Background A subset of patients with severe COVID-19 develop a hyperinflammatory syndrome, which might contribute to morbidity and mortality. This study explores a specific phenotype of COVID-19-associated hyperinflammation (COV-HI), and its associations with escalation of respiratory support and survival. Methods In this retrospective cohort study, we enrolled consecutive inpatients (aged ≥18 years) admitted to University College London Hospitals and Newcastle upon Tyne Hospitals in the UK with PCR-confirmed COVID-19 during the first wave of community-acquired infection. Demographic data, laboratory tests, and clinical status were recorded from the day of admission until death or discharge, with a minimum follow-up time of 28 days. We defined COV-HI as a C-reactive protein concentration greater than 150 mg/L or doubling within 24 h from greater than 50 mg/L, or a ferritin concentration greater than 1500 μg/L. Respiratory support was categorised as oxygen only, non-invasive ventilation, and intubation. Initial and repeated measures of hyperinflammation were evaluated in relation to the next-day risk of death or need for escalation of respiratory support (as a combined endpoint), using a multi-level logistic regression model. Findings We included 269 patients admitted to one of the study hospitals between March 1 and March 31, 2020, among whom 178 (66%) were eligible for escalation of respiratory support and 91 (34%) patients were not eligible. Of the whole cohort, 90 (33%) patients met the COV-HI criteria at admission. Despite having a younger median age and lower median Charlson Comorbidity Index scores, a higher proportion of patients with COV-HI on admission died during follow-up (36 [40%] of 90 patients) compared with the patients without COV-HI on admission (46 [26%] of 179). Among the 178 patients who were eligible for full respiratory support, 65 (37%) met the definition for COV-HI at admission, and 67 (74%) of the 90 patients whose respiratory care was escalated met the criteria by the day of escalation. Meeting the COV-HI criteria was significantly associated with the risk of next-day escalation of respiratory support or death (hazard ratio 2·24 [95% CI 1·62–2·87]) after adjustment for age, sex, and comorbidity. Interpretation Associations between elevated inflammatory markers, escalation of respiratory support, and survival in people with COVID-19 indicate the existence of a high-risk inflammatory phenotype. COV-HI might be useful to stratify patient groups in trial design. Funding None.

Background Juvenile-onset systemic lupus erythematosus (SLE) is a rare autoimmune rheumatic disease characterised by more severe disease manifestations, earlier damage accrual, and higher mortality than in adult-onset SLE. We aimed to use machine-learning approaches to characterise the immune cell profile of patients with juvenile-onset SLE and investigate links with the disease trajectory over time. Methods This study included patients who attended the University College London Hospital (London, UK) adolescent rheumatology service, had juvenile-onset SLE according to the 1997 American College of Rheumatology revised classification criteria for lupus or the 2012 Systemic Lupus International Collaborating Clinics criteria, and were diagnosed before 18 years of age. Blood donated by healthy age-matched and sex-matched volunteers who were taking part in educational events in the Centre for Adolescent Rheumatology Versus Arthritis at University College London (London, UK) was used as a control. Immunophenotyping profiles (28 immune cell subsets) of peripheral blood mononuclear cells from patients with juvenile-onset SLE and healthy controls were determined by flow cytometry. We used balanced random forest (BRF) and sparse partial least squares-discriminant analysis (sPLS-DA) to assess classification and parameter selection, and validation was by tenfold cross-validation. We used logistic regression to test the association between immune phenotypes and k-means clustering to determine patient stratification. Retrospective longitudinal clinical data, including disease activity and medication, were related to the immunological features identified. Findings Between Sept 5, 2012, and March 7, 2018, peripheral blood was collected from 67 patients with juvenile-onset SLE and 39 healthy controls. The median age was 19 years (IQR 13-25) for patients with juvenile-onset SLE and 18 years (16-25) for healthy controls. The BRF model discriminated patients with juvenile-onset SLE from healthy controls with 90•9% prediction accuracy. The top-ranked immunological features from the BRF model were confirmed using sPLS-DA and logistic regression, and included total CD4, total CD8, CD8 effector memory, and CD8 naive T cells, Bm1, and unswitched memory B cells, total CD14 monocytes, and invariant natural killer T cells. Using these markers patients were clustered into four distinct groups. Notably, CD8 T-cell subsets were important in driving patient stratification, whereas B-cell markers were similarly expressed across the cohort of patients with juvenile-onset SLE. Patients with juvenile-onset SLE and elevated CD8 effector memory T-cell frequencies had more persistently active disease over time, as assessed by the SLE disease activity index 2000, and this was associated with increased treatment with mycophenolate mofetil and an increased prevalence of lupus nephritis. Finally, network analysis confirmed the strong association between immune phenotype and differential clinical features. Interpretation Machine-learning models can define p...