Showing papers by "Colin J Crooks published in 2020"

COVID-19-associated hyperinflammation and escalation of patient care: a retrospective longitudinal cohort study.

Abstract: Summary 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.

Incidence, prevalence and mortality of autoimmune hepatitis in England 1997-2015. A population-based cohort study

Abstract: Background & Aims There are few population‐based studies of the incidence and mortality of autoimmune hepatitis. The burden of the disease and how it has changed over time have not been fully explored. We conducted a population‐based cohort study on the incidence and mortality of autoimmune hepatitis in England, 1997–2015. Methods From the Clinical Practice Research Datalink we included 882 patients diagnosed with autoimmune hepatitis in England, 1997‐2015. The patients were followed through 2015, and we calculated the sex‐ and age‐ standardised incidence and prevalence of autoimmune hepatitis. We examined variation in incidence by sex, age, calendar year, geographical region, and socioeconomic status, and incidence rate ratios were calculated with Poisson regression. We calculated all‐cause and cause‐specific mortality. Results The overall standardised incidence rate of autoimmune hepatitis was 2.08 (95% confidence interval 1.94‐2.22) per 100,000 population per year, higher in women, higher in older age, and independent of region and socioeconomic status. From 1997 to 2015 the incidence doubled from 1.27 (95% confidence interval 0.51‐2.02) to 2.56 (95% confidence interval 1.79‐3.33) per 100,000 population per year. The 10‐year cumulative all‐cause mortality was 31.9% (95% confidence interval 27.6‐36.5), and the 10‐year cumulative liver‐related mortality, including hepatocellular carcinoma was ~ 10.5%. Conclusions This population‐based study showed that the incidence of autoimmune hepatitis doubled over an eighteen‐year period. The incidence was particularly high in older women and was similar across all regions of England and independent of socioeconomic status. Patients with autoimmune hepatitis had a high mortality.

Predicting the need for escalation of care or death from repeated daily clinical observations and laboratory results in patients with SARS-CoV-2 during 2020: a retrospective population-based cohort study from the United Kingdom

Abstract: Objectives Currently used prognostic tools for patients with SARS-CoV-2 infection are based on clinical and laboratory parameters measured at a single point in time, usually on admission. We aimed to determine how dynamic changes in clinical and laboratory parameters relate to SARS-CoV-2 prognosis. Design retrospective, observational cohort study using routinely collected clinical data to model the dynamic change in prognosis of SARS-CoV-2. Setting a single, large hospital in England. Participants all patients with confirmed SARS-CoV-2 admitted to Nottingham University Hospitals (NUH) NHS Trust, UK from 1st February 2020 until 30th November 2020. Main outcome measures Intensive Care Unit (ICU) admission, death and discharge from hospital. Statistical Methods We split patients into 1st (admissions until 30th June) and 2nd (admissions thereafter) waves. We incorporated all clinical observations, blood tests and other covariates from electronic patient records and follow up until death or 30 days from the point of hospital discharge. We modelled daily risk of admission to ICU or death with a time varying Cox proportional hazards model. Results 2,964 patients with confirmed SARS-CoV-2 were included. Of 1,374 admitted during the 1st wave, 593 were eligible for ICU escalation, and 466 had near complete ascertainment of all covariates at admission. Our validation sample included 1,590 confirmed cases, of whom 958 were eligible for ICU admission. Our model had good discrimination of daily need for ICU admission or death (C statistic = 0.87 (IQR 0.85-0.90)) and predicted this daily prognosis better than previously published scores (NEWS2, ISCARIC 4C). In validation in the 2nd wave the score overestimated escalation (calibration slope 0.55), whilst retaining a linear relationship and good discrimination (C statistic = 0.88 (95% CI 0.81 −0.95)). Conclusions A bespoke SARS-CoV-2 escalation risk prediction score can predict need for clinical escalation better than a generic early warning score or a single estimation of risk at admission. What is already known on this topic SARS-CoV-2 is a recently emerged viral infection, which presents typically with flu like symptoms, can have severe sequelae and has caused a pandemic during 2020. A number of risk factors for poor outcomes including obesity, age and comorbidity have been recognized. Risk scores have been developed to stratify risk of poor outcome for patients with SARS-CoV-2 at admission, but these do not take account of dynamic changes in severity of disease on a daily basis. What this study adds We have developed a dynamic risk score to predict escalation to ICU or death within the next 24 hours. Our score has good discrimination between those who will and not require ICU admission (or die) in both our derivation and validation cohorts. Our bespoke SARS-CoV-2 escalation risk prediction score can predict need for clinical escalation better than a generic early warning score or a single estimation of risk at admission.

Supervised Bayesian Statistical Learning to Identify Prognostic Risk Factor Patterns from Population Data.

Abstract: Current methods for building risk models assume averaged uniform effects across populations They use weighted sums of individual risk factors from regression models with only a few interactions, such as age This does not allow risk factor effects to vary in different morbidity contexts This study modified a supervised Bayesian statistical learning method of topic modelling, allowing individual factors to have different effects depending on a patient's other comorbidity This study used topic modelling to assess more than 71,000 unique risk factors in a population cohort of 14 million adults within routine data The model learnt prognostically important risk factor patterns that predicted 5 year survival, and the resulting model achieved excellent calibration and discrimination with a C statistic of 09 in a held out validation cohort The model explained 92% of the observed variation in 5 year survival in the population This paper validates using survival supervised Bayesian topic modelling within large routine electronic population health data to identify prognostically important risk factor patterns