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ObjectivesTo develop cross-validated prediction models for severe outcomes in COVID-19 using blood biomarker and demographic data; Demonstrate best practices for clinical data curation and statistical modelling decisions, with an emphasis on Bayesian methods. DesignRetrospective observational cohort study. SettingMulticentre across National Health Service (NHS) trusts in Southwest region, England, UK. ParticipantsHospitalised adult patients with a positive SARS-CoV 2 by PCR during the first wave (March - October 2020). 843 COVID-19 patients (mean age 71, 45% female, 32% died or needed ICU stay) split into training (n=590) and validation groups (n=253) along with observations on demographics, co-infections, and 30 laboratory blood biomarkers. Primary outcome measuresICU admission or death within 28-days of admission to hospital for COVID-19 or a positive PCR result if already admitted. ResultsPredictive regression models were fit to predict primary outcomes using demographic data and initial results from biomarker tests collected within 3 days of admission or testing positive if already admitted. Using all variables, a standard logistic regression yielded an internal validation median AUC of 0.7 (95% Interval [0.64,0.81]), and an external validation AUC of 0.67 [0.61, 0.71], a Bayesian logistic regression using a horseshoe prior yielded an internal validation median AUC of 0.78 [0.71, 0.85], and an external validation median AUC of 0.70 [0.68, 0.71]. Variable selection performed using Bayesian predictive projection determined a four variable model using Age, Urea, Prothrombin time and Neutrophil-Lymphocyte ratio, with a median AUC of 0.74 [0.67, 0.82], and external validation AUC of 0.70 [0.69, 0.71]. ConclusionsOur study reiterates the predictive value of previously identified biomarkers for COVID-19 severity assessment. Given the small data set, the full and reduced models have decent performance, but would require improved external validation for clinical application. The study highlights a variety of challenges present in complex medical data sets while maintaining best statistical practices with an emphasis on showcasing recent Bayesian methods.
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IntroductionHospitalisations relating to acute respiratory deteriorations (ARD) in Interstitial Lung Disease (ILD) have poor outcomes. Factors predicting adverse outcomes are not fully understood and data addressing the use of illness severity scores in prognostication are limited. ObjectiveTo validate the use of CURB-65 and NEWS-2 severity scores to predict mortality following ARD-ILD hospitalisation. MethodsA dual-centre prospective observational cohort study of all adults ([≥]18y) hospitalised with ARD-ILD in Bristol, UK (n=179). Gender-Age-Physiology (GAP), CURB-65 and NEWS-2 scores were calculated for each eligible admission. Receiver operating characteristics (ROC) curve analysis was used to quantify the strength of discrimination for NEWS-2 and CURB-65 scores. Univariable and multivariable logistic regression analyses were performed to explore the relationship between baseline severity scores and mortality. ResultsGAP showed some merit at predicting 30-day mortality (AUC=0.64, P=0.015); whereas CURB-65 showed modest predictive value for in-hospital (AUC=0.72, P<0.001) and 90-day mortality (AUC=0.67, P<0.001). NEWS-2 showed higher predictive value for in-hospital (AUC=0.80, P<0.001) and 90-day mortality (AUC=0.75, P<0.001), with an optimal derived cut-off [≥]6.5 found to be sensitive and specific for predicting in-hospital (83% and 63%) and 90-day (73% and 72%) mortality. In exploratory analyses, GAP score addition improved the predictive ability of NEWS-2 against 30-day mortality and CURB-65 across all time-periods. ConclusionNEWS-2 has good discriminatory value for predicting in-hospital mortality and moderate discriminatory value for predicting 90-day mortality. The optimal NEWS-2 cut-off value determined was the same as in a previous retrospective cohort, confirming the NEWS-2 score shows promise in predicting mortality following ARD-ILD hospitalisation. KEY MESSAGESO_ST_ABSWhat is the key question?C_ST_ABS- Can NEWS-2 and CURB-65 be used to predict inpatient mortality in a cohort of patients with acute respiratory deterioration on a background of known interstitial lung disease? What is the bottom line?- The NEWS-2 score shows high sensitivity and specificity in predicting both 90-day and in-hospital mortality in patients hospitalised with ARD-ILD - Whilst the CURB-65 score showed high sensitivity for predicting mortality, there was a low specificity, and did not add value to the predictive ability of the NEWS-2 score. Why read on?- This analysis included 179 patients from two study sites and provides, for the first time, prospective evidence for utilising NEWS-2 and CURB-65 as tools to predict in-hospital and post hospitalisation morbidity.
