COVID-19 pandemic in the intensive care unit:Psychological implications and interventions,a systematic review

BACKGROUND The coronavirus disease 2019(COVID-19) pandemic produced changes in intensive care units(ICUs) in patient care and health organizations. The pandemic event increased patients’ risk of developing psychological symptoms during and after hospitalisation. These consequences also affected those family members who could not access the hospital. In addition, the initial lack of knowledge about the virus and its management, the climate of fear and uncertainty, the increased workload and the risk of becoming infected and being contagious, had a strong impact on healthcare staff and organizations. This highlighted the importance of interventions aimed at providing psychological support to ICUs, involving patients, their relatives, and the staff;this might involve the reorganisation of the daily routine and rearrangement of ICU staff duties.AIM To conduct a systematic review of psychological issues in ICUs during the COVID-19 pandemic involving patients, their relatives, and ICU staff.METHODS We investigated the PubMed and the ClinicalTrials.gov databases and found 65 eligible articles,upon which we commented.RESULTS Our results point to increased perceived stress and psychological distress in staff, patients and their relatives and increased worry for being infected with severe acute respiratory syndrome coronavirus-2 in patients and relatives. Furthermore, promising results were obtained for some psychological programmes aiming at improving psychological measures in all ICU categories.CONCLUSION As the pandemic limited direct inter-individual interactions, the role of interventions using digital tools and virtual reality is becoming increasingly important. All considered, our results indicate an essential role for psychologists in ICUs.

Echography in brain imaging in intensive care unit: State of the art

Transcranial sonography(TCS)is an ultrasound-based imaging technique,which allows the identification of several structures within the brain parenchyma.In the past it has been applied for bedside assessment of different intracranial pathologies in children.Pres-ently,TCS is also used on adult patients to diagnose intracranial space occupying lesions of various origins,intracranial hemorrhage,hydrocephalus,midline shift and neurodegenerative movement disorders,in both acute and chronic clinical settings.In comparison with conventional neuroimaging methods(such as com-puted tomography or magnetic resonance),TCS has the advantages of low costs,short investigation times,repeatability,and bedside availability.These noninva-sive characteristics,together with the possibility of of-fering a continuous patient neuro-monitoring system,determine its applicability in the monitoring of multiple emergency and non-emergency settings.Currently,TCS is a still underestimated imaging modality that requires a wider diffusion and a qualified training process.In this review we focused on the main indications of TCSfor the assessment of acute neurologic disorders in in-tensive care unit.

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 out-come 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<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.