Background The COVID-19 pandemic has placed unprecedented strain on health-care systems. Frailty is being used in clinical decision making for patients with COVID-19, yet the prevalence and effect of frailty in people with COVID-19 is not known. In the COVID-19 in Older PEople (COPE) study we aimed to establish the prevalence of frailty in patients with COVID-19 who were admitted to hospital and investigate its association with mortality and duration of hospital stay.Methods This was an observational cohort study conducted at ten hospitals in the UK and one in Italy. All adults (≥18 years) admitted to participating hospitals with COVID-19 were included. Patients with incomplete hospital records were excluded. The study analysed routinely generated hospital data for patients with COVID-19. Frailty was assessed by specialist COVID-19 teams using the clinical frailty scale (CFS) and patients were grouped according to their score (1-2=fit; 3-4=vulnerable, but not frail; 5-6=initial signs of frailty but with some degree of independence; and 7-9=severe or very severe frailty). The primary outcome was in-hospital mortality (time from hospital admission to mortality and day-7 mortality).

Background C-reactive protein (CRP) is a non-specific acute phase reactant elevated in infection or inflammation. Higher levels indicate more severe infection and have been used as an indicator of COVID-19 disease severity. However, the evidence for CRP as a prognostic marker is yet to be determined. The aim of this study is to examine the CRP response in patients hospitalized with COVID-19 and to determine the utility of CRP on admission for predicting inpatient mortality. Methods Data were collected between 27 February and 10 June 2020, incorporating two cohorts: the COPE (COVID-19 in Older People) study of 1564 adult patients with a diagnosis of COVID-19 admitted to 11 hospital sites (test cohort) and a later validation cohort of 271 patients. Admission CRP was investigated, and finite mixture models were fit to assess the likely underlying distribution. Further, different prognostic thresholds of CRP were analysed in a time-to-mortality Cox regression to determine a cut-off. Bootstrapping was used to compare model performance [Harrell’s C statistic and Akaike information criterion (AIC)]. Results The test and validation cohort distribution of CRP was not affected by age, and mixture models indicated a bimodal distribution. A threshold cut-off of CRP ≥40 mg/L performed well to predict mortality (and performed similarly to treating CRP as a linear variable). Conclusions The distributional characteristics of CRP indicated an optimal cut-off of ≥40 mg/L was associated with mortality. This threshold may assist clinicians in using CRP as an early trigger for enhanced observation, treatment decisions and advanced care planning.

Background:Colorectal cancer (CRC) is common and associated with significant mortality. Current screening methods for CRC lack patient compliance. microRNAs (miRNAs), identified in body fluids, are negative regulators of gene expression and are dysregulated in many cancers, including CRC. This paper summarises studies identifying blood-based miRNAs dysregulated in CRC compared with healthy controls in an attempt to evaluate their use as a screening tool for the diagnosis of CRC.Methods:A search of electronic databases (PubMed and EMBASE) and grey literature was performed between January 2002 and April 2016. Studies reporting plasma or serum miRNAs in the diagnosis of CRC compared with healthy controls were selected. Patient demographics, type of patient sample (serum or plasma), method of miRNA detection, type of normalisation, and the number of significantly dysregulated miRNAs identified were recorded. Statistical evaluation of dysregulated miRNAs using sensitivity, specificity, and area under the curve (AUC) was performed.Results:Thirty-four studies investigating plasma or serum miRNAs in the diagnosis of CRC were included. A total of 31 miRNAs were found to be either upregulated (n=17) or downregulated (n=14) in CRC cases as compared with controls. Fourteen studies identified panels of ⩾2 dysregulated miRNAs. The highest AUC, 0.943, was identified using a panel of 4 miRNAs with 83.3% sensitivity and 93.1% specificity. Meta-analysis of studies identifying a single dysregulated miRNA in CRC cases compared with controls was performed. Overall sensitivity and specificity of 28 individual miRNAs in the diagnosis of CRC were 76% (95% CI 72%–80%) and 76% (95% CI 72%–80%), respectively, indicating good discriminative ability of miRNAs as biomarkers for CRC. These data did not change with sensitivity analyses.Conclusions:Blood-based miRNAs distinguish patients with CRC from healthy controls with high sensitivity and specificity comparable to other common and invasive currently used screening methods for CRC. In future, miRNAs may be used as a relatively non-invasive blood-based marker for detection of CRC.

OBJECTIVE(S) Develop a plasma-based microRNA (miRNA) diagnostic assay specific for colorectal neoplasms, building upon our prior work. BACKGROUND Colorectal neoplasms (colorectal cancer [CRC] and colorectal advanced adenoma [CAA]) frequently develop in individuals at ages when other common cancers also occur. Current screening methods lack sensitivity, specificity, and have poor patient compliance. METHODS Plasma was screened for 380 miRNAs using microfluidic array technology from a “Training” cohort of 60 patients, (10 each) control, CRC, CAA, breast (BC), pancreatic (PC) and lung (LC) cancer. We identified uniquely dysregulated miRNAs specific for colorectal neoplasia (p<0.05, false discovery rate: 5%, adjusted α=0.0038). These miRNAs were evaluated using single assays in a “Test” cohort of 120 patients. A mathematical model was developed to predict blinded sample identity in a 150 patient “Validation” cohort using repeat-sub-sampling validation of the testing dataset with 1000 iterations each to assess model detection accuracy. RESULTS Seven miRNAs (miR-21, miR-29c, miR-122, miR-192, miR-346, miR-372, miR-374a) were selected based upon p-value, area-under-the-curve (AUC), fold-change, and biological plausibility. AUC (±95% CI) for “Test” cohort comparisons were 0.91 (0.85-0.96), 0.79 (0.70-0.88) and 0.98 (0.96-1.0), respectively. Our mathematical model predicted blinded sample identity with 69-77% accuracy between all neoplasia and controls, 67-76% accuracy between colorectal neoplasia and other cancers, and 86-90% accuracy between colorectal cancer and colorectal adenoma. CONCLUSIONS Our plasma miRNA assay and prediction model differentiates colorectal neoplasia from patients with other neoplasms and from controls with higher sensitivity and specificity compared to current clinical standards.