asian patients. However, the two populations were highly heterogeneous in terms of source of infection and severity scores at admission. Conclusion: The EUPHAS 2 is the largest registry conducted outside Japan on the clinical use of PMX-HP in septic patients. Data analysis confirmed the feasibility of PMX-HP to treat septic patients in daily clinical practice, showing clinical benefits associated with endotoxin removal without significant adverse events related to the extracorporeal technique. \ua9 2016, The Author(s)

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P &lt; 0.01 [paired t-test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources