The outbreak of coronavirus disease 2019 (COVID-19) in December 2019 in the city of Wuhan in Mainland China has spread across the globe with >100,000 infected individuals and 3000 deaths reported in 93 countries as of 7 March 2020. We report a case of COVID-19 infection in a 64-year-old man who developed rapidly worsening respiratory failure and acute respiratory distress syndrome (ARDS) that required intubation. As the clinical spectrum of COVID-19 infection ranges from mild illness to ARDS with high mortality risk, there is need for research that identifies early markers of disease severity. Current evidence suggests that patients with advanced age, dyspnoea or pre-existing comorbidities should be monitored closely, especially at 1–2 weeks after symptom onset. It remains to be seen whether laboratory findings such as lymphopaenia or elevated lactate dehydrogenase may serve as early surrogates for critical illness or markers of disease recovery. Management of ARDS in COVID-19 patients remains supportive while we await results of drug trials. More studies are needed to understand the incidence and outcomes of ARDS and critical illness from COVID-19 infection which are important for critical care management of patients and resource planning. Key words: Intensive Care, Mortality, Pneumonia, Risk factors

IntroductionA key aim of triage is to identify those with high risk of cardiac arrest, as they require intensive monitoring, resuscitation facilities, and early intervention. We aim to validate a novel machine learning (ML) score incorporating heart rate variability (HRV) for triage of critically ill patients presenting to the emergency department by comparing the area under the curve, sensitivity and specificity with the modified early warning score (MEWS).MethodsWe conducted a prospective observational study of critically ill patients (Patient Acuity Category Scale 1 and 2) in an emergency department of a tertiary hospital. At presentation, HRV parameters generated from a 5-minute electrocardiogram recording are incorporated with age and vital signs to generate the ML score for each patient. The patients are then followed up for outcomes of cardiac arrest or death.ResultsFrom June 2006 to June 2008 we enrolled 925 patients. The area under the receiver operating characteristic curve (AUROC) for ML scores in predicting cardiac arrest within 72 hours is 0.781, compared with 0.680 for MEWS (difference in AUROC: 0.101, 95% confidence interval: 0.006 to 0.197). As for in-hospital death, the area under the curve for ML score is 0.741, compared with 0.693 for MEWS (difference in AUROC: 0.048, 95% confidence interval: -0.023 to 0.119). A cutoff ML score ≥ 60 predicted cardiac arrest with a sensitivity of 84.1%, specificity of 72.3% and negative predictive value of 98.8%. A cutoff MEWS ≥ 3 predicted cardiac arrest with a sensitivity of 74.4%, specificity of 54.2% and negative predictive value of 97.8%.ConclusionWe found ML scores to be more accurate than the MEWS in predicting cardiac arrest within 72 hours. There is potential to develop bedside devices for risk stratification based on cardiac arrest prediction.

Purpose In the critically ill, hospital-acquired bloodstream infections (HA-BSI) are associated with significant mortality. Granular data are required for optimizing management, and developing guidelines and clinical trials. Methods We carried out a prospective international cohort study of adult patients (≥ 18 years of age) with HA-BSI treated in intensive care units (ICUs) between June 2019 and February 2021. Results 2600 patients from 333 ICUs in 52 countries were included. 78% HA-BSI were ICU-acquired. Median Sequential Organ Failure Assessment (SOFA) score was 8 [IQR 5; 11] at HA-BSI diagnosis. Most frequent sources of infection included pneumonia (26.7%) and intravascular catheters (26.4%). Most frequent pathogens were Gram-negative bacteria (59.0%), predominantly Klebsiella spp. (27.9%), Acinetobacter spp . (20.3%), Escherichia coli (15.8%), and Pseudomonas spp . (14.3%). Carbapenem resistance was present in 37.8%, 84.6%, 7.4%, and 33.2%, respectively. Difficult-to-treat resistance (DTR) was present in 23.5% and pan-drug resistance in 1.5%. Antimicrobial therapy was deemed adequate within 24 h for 51.5%. Antimicrobial resistance was associated with longer delays to adequate antimicrobial therapy. Source control was needed in 52.5% but not achieved in 18.2%. Mortality was 37.1%, and only 16.1% had been discharged alive from hospital by day-28. Conclusions HA-BSI was frequently caused by Gram-negative, carbapenem-resistant and DTR pathogens. Antimicrobial resistance led to delays in adequate antimicrobial therapy. Mortality was high, and at day-28 only a minority of the patients were discharged alive from the hospital. Prevention of antimicrobial resistance and focusing on adequate antimicrobial therapy and source control are important to optimize patient management and outcomes. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-022-06944-2.

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