Purpose: Intraoperative hypotension is linked to increased incidence of perioperative adverse events such as myocardial and cerebrovascular infarction and acute kidney injury. Hypotension prediction index (HPI) is a novel machine learning guided algorithm which can predict hypotensive events using high fidelity analysis of pulse-wave contour. Goal of this trial is to determine whether use of HPI can reduce the number and duration of hypotensive events in patients undergoing major thoracic procedures. Methods: Thirty four patients undergoing esophageal or lung resection were randomized into 2 groups -“machine learning algorithm” (AcumenIQ) and “conventional pulse contour analysis” (Flotrac). Analyzed variables were occurrence, severity and duration of hypotensive events (defined as a period of at least one minute of MAP below 65 mmHg), hemodynamic parameters at 9 different timepoints interesting from a hemodynamics viewpoint and laboratory (serum lactate levels, arterial blood gas) and clinical outcomes (duration of mechanical ventilation, ICU and hospital stay, occurrence of adverse events and in-hospital and 28-day mortality). Results: Patients in the AcumenIQ group had significantly lower area below the hypotensive threshold (AUT, 2 vs 16.7 mmHg x minutes) and time-weighted AUT (TWA, 0.01 vs 0.08 mmHg). Also, there were less patients with hypotensive events and cumulative duration of hypotension in the AcumenIQ group. No significant difference between groups was found in terms of laboratory and clinical outcomes. Conclusions: Hemodynamic optimization guided by machine learning algorithm leads to a significant decrease in number and duration of hypotensive events compared to traditional goal directed therapy using pulse-contour analysis hemodynamic monitoring in patients undergoing major thoracic procedures. Further, larger studies are needed to determine true clinical utility of HPI guided hemodynamic monitoring. Trial registration: Date of first registration: 14/11/2022 Registration number: 04729481-3a96-4763-a9d5-23fc45fb722d

Healthcare-associated infections are an emerging cause of morbidity and mortality in COVID-19 intensive care units (ICUs) worldwide, especially those caused by multidrug-resistant (MDR) pathogens. The objectives of this study were to assess the incidence of bloodstream infections (BSIs) among critically ill COVID-19 patients and to analyze the characteristics of healthcare-associated BSIs due to MDR Acinetobacter baumannii in an COVID-19 ICU. A single-center retrospective study was conducted at a tertiary hospital during a 5-month period. The detection of carbapenemase genes was performed by PCR and genetic relatedness by pulsed-field gel electrophoresis (PFGE) and multilocus-sequence typing. A total of 193 episodes were registered in 176 COVID-19 ICU patients, with an incidence of 25/1000 patient-days at risk. A. baumannii was the most common etiological agent (40.3%), with a resistance to carbapenems of 100%. The bla_OXA-23 gene was detected in ST2 isolates while the bla_OXA-24 was ST636-specific. PFGE revealed a homogeneous genetic background of the isolates. The clonal spread of OXA-23-positive A. baumannii is responsible for the high prevalence of MDR A. baumannii BSIs in our COVID-19 ICU. Further surveillance of resistance trends and mechanisms is needed along with changes in behavior to improve the implementation of infection control and the rational use of antibiotics.

Background: Superinfections contribute to mortality and length of stay in critically ill COVID-19 patients. The aim of this study was to determine the incidence and pathogen distribution of bacterial and fungal superinfections of the lower respiratory tract (LRTI), urinary tract (UTI) and bloodstream (BSI) and to determine the predictive value of biomarkers of inflammatory response on their ICU survival rates. Methods: A retrospective observational study that included critically ill COVID-19 patients treated during an 11-month period in a Croatian national COVID-19 hospital was performed. Clinical and diagnostic data were analyzed according to the origin of superinfection, and multivariate regression analysis was performed to determine the predictive values of biomarkers of inflammation on their survival rates. Results: 55.3% critically ill COVID-19 patients developed bacterial or fungal superinfections, and LRTI were most common, followed by BSI and UTI. Multidrug-resistant pathogens were the most common causes of LRTI and BSI, while Enterococcus faecalis was the most common pathogen causing UTI. Serum ferritin and neutrophil count were associated with decreased chances of survival in patients with LRTI, and patients with multidrug-resistant isolates had significantly higher mortality rates, coupled with longer ICU stays. Conclusion: The incidence of superinfections in critically ill COVID-19 patients was 55.3%, and multidrug-resistant pathogens were dominant. Elevated ferritin levels and neutrophilia at ICU admission were associated with increased ICU mortality in patients with positive LRTI.

To describe epidemiological characteristics and baseline clinical features, laboratory findings at intensive care unit (ICU) admission, and survival rates of critically ill coronavirus disease 2019 (COVID-19) patients treated at a tertiary institution ...

Background: Derangement of liver blood tests (LBT) is frequent in patients with Coronavirus disease 2019 (COVID-19). We aimed to evaluate (a) the prevalence of deranged LBT as well as their association with (b) clinical severity at admission and (c) 30-day outcomes among the hospitalized patients with COVID-19. Methods: Consecutive patients with COVID-19 hospitalized in the regional referral center over the 12-month period were included. Clinical severity of COVID-19 at hospital admission and 30-day outcomes (need for intensive care, mechanical ventilation, or death) were analyzed. Results: Derangement of LBT occurred in 2854/3812 (74.9%) of patients, most frequently due to elevation of AST (61.6%), GGT (46.1%) and ALT (33.4%). Elevated AST, ALT, GGT and low albumin were associated with more severe disease at admission. However, in multivariate Cox regression analysis, when adjusted for age, sex, obesity and presence of chronic liver disease, only AST remained associated with the risk of dying (HR 1.5081 and 2.1315, for elevations 1–3 × ULN and >3 × ULN, respectively) independently of comorbidity burden and COVID-19 severity at admission. Patients with more severe liver injury more frequently experienced defined adverse outcomes. Conclusions: Deranged LBTs are common among patients hospitalized with COVID-19 and might be used as predictors of adverse clinical outcomes.