Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial.

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.

Epidemiological characteristics, baseline clinical features, and outcomes of critically ill patients treated in a coronavirus disease 2019 tertiary center in continental Croatia.

AIM: 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 specialized for COVID-19 patients. METHODS: This retrospective study recruited 692 patients (67.1% men). Baseline demographic data, major comorbidities, anthropometric measurements, clinical features, and laboratory findings at admission were compared between survivors and non-survivors. RESULTS: The median age was 72 (64-78) years. The median body mass index was 29.1 kg/m2. The most relevant comorbidities were diabetes mellitus (32.6%), arterial hypertension (71.2%), congestive heart failure (19.1%), chronic kidney disease (12.6%), and hematological disorders (10.3%). The median number of comorbidities was 3 and median Charlson Comorbidity Index (CCI) was 5. A total of 61.8% patients received high-flow nasal oxygen therapy (HFNO) and 80.5% received mechanical ventilation (MV). Median duration of HFNO was 3, and that of MV was 7 days. ICU mortality rate was 72.7%. Survivors had significantly lower age, number of comorbidities, CCI, sequential organ failure assessment score, serum ferritin, C-reactive protein, D-dimer, and procalcitonin, interleukin-6, lactate, white blood cell, and neutrophil counts. They also had higher lymphocyte counts, PaO2/FiO2 ratio, and glomerular filtration rate at admission. Length of ICU stay was 9 days. The median survival was 11 days for mechanically ventilated patients, and 24 days for patients who were not mechanically ventilated. CONCLUSION: The parameters that differentiate survivors from non-survivors are in agreement with published data. Further multivariate analyses are warranted to identify individual mortality risk factors.

Clinical and laboratory predictors at ICU admission affecting course of illness and mortality rates in a tertiary COVID-19 center.

BACKGROUND: Survival rates of critically ill COVID-19 patients are affected by various clinical features and laboratory parameters at ICU admission. Some of these predictors are universal but others may be population specific. OBJECTIVE: To determine utility of baseline clinical and laboratory parameters in a multivariate regression model to predict outcomes in critically ill COVID-19 patients in a tertiary hospital in Croatia. METHODS: 692 critically ill COVID-19 patients treated during a 10-month period were included in this retrospective observational trial to assess the risk factors determining mortality rates. Various anthropometric features, comorbidities, laboratory parameters, clinical features and therapeutic interventions were included in the analysis. ICU mortality rates and length of ICU stay were primary endpoints analyzed in this study. RESULTS: After multivariate adjustment, only the SOFA score, PaO2/FiO2 and history of arterial hypertension had an effect on ICU mortality, as well as the need to initiate invasive mechanical ventilation. Increase in PaO2/FiO2 over the first 7 days was present in survivors, while reverse applied to SOFA. Length of ICU stay was 9 (4-14) days. Factors affecting survival times were admission from wards, congestive heart failure, invasive mechanical ventilation, bacterial superinfections, age > 75 years, SOFA score, and serum ferritin, CRP and IL-6 values at ICU admission. CONCLUSION: Elevated inflammatory biomarkers and SOFA score at ICU admission were detected as significant predictors of ICU mortality in this cohort, while initiation of invasive mechanical ventilation is the most relevant interventional mortality risk factor in critically ill COVID-19 patients.

Distribution of Pathogens and Predictive Values of Biomarkers of Inflammatory Response at ICU Admission on Outcomes of Critically Ill COVID-19 Patients with Bacterial Superinfections-Observations from National COVID-19 Hospital in Croatia.

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.