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The 4C Deterioration model was developed and validated on data collected in UK hospitals until August 26, 2020, but has not yet been validated in the presence of SARS-CoV-2 variants and novel treatment regimens that have emerged subsequently. In this first validation study of the 4C Deterioration model on patients admitted between August 27, 2020 and April 16, 2021 we found, despite a slightly overestimation of risk, that the discrimination (area under the curve 0.75, 95% CI 0.71-0.78) and calibration of the model remained consistent with the development study, strengthening the evidence for adopting this model into clinical practice.

Introduction: We describe the clinical features and inpatient trajectories of older adults hospitalized with COVID-19 and explore relationships with frailty. Methods: This retrospective observational study included older adults admitted as an emergency to a University Hospital who were diagnosed with COVID-19. Patient characteristics and hospital outcomes, primarily inpatient death or death within 14 days of discharge, were described for the whole cohort and by frailty status. Associations with mortality were further evaluated using Cox Proportional Hazards Regression (Hazard Ratio (HR), 95% Confidence Interval). Results: 214 patients (94 women) were included of whom 142 (66.4%) were frail with a median Clinical Frailty Scale (CFS) score of 6. Frail compared to nonfrail patients were more likely to present with atypical symptoms including new or worsening confusion (45.1% vs. 20.8%, p &lt, 0.001) and were more likely to die (66% vs. 16%, p = 0.001). Older age, being male, presenting with high illness acuity and high frailty were independent predictors of death and a dose–response association between frailty and mortality was observed (CFS 1–4: reference, CFS 5–6: HR 1.78, 95% CI 0.90, 3.53, CFS 7–8: HR 2.57, 95% CI 1.26, 5.24). Conclusions: Clinicians should have a low threshold for testing for COVID-19 in older and frail patients during periods of community viral transmission, and diagnosis should prompt early advanced care planning.

Background: While numerous point-of-admission disease severity models for COVID-19 have been proposed, disease stratification that accounts for changes in a patient’s condition while in hospital is urgently needed to facilitate patient management and resource allocation. Methods: We developed a prognostic model for 48-hour in-hospital mortality using 473 consecutive patients with COVID-19 presenting to a UK hospital between March 1 and September 12, 2020; and temporally validated using data on 405 patients presenting between September 13, 2020 and January 3, 2021.The primary outcome was all-cause in-hospital mortality. We additionally considered the competing risks of discharge from hospital and transfer to a tertiary Intensive Care Unit for extracorporeal membrane oxygenation. We adopted a landmarking approach to dynamic prediction that accounts for competing risks and informative missingness, and selected predictors using penalised regression. The model estimates, at any point during a hospital visit, the probability of in-hospital mortality during the next 48 hours. Results: Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, SpO2/FiO2 ratio, white cell count, presence of acidosis (pH < 7.35) and Interleukin-6. Internal validation achieved an AUROC of 0.90 (95% CI 0.87–0.93) and temporal validation gave an AUROC of 0.91 (95% CI 0.88-0.95). Interpretation: Our model uniquely incorporates both static risk factors (e.g. age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. External validation outside the study hospital will be required before adoption. Funding: NIHR Cambridge Biomedical Research Centre, UKRI Medical Research Council Declaration of Interest: None to declare. Ethical Approval: The study was approved by a UK Health Research Authority ethics committee (20/WM/0125). Patient consent was waived because the de-identified data presented here were collected during routine clinical practice; there was no requirement for informed consent.

Background: A comprehensive description of the clinical characteristics, inpatient trajectory and relationship with frailty of older inpatients admitted with COVID-19 is essential in the management of older adults during the COVID-19 pandemic. The aim of this study was to describe the clinical features and inpatient trajectory of older inpatients with confirmed COVID -19.Methods: This was a retrospective observational study of hospitalised older adults. Subjects include unscheduled medical admissions of older inpatients to a University Hospital with laboratory and clinically confirmed COVID-19. The primary outcome was death during the inpatient stay or within 14 days of discharge after a maximum follow up time of 45 days. The characteristics of the cohort were described in detail as a whole and by frailty status.Results: 214 patients were included in this study with a mean length of stay of 11 days (Range 6 to 18 days), of whom 140 (65.4%) patients were discharged and 74 (34.6%) patients died in hospital. 142 (66.4%) patients were frail with median Clinical Frailty Scale (CFS) score of 6. Frail patients were more likely to present with atypical symptoms including new or worsening confusion compared to non-frail patients (20.8% vs 45.1%, pConclusions: Older adult inpatients with COVID-19 infection are likely to present with atypical symptoms, experience delirium and have a high mortality, especially if they are also living with frailty. Clinicians should have a low threshold for testing for COVID-19 in older and frail patients presenting to hospital as an emergency during periods when there is community transmission of COVID-19 and, when diagnosed, this should prompt early advanced care planning with the patient and family.

Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention.To develop and validate a model based on data available at ICU admission to predict delirium development during a patient's complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development.Prospective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU.In total, 2914 patients were included. Delirium incidence was 23.6%. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95% confidence interval (CI) 0.73-0.77] in the development dataset and 0.75 (95% CI 0.71-0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95% CI 0.67-0.74), for delirium that developed2 days, to 0.81 (95% CI 0.78-0.84), for delirium that developed6 days.Patients' delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium.

OBJECTIVE A recent randomized trial compared prandial insulin aspart (IAsp) with human insulin in type 1 diabetic pregnancy. The aim of this exploratory analysis was to investigate the incidence of severe hypoglycemia during pregnancy and compare women enrolled preconception with women enrolled during early pregnancy. RESEARCH DESIGN AND METHODS IAsp administered immediately before each meal was compared with human insulin administered 30 min before each meal in 99 subjects (44 to IAsp and 55 to human insulin) randomly assigned preconception and in 223 subjects (113 for IAsp and 110 for human insulin) randomly assigned in early pregnancy (

The advantages of Bayesian statistical approaches, such as flexibility and the ability to acknowledge uncertainty in all parameters, have made them the prevailing method for analysing the spread of infectious diseases in human or animal populations. We introduce a Bayesian approach to experimental host-pathogen systems that shares these attractive features. Since uncertainty in all parameters is acknowledged, existing information can be accounted for through prior distributions, rather than through fixing some parameter values. The non-linear dynamics, multi-factorial design, multiple measurements of responses over time and sampling error that are typical features of experimental host-pathogen systems can also be naturally incorporated. We analyse the dynamics of the free-living protozoan Paramecium caudatum and its specialist bacterial parasite Holospora undulata. Our analysis provides strong evidence for a saturable infection function, and we were able to reproduce the two waves of infection apparent in the data by separating the initial inoculum from the parasites released after the first cycle of infection. In addition, the parameter estimates from the hierarchical model can be combined to infer variations in the parasite's basic reproductive ratio across experimental groups, enabling us to make predictions about the effect of resources and host genotype on the ability of the parasite to spread. Even though the high level of variability between replicates limited the resolution of the results, this Bayesian framework has strong potential to be used more widely in experimental ecology.