Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life: an application to settings in Europe, Asia, Africa, and South America (preprint)

Long-term COVID-19 complications are a globally pervasive threat, but their plausible social drivers are often not prioritized. Here, we use data from a multinational consortium to quantify the relative contributions of social and clinical factors to differences in quality of life among participants experiencing long COVID and measure the extent to which social variables impacts can be attributed to clinical intermediates, across diverse contexts. In addition to age, neuropsychological and rheumatological comorbidities, educational attainment, employment status, and female sex were identified as important predictors of long COVID-associated quality of life days (long COVID QALDs). Furthermore, a great majority of their impacts on long COVID QALDs could not be tied to key long COVID-predicting comorbidities, such as asthma, diabetes, hypertension, psychological disorder, and obesity. In Norway, 90% (95% CI: 77%, 100%) of the effect of belonging to the highest versus lowest educational attainment quintile was not attributed to intermediate comorbidity impacts. The same was true for 86% (73%, 100%) of the protective effects of full-time employment versus all other employment status categories (excluding retirement) in the UK and 74% (46%,100%) of the protective effects of full-time employment versus all other employment status categories in a cohort of four middle-income countries (MIC). Of the effects of female sex on long COVID QALDs in Norway, UK, and the MIC cohort, 77% (46%,100%), 73% (52%, 94%), and 84% (62%, 100%) were unexplained by the clinical mediators, respectively. Our findings highlight that socio-economic proxies and sex may be as predictive of long COVID QALDs as commonly emphasized comorbidities and that broader structural determinants likely drive their impacts. Importantly, we outline a multi-method, adaptable causal machine learning approach for evaluating the isolated contributions of social disparities to long COVID quality of life experiences.

At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study (preprint)

By September, 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

Liver injury in hospitalized patients with COVID-19: An International observational cohort study (preprint)

Background: Using a large dataset, we evaluated prevalence and severity of alterations in liver enzymes in COVID-19 and association with patient-centred outcomes. Methods: We included hospitalized patients with confirmed or suspected SARS-CoV-2 infection from the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) database. Key exposure was baseline liver enzymes (AST, ALT, bilirubin). Patients were assigned Liver Injury Classification score based on 3 components of enzymes at admission: Normal; Stage I) Liver injury: any component between 1-3x upper limit of normal (ULN); Stage II) Severe liver injury: any component >= 3x ULN. Outcomes were hospital mortality, utilization of selected resources, complications, and durations of hospital and ICU stay. Analyses used logistic regression with associations expressed as adjusted odds ratios (OR) with 95% confidence intervals (CI). Results: Of 17,531 included patients, 46.2% (8099) and 8.2% (1430) of patients had stage 1 and 2 liver injury respectively. Compared to normal, stages 1 and 2 were associated with higher odds of mortality (OR 1.53 [1.37-1.71]; OR 2.50 [2.10-2.96]), ICU admission (OR 1.63 [1.48-1.79]; OR 1.90 [1.62-2.23]) and invasive mechanical ventilation (OR 1.43 [1.27-1.70]; OR 1.95 (1.55-2.45).Stages 1 and 2 were also associated with higher odds of developing sepsis (OR 1.38 [1.27-1.50]; OR 1.46 [1.25-1.70]), acute kidney injury (OR 1.13 [1.00-1.27]; OR 1.59 [1.32-1.91]), and acute respiratory distress syndrome (OR 1.38 [1.22-1.55]; OR 1.80 [1.49-2.17]). Conclusions: Liver enzyme abnormalities are common among COVID-19 patients and associated with worse outcomes.

Symptom-based case definitions for COVID-19: time and geographical variations for detection at hospital admission among 260,000 patients (preprint)

Introduction: Case definitions are used to guide clinical practice, surveillance, and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions. Methods: Patients were assessed using the CDC, ECDC, WHO, and UKHSA case definitions by age, region, and time. Case fatality ratios (CFR) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test-result were excluded. Results: 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC), and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years; geographical and time variations were also observed. Estimated CFRs were similar for the patients that met the case definitions. However, when more patients did not meet the case definition, the CFR increased. Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients (preprint)

BackgroundWhilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. MethodsHere, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. ResultsOur analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61 - 0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. ConclusionsAlthough clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome.

