BackgroundA passive leg raising (PLR) test is positive if the cardiac index (CI) increased by > 10%, but it requires a direct measurement of CI. On the oxygen saturation plethysmographic signal, the perfusion index (PI) is the ratio between the pulsatile and the non-pulsatile portions. We hypothesised that the changes in PI could predict a positive PLR test and thus preload responsiveness in a totally non-invasive way.MethodsIn patients with acute circulatory failure, we measured PI (Radical-7) and CI (PiCCO2) before and during a PLR test and, if decided, before and after volume expansion (500-mL saline).ResultsThree patients were excluded because the plethysmography signal was absent and 3 other ones because it was unstable. Eventually, 72 patients were analysed. In 34 patients with a positive PLR test (increase in CI ≥ 10%), CI and PI increased during PLR by 21 ± 10% and 54 ± 53%, respectively. In the 38 patients with a negative PLR test, PI did not significantly change during PLR. In 26 patients in whom volume expansion was performed, CI and PI increased by 28 ± 14% and 53 ± 63%, respectively. The correlation between the PI and CI changes for all interventions was significant (r = 0.64, p < 0.001). During the PLR test, if PI increased by > 9%, a positive response of CI (≥ 10%) was diagnosed with a sensitivity of 91 (76–98%) and a specificity of 79 (63–90%) (area under the receiver operating characteristics curve 0.89 (0.80–0.95), p < 0.0001).ConclusionAn increase in PI during PLR by 9% accurately detects a positive response of the PLR test.Trial registrationID RCB 2016-A00959-42. Registered 27 June 2016.Electronic supplementary materialThe online version of this article (10.1186/s13054-019-2306-z) contains supplementary material, which is available to authorized users.

Purpose To survey haemodynamic monitoring and management practices in intensive care patients with the coronavirus disease 2019 (COVID-19). Methods A questionnaire was shared on social networks or via email by the authors and by Anaesthesia and/or Critical Care societies from France, Switzerland, Belgium, Brazil, and Portugal. Intensivists and anaesthetists involved in COVID-19 ICU care were invited to answer 14 questions about haemodynamic monitoring and management. Results Globally, 1000 questionnaires were available for analysis. Responses came mainly from Europe (n = 460) and America (n = 434). According to a majority of respondents, COVID-19 ICU patients frequently or very frequently received continuous vasopressor support (56%) and had an echocardiography performed (54%). Echocardiography revealed a normal cardiac function, a hyperdynamic state (43%), hypovolaemia (22%), a left ventricular dysfunction (21%) and a right ventricular dilation (20%). Fluid responsiveness was frequently assessed (84%), mainly using echo (62%), and cardiac output was measured in 69%, mostly with echo as well (53%). Venous oxygen saturation was frequently measured (79%), mostly from a CVC blood sample (94%). Tissue perfusion was assessed biologically (93%) and clinically (63%). Pulmonary oedema was detected and quantified mainly using echo (67%) and chest X-ray (61%). Conclusion Our survey confirms that vasopressor support is not uncommon in COVID-19 ICU patients and suggests that different haemodynamic phenotypes may be observed. Ultrasounds were used by many respondents, to assess cardiac function but also to predict fluid responsiveness and quantify pulmonary oedema. Although we observed regional differences, current international guidelines were followed by most respondents.

Background Ventilator-associated pneumonia caused by Pseudomonas aeruginosa (PA) in hospitalised patients is associated with high mortality. The effectiveness of the bivalent, bispecific mAb MEDI3902 (gremubamab) in preventing PA nosocomial pneumonia was assessed in PA-colonised mechanically ventilated subjects. Methods EVADE (NCT02696902) was a phase 2, randomised, parallel-group, double-blind, placebo-controlled study in Europe, Turkey, Israel, and the USA. Subjects ≥ 18 years old, mechanically ventilated, tracheally colonised with PA, and without new-onset pneumonia, were randomised (1:1:1) to MEDI3902 500, 1500 mg (single intravenous dose), or placebo. The primary efficacy endpoint was the incidence of nosocomial PA pneumonia through 21 days post-dose in MEDI3902 1500 mg versus placebo, determined by an independent adjudication committee. Results Even if the initial sample size was not reached because of low recruitment, 188 subjects were randomised (MEDI3902 500/1500 mg: n = 16/87; placebo: n = 85) between 13 April 2016 and 17 October 2019. Out of these, 184 were dosed (MEDI3902 500/1500 mg: n = 16/85; placebo: n = 83), comprising the modified intent-to-treat set. Enrolment in the 500 mg arm was discontinued due to pharmacokinetic data demonstrating low MEDI3902 serum concentrations. Subsequently, enrolled subjects were randomised (1:1) to MEDI3902 1500 mg or placebo. PA pneumonia was confirmed in 22.4% (n = 19/85) of MEDI3902 1500 mg recipients and in 18.1% (n = 15/83) of placebo recipients (relative risk reduction [RRR]: − 23.7%; 80% confidence interval [CI] − 83.8%, 16.8%; p = 0.49). At 21 days post-1500 mg dose, the mean (standard deviation) serum MEDI3902 concentration was 9.46 (7.91) μg/mL, with 80.6% (n = 58/72) subjects achieving concentrations > 1.7 μg/mL, a level associated with improved outcome in animal models. Treatment-emergent adverse event incidence was similar between groups. Conclusions The bivalent, bispecific monoclonal antibody MEDI3902 (gremubamab) did not reduce PA nosocomial pneumonia incidence in PA-colonised mechanically ventilated subjects. Trial registration Registered on Clinicaltrials.gov (NCT02696902) on 11th February 2016 and on EudraCT (2015-001706-34) on 7th March 2016.

Background Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients. Methods Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEFNov) and by experts (LVEFExp) were compared with LVEF reference measurements (LVEFRef) taken manually by echo experts. Results LVEFRef ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEFRef < 50% (left ventricular systolic dysfunction). Real-time LVEFExp and LVEFNov measurements ranged from 31 to 68% (mean 54 ± 10%) and from 28 to 70% (mean 54 ± 9%), respectively. The reproducibility of measurements was comparable for LVEFExp (5 ± 4%) and for LVEFNov (6 ± 5%) and significantly better than for reference measurements (p < 0.001). We observed a strong relationship between LVEFRef and both real-time LVEFExp (r = 0.86, p < 0.001) and LVEFNov (r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEFExp and 0 ± 7% for LVEFNov. The sensitivity to detect systolic dysfunction was 70% for real-time LVEFExp and 73% for LVEFNov. The specificity to detect systolic dysfunction was 98% both for LVEFExp and LVEFNov. Conclusion Machine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved. Trial registration: NCT05336448. Retrospectively registered on April 19, 2022.