Background: The COVID-19 pandemic has led highly developed healthcare systems to the brink of collapse due to the large numbers of patients being admitted into hospitals. One of the potential prognostic indicators in patients with COVID-19 is frailty. The degree of frailty could be used to assist both the triage into intensive care, and decisions regarding treatment limitations. Our study sought to determine the interaction of frailty and age in elderly COVID-19 ICU patients. Methods: A prospective multicentre study of COVID-19 patients ≥ 70 years admitted to intensive care in 138 ICUs from 28 countries was conducted. The primary endpoint was 30-day mortality. Frailty was assessed using the clinical frailty scale. Additionally, comorbidities, management strategies and treatment limitations were recorded. Results: The study included 1346 patients (28% female) with a median age of 75 years (IQR 72–78, range 70–96), 16.3% were older than 80 years, and 21% of the patients were frail. The overall survival at 30 days was 59% (95% CI 56–62), with 66% (63–69) in fit, 53% (47–61) in vulnerable and 41% (35–47) in frail patients (p < 0.001). In frail patients, there was no difference in 30-day survival between different age categories. Frailty was linked to an increased use of treatment limitations and less use of mechanical ventilation. In a model controlling for age, disease severity, sex, treatment limitations and comorbidities, frailty was independently associated with lower survival. Conclusion: Frailty provides relevant prognostic information in elderly COVID-19 patients in addition to age and comorbidities. Trial registration Clinicaltrials.gov: NCT04321265, registered 19 March 2020.

Introduction: Delirium is a common occurrence in critically ill patients and is associated with an increase in morbidity and mortality. Septic patients with delirium may differ from a general critically ill population. The aim of this investigation was to study the relationship between systemic inflammation and the development of delirium in septic and non-septic critically ill patients. Methods: We performed a prospective cohort study in a 20-bed mixed intensive care unit (ICU) including 78 (delirium = 31; non-delirium = 47) consecutive patients admitted for more than 24 hours. At enrollment, patients were allocated to septic or non-septic groups according to internationally agreed criteria. Delirium was diagnosed using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) during the first 72 hours of ICU admission. Blood samples were collected within 12 hours of enrollment for determination of tumor necrosis factor (TNF)-α, soluble TNF Receptor (STNFR)-1 and -2, interleukin (IL)-1β, IL-6, IL-10 and adiponectin. Results: Out of all analyzed biomarkers, only STNFR1 (P = 0.003), STNFR2 (P = 0.005), adiponectin (P = 0.005) and IL-1β (P < 0.001) levels were higher in delirium patients. Adjusting for sepsis and sedation, these biomarkers were also independently associated with delirium occurrence. However, none of them were significant influenced by sepsis. Conclusions: STNFR1, STNFR2, adiponectin and IL-1β were associated with delirium. Sepsis did not modify the relationship between the biomarkers and delirium occurrence.

The early metabolic signatures associated with the progression of septic shock and with responsiveness to therapy can be useful for developing target therapy. The Sequential Organ Failure Assessment (SOFA) score is used for stratifying risk and predicting mortality. This study aimed to verify whether different responses to therapy, assessed as changes in SOFA score at admission (T1, acute phase) and 48 h later (T2, post-resuscitation), are associated with different metabolite patterns. We examined the plasma metabolome of 21 septic shock patients (pts) enrolled in the Shockomics clinical trial (NCT02141607). Patients for which SOFA_T2 was >8 and Δ = SOFA_T1 − SOFA_T2 < 5, were classified as not responsive to therapy (NR, 7 pts), the remaining 14 as responsive (R). We combined untargeted and targeted mass spectrometry-based metabolomics strategies to cover the plasma metabolites repertoire as far as possible. Metabolite concentration changes from T1 to T2 (Δ = T2 − T1) were used to build classification models. Our results support the emerging evidence that lipidome alterations play an important role in individual patients’ responses to infection. Furthermore, alanine indicates a possible alteration in the glucose-alanine cycle in the liver, providing a different picture of liver functionality from bilirubin. Understanding these metabolic disturbances is important for developing any effective tailored therapy for these patients.