Use of an extended KDIGO definition to diagnose acute kidney injury in patients with COVID-19: A multinational study using the ISARIC–WHO clinical characterisation protocol

Background Acute kidney injury (AKI) is one of the most common and significant problems in patients with Coronavirus Disease 2019 (COVID-19). However, little is known about the incidence and impact of AKI occurring in the community or early in the hospital admission. The traditional Kidney Disease Improving Global Outcomes (KDIGO) definition can fail to identify patients for whom hospitalisation coincides with recovery of AKI as manifested by a decrease in serum creatinine (sCr). We hypothesised that an extended KDIGO (eKDIGO) definition, adapted from the International Society of Nephrology (ISN) 0by25 studies, would identify more cases of AKI in patients with COVID-19 and that these may correspond to community-acquired AKI (CA-AKI) with similarly poor outcomes as previously reported in this population. Methods and findings All individuals recruited using the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC)–World Health Organization (WHO) Clinical Characterisation Protocol (CCP) and admitted to 1,609 hospitals in 54 countries with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection from February 15, 2020 to February 1, 2021 were included in the study. Data were collected and analysed for the duration of a patient’s admission. Incidence, staging, and timing of AKI were evaluated using a traditional and eKDIGO definition, which incorporated a commensurate decrease in sCr. Patients within eKDIGO diagnosed with AKI by a decrease in sCr were labelled as deKDIGO. Clinical characteristics and outcomes—intensive care unit (ICU) admission, invasive mechanical ventilation, and in-hospital death—were compared for all 3 groups of patients. The relationship between eKDIGO AKI and in-hospital death was assessed using survival curves and logistic regression, adjusting for disease severity and AKI susceptibility. A total of 75,670 patients were included in the final analysis cohort. Median length of admission was 12 days (interquartile range [IQR] 7, 20). There were twice as many patients with AKI identified by eKDIGO than KDIGO (31.7% versus 16.8%). Those in the eKDIGO group had a greater proportion of stage 1 AKI (58% versus 36% in KDIGO patients). Peak AKI occurred early in the admission more frequently among eKDIGO than KDIGO patients. Compared to those without AKI, patients in the eKDIGO group had worse renal function on admission, more in-hospital complications, higher rates of ICU admission (54% versus 23%) invasive ventilation (45% versus 15%), and increased mortality (38% versus 19%). Patients in the eKDIGO group had a higher risk of in-hospital death than those without AKI (adjusted odds ratio: 1.78, 95% confidence interval: 1.71 to 1.80, p-value < 0.001). Mortality and rate of ICU admission were lower among deKDIGO than KDIGO patients (25% versus 50% death and 35% versus 70% ICU admission) but significantly higher when compared to patients with no AKI (25% versus 19% death and 35% versus 23% ICU admission) (all p-values <5 × 10−5). Limitations include ad hoc sCr sampling, exclusion of patients with less than two sCr measurements, and limited availability of sCr measurements prior to initiation of acute dialysis. Conclusions An extended KDIGO definition of AKI resulted in a significantly higher detection rate in this population. These additional cases of AKI occurred early in the hospital admission and were associated with worse outcomes compared to patients without AKI.