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Limited data exist assessing severity of disease in adults hospitalised with Omicron SARS-CoV-2 variant infections, and to what extent patient-factors, including vaccination and pre-existing disease, affect variant-dependent disease severity. This prospective cohort study of all adults ([≥]18 years of age) hospitalised at acute care hospitals in Bristol, UK assessed disease severity using 3 different measures: FiO2 >28%, World Health Organization (WHO) outcome score >5, and hospital length of stay (LOS) >3 days following admission for Omicron or Delta variant infection. Independent of other variables, including vaccination, Omicron variant infection was associated with a statistically lower severity compared to Delta; risk reductions were 58%, 67%, and 16% for FiO2, WHO score, and LOS, respectively. Younger age and vaccination with two or three doses were also independently associated with lower COVID-19 severity. Despite lower severity relative to Delta, Omicron infection still resulted in substantial patient and public health burden following admission.
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Neutrophils are vital in defence against pathogens but excessive neutrophil activity can lead to tissue damage and promote acute respiratory distress syndrome (ARDS). COVID-19 is associated with systemic expansion of immature neutrophils but the functional consequences of this shift to immaturity are not understood. We used flow cytometry to investigate activity and phenotypic diversity of circulating neutrophils in acute and convalescent COVID-19 patients. First, we demonstrate hyperactivation of immature CD10- subpopulations in severe disease, with elevated markers of secondary granule release. Partially activated immature neutrophils were detectable three months post symptom onset, indication long term myeloid dysregulation in convalescent COVID-19 patients. Second, we demonstrate that neutrophils from moderately ill patients downregulate the chemokine receptor CXCR2, while neutrophils from severely ill individuals failed to do so, suggesting altered ability for organ trafficking and a potential mechanism for induction of disease tolerance. CD10-and CXCR2hi neutrophil subpopulations were enriched in severe disease and may represent prognostic biomarkers for identification of individuals at high risk of progressing to severe COVID-19.
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For patients with pneumonia and COVID19 repeating chest radiography is recommend in current British Thoracic Society (BTS) guidelines. Over two distinct time periods during the COVID19 pandemic (Aug-Dec 2020, Jun-Aug 2021) we undertook an audit of 829 patients hospitalised with infective radiological change (pneumonia=481, COVID19=348). 654/829 patients (79%) required radiological follow-up under BTS guideline criteria. 414/654 (63%) were planned, 322/654 (49%) occurred and, of patients receiving radiological follow-up, most occurred within BTS timelines (86%). Further audits should be conducted to ensure BTS guidelines adherence, to avoid delay in diagnosing underlying malignancy or chronic lung disease.
