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.

Background The study aimed to describe the epidemiology and outcomes of hospital-acquired bloodstream infections (HABSIs) between COVID-19 and non-COVID-19 critically ill patients. Methods We used data from the Eurobact II study, a prospective observational multicontinental cohort study on HABSI treated in ICU. For the current analysis, we selected centers that included both COVID-19 and non-COVID-19 critically ill patients. We performed descriptive statistics between COVID-19 and non-COVID-19 in terms of patients’ characteristics, source of infection and microorganism distribution. We studied the association between COVID-19 status and mortality using multivariable fragility Cox models. Results A total of 53 centers from 19 countries over the 5 continents were eligible. Overall, 829 patients (median age 65 years [IQR 55; 74]; male, n = 538 [64.9%]) were treated for a HABSI. Included patients comprised 252 (30.4%) COVID-19 and 577 (69.6%) non-COVID-19 patients. The time interval between hospital admission and HABSI was similar between both groups. Respiratory sources (40.1 vs. 26.0%, p < 0.0001) and primary HABSI (25.4% vs. 17.2%, p = 0.006) were more frequent in COVID-19 patients. COVID-19 patients had more often enterococcal (20.5% vs. 9%) and Acinetobacter spp. (18.8% vs. 13.6%) HABSIs. Bacteremic COVID-19 patients had an increased mortality hazard ratio (HR) versus non-COVID-19 patients (HR 1.91, 95% CI 1.49–2.45). Conclusions We showed that the epidemiology of HABSI differed between COVID-19 and non-COVID-19 patients. Enterococcal HABSI predominated in COVID-19 patients. COVID-19 patients with HABSI had elevated risk of mortality. Trial registration ClinicalTrials.org number NCT03937245. Registered 3 May 2019.

Invasive fungal infections (IFIs) have increasingly been recognized as a serious clinical concern in critically-ill patients admitted to the intensive care units (ICUs). The most abundant pathogens in this population are Candida species. As such, a clear understanding on the epidemiology of this infection seems to be a key step in providing appropriate treatment. Expert input forums are among the practical approaches to defi ne locally-adapted clinicalpathways with regard to debated medical perspectives. To agree upon a shared approach towards IFI management in ICU, an interdisciplinary panel of experts from infectious diseases and intensive care fi elds met up in Shiraz on 28 November 2015 within the IFI-Clinical Forum (IFI-CF).This clinical forum aimed to view the available evidence, taking into consideration the recent practice guidelines on IFIs management in the ICU to arrive at an agreed position in current clinical practice. The aim of this summary is to discuss IFIs in ICU from epidemiology, the range of pathophysiology from colonization to the invasive infections, risk prediction, diagnosis and treatment perspectives.

A bstract Background Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). Aims and objectives To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. Materials and methods In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. Results A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O 2 saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. Conclusion In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients’ early data, particularly in low- and middle-income countries where their resources are as limited as Iran. How to cite this article Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688–695. Ethics approval This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018).

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