Use of an extended KDIGO definition to diagnose acute kidney injury in patients with COVID-19: A multinational study of the ISARIC cohort (preprint)

BackgroundAcute kidney injury (AKI) is one of the most common and significant problems in patients with COVID-19. However, little is known about the incidence and impact of AKI occurring in the community or early in the hospital admission. The traditional KDIGO definition can fail to identify patients for whom hospitalization coincides with recovery of AKI as manifested by a decrease in serum creatinine (sCr). We hypothesized that an extended KDIGO definition, adapted from the International Society of Nephrology 0by25 studies, would identify more cases of AKI in patients with COVID-19 and that these may correspond to community-acquired AKI with similarly poor outcomes as previously reported in this population. Methods and FindingsAll individuals in the ISARIC cohort admitted to hospital with SARS-CoV-2 infection from February 15th, 2020, to February 1st, 2021, were included in the study. Data was collected and analysed for the duration of a patients admission. Incidence, staging and timing of AKI were evaluated using a traditional and extended KDIGO (eKDIGO) definition which incorporated a commensurate decrease in serum creatinine. Patients within eKDIGO diagnosed with AKI by a decrease in sCr were labelled as deKDIGO. Clinical characteristic and outcomes - intensive care unit (ICU) admission, invasive mechanical ventilation and in-hospital death - were compared for all three groups of patients. The relationship between eKDIGO AKI and in-hospital death was assessed using survival curves and logistic regression, adjusting for disease severity and AKI susceptibility. 75,670 patients from 54 countries were included in the final analysis cohort. Median length of admission was 12 days (IQR 7, 20). There were twice as many patients with AKI identified by eKDIGO than KDIGO (31.7 vs 16.8%). Those in the eKDIGO group had a greater proportion of stage 1 AKI (58% vs 36% in KDIGO patients). Peak AKI occurred early in the admission more frequently among eKDIGO than KDIGO patients. Compared to those without AKI, patients in the eKDIGO group had worse renal function on admission, more in-hospital complications, higher rates of ICU admission (54% vs 23%) invasive ventilation (45% vs 15%) and increased mortality (38% vs 19%). Patients in the eKDIGO group had a higher risk of in-hospital death than those without AKI (adjusted OR: 1.78, 95% confidence interval: 1.71-1.8, p-value < 0.001). Mortality and rate of ICU admission were lower among deKDIGO than KDIGO patients (25% vs 50% death and 35% vs 70% ICU admission) but significantly higher when compared to patients with no AKI (25% vs 19% death and 35% vs 23% ICU admission) (all p values < 5x10-5). Limitations include ad hoc sCr sampling, exclusion of patients with less than two sCr measurements, and limited availability of sCr measurements prior to initiation of acute dialysis. ConclusionsThe use of an extended KDIGO definition to diagnose AKI in this population resulted in a significantly higher incidence rate compared to traditional KDIGO criteria. These additional cases of AKI appear to be occurring in the community or early in the hospital admission and are associated with worse outcomes than those without AKI. Author SummaryO_ST_ABSWhy was this study done?C_ST_ABSO_LIPrevious studies have shown that acute kidney injury (AKI) is a common problem among hospitalized patients with COVID-19. C_LIO_LIThe current biochemical criteria used to diagnose AKI may be insufficient to capture AKI that develops in the community and is recovering by the time a patient presents to hospital. C_LIO_LIThe use of an extended definition, that can identify AKI both during its development and recovery phase, may allow us to identify more patients with AKI. These patients may benefit from early management strategies to improve long term outcomes. C_LI What did the researchers do and find?O_LIIn this study, we examined AKI incidence, severity and outcomes among a large international cohort of patients with COVID-19 using both a traditional and extended definition of AKI. C_LIO_LIWe found that using the extended definition identified almost twice as many cases of AKI than the traditional definition (31.7 vs 16.8%). C_LIO_LIThese additional cases of AKI were generally less severe and occurred earlier in the hospital admission. Nevertheless, they were associated with worse outcomes, including ICU admission and in-hospital death (adjusted odds ratio: 1.78, 95% confidence interval: 1.71-1.8, p-value < 0.001) than those with no AKI. C_LI What do these findings mean?O_LIThe current definition of AKI fails to identify a large group of patients with AKI that appears to develop in the community or early in the hospital admission. C_LIO_LIGiven the finding that these cases of AKI are associated with worse admission outcomes than those without AKI, identifying and managing them in a timely manner is enormously important. C_LI