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The SARS-CoV-2 virus causes COVID-19, an infection capable of causing severe disease and death but which may also be asymptomatic or oligosymptomatic in many individuals. While several risk factors, including age, have been described, the mechanisms of this variation are poorly understood. Several studies have described associations between blood group and COVID-19 severity, while others do not. Expression of ABO glycans on secreted proteins and non-erythroid cells is controlled by a fucosyltransferase (FUT2). Inactivating mutations result in a non-secretor phenotype which is known to protect against some viral infections. We investigated whether ABO or secretor status was associated with COVID-19 severity. Data combined from healthcare records and laboratory tests (n=275) of SARS-CoV-2 PCR positive patients hospitalised with COVID-19, confirmed higher than expected numbers of blood group A individuals compared to O (RR=1.24, CI 95% [1.05,1.47], P=0.0111). There was also a significant association between group A and COVID-19-related cardiovascular complications (RR=2.56, CI 95% [1.43,4.55], P=0.0011) which is independent of gender. Molecular analysis of phenotype revealed that group A patients who are non-secretors are significantly less likely to be hospitalised than secretors. In a larger cohort of 1000 convalescent plasma donors, among whom the majority displayed COVID-19 symptoms and only a small minority required hospitalisation, group A non-secretors were slightly over-represented. Our findings indicate that group A non-secretors are not resistant to infection by SARS-CoV-2, but they are likely to experience a less severe form of its associated disease. Key PointsO_LIBlood group type A is associated with an increased risk of cardiovascular complications in COVID-19 patients. C_LIO_LIFUT2 "non-secretor" status reduces the risk of severe COVID-19 outcomes in patients with blood group A. C_LI
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BackgroundCOVID-19 causes a wide spectrum of disease. The incidence and severity of sequelae after the acute infection is uncertain. Data measuring the longer-term impact of COVID-19 on symptoms, radiology and pulmonary function are urgently needed to plan follow-up services. MethodsConsecutive patients hospitalised with COVID-19 were prospectively recruited to this observational study with outcomes recorded at 28-days. All were invited to a systematic follow up at 8-12 weeks, including chest radiograph, spirometry, exercise test, bloods, and health-related quality of life (HRQoL) questionnaires. FindingsBetween 30th March and 3rd June 2020, 163 patients with COVID-19 were recruited. Median hospital length of stay was 5 days (IQR 2-8) and 19 patients died. At 8-12 weeks post admission, 134 patients were available for follow up and 110 attended. Most (74%) had persistent symptoms (notably breathlessness and excessive fatigue) with reduced HRQoL. Only patients who required oxygen therapy in hospital had abnormal radiology, clinical examination or spirometry at follow up. Thirteen (12%) patients had an abnormal chest X-ray with improvement in all but 2 from admission. Eleven (10%) had restrictive spirometry. Blood test abnormalities had returned to baseline in the majority (104/110). InterpretationPatients with COVID-19 remain highly symptomatic at 8-12 weeks, however, clinical abnormalities requiring action are infrequent, especially in those without a supplementary oxygen requirement during their acute illness. This has significant implications for physicians assessing patients with persistent symptoms, suggesting that a more holistic approach focussing on rehabilitation and general wellbeing is paramount. FundingSouthmead Hospital Charity
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ObjectivesTo develop a regional model of COVID-19 dynamics, for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West of England (SW) as an example case. DesignOpen-source age-structured variant of a susceptible-exposed-infectious-recovered (SEIR) deterministic compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths. SettingSW at a time considered early in the pandemic, where National Health Service (NHS) authorities required evidence to guide localised planning and support decision-making. ParticipantsPublicly-available data on COVID-19 patients. Primary and secondary outcome measuresThe expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ("R") number over time. ResultsSW model projections indicate that, as of the 11th May 2020 (when lockdown measures were eased), 5,793 (95% credible interval, CrI, 2,003 - 12,051) individuals were still infectious (0.10% of the total SW England population, 95%CrI 0.04 - 0.22%), and a total of 189,048 (95%CrI 141,580 - 277,955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95%CrI 2.5 - 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on the 11th May 2020 was predicted to be 701 (95%CrI 169 - 1,543) and 110 (95%CrI 8 - 464) respectively. The R value in SW England was predicted to be 2.6 (95%CrI 2.0 - 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95%CrI 1.8 - 2.9) and lockdown/ school closures further reducing the R value to 0.6 (95CrI% 0.5 - 0.7). ConclusionsThe developed model has proved a valuable asset for local and regional healthcare services. The model will be used further in the SW as the pandemic evolves, and - as open source software - is portable to healthcare systems in other geographies. Future work/ applicationsO_LIOpen-source modelling tool available for wider use and re-use. C_LIO_LICustomisable to a number of granularities such as at the local, regional and national level. C_LIO_LISupports a more holistic understanding of intervention efficacy through estimating unobservable quantities, e.g. asymptomatic population. C_LIO_LIWhile not presented here, future use of the model could evaluate the effect of various interventions on transmission of COVID-19. C_LIO_LIFurther developments could consider the impact of bedded capacity in terms of resulting excess deaths. C_LI