Post COVID-19 Condition in South Africa: 3-month follow-up after hospitalisation with SARS-CoV-2 (preprint)

Background Post COVID-19 Condition (PCC) as defined by WHO refers to a wide range of new, returning, or ongoing health problems experienced by COVID-19 survivors, and represents a rapidly emerging public health priority. We aimed to establish how this developing condition has impacted patients in South Africa and which population groups are at risk. Methods In this prospective cohort study, participants [≥]18 years who had been hospitalised with laboratory-confirmed SARS-CoV-2 infection during the second and third wave between December 2020 and August 2021 underwent telephonic follow-up assessment up at one-month and three-months after hospital discharge. Participants were assessed using a standardised questionnaire for the evaluation of symptoms, functional status, health-related quality of life and occupational status. Multivariable logistic regression models were used to determine factors associated with PCC. Findings In total, 1,873 of 2,413 (78%) enrolled hospitalised COVID-19 participants were followed up at three-months after hospital discharge. Participants had a median age of 52 years (IQR 41-62) and 960 (51.3%) were women. At three-months follow-up, 1,249 (66.7%) participants reported one or more persistent COVID-related symptom(s), compared to 1,978/2,413 (82.1%) at one-month post-hospital discharge. The most common symptoms reported were fatigue (50.3%), shortness of breath (23.4%), confusion or lack of concentration (17.5%), headaches (13.8%) and problems seeing/blurred vision (10.1%). On multivariable analysis, factors associated with new or persistent symptoms following acute COVID-19 were age [≥]65 years [adjusted odds ratio (aOR) 1.62; 95%confidence interval (CI) 1.00-2.61]; female sex (aOR 2.00; 95% CI 1.51-2.65); mixed ethnicity (aOR 2.15; 95% CI 1.26-3.66) compared to black ethnicity; requiring supplemental oxygen during admission (aOR 1.44; 95% CI 1.06-1.97); ICU admission (aOR 1.87; 95% CI 1.36-2.57); pre-existing obesity (aOR 1.44; 95% CI 1.09-1.91); and the presence of [≥]4 acute symptoms (aOR 1.94; 95% CI 1.19-3.15) compared to no symptoms at onset. Interpretation The majority of COVID-19 survivors in this cohort of previously hospitalised participants reported persistent symptoms at three-months from hospital discharge, as well as a significant impact of PCC on their functional and occupational status. The large burden of PCC symptoms identified in this study emphasises the need for a national health strategy. This should include the development of clinical guidelines and training of health care workers, in identifying, assessing and caring for patients affected by PCC, establishment of multidisciplinary national health services, and provision of information and support to people who suffer from PCC.

Risk Factors for 28-Day in-Hospital Mortality in Mechanically Ventilated Patients with COVID-19: An International Cohort Study (preprint)

Background: Risk factors associated with mortality in patients with coronavirus disease 2019 (COVID-19) on mechanical ventilation are still not fully elucidated. Thus, we aimed to identify patient-level factors, readily available at the bedside, associated with the risk of in-hospital mortality within 28 days from commencement of invasive mechanical ventilation (28-day IMV mortality) in patients with COVID-19. Methods: Prospective observational cohort study in 148 intensive care units in the global COVID-19 Critical Care Consortium . Patients with clinically suspected or laboratory confirmed COVID-19 infection admitted to the intensive care unit (ICU) from February 2 nd through December 29th, 2020, requiring IMV. No study-specific interventions were performed. Patient characteristics and clinical data were assessed upon ICU admission, the commencement of IMV and for 28 days thereafter. We primarily aimed to identify time-independent and time-dependent risk factors for 28-day IMV mortality. Results: : A total of 1713 patients were included in the survival analysis, 588 patients died in hospital within 28 days of commencing IMV (34.3%). Cox-regression analysis identified associations between the hazard of 28-day IMV mortality with age (HR 1.27 per 10-year increase in age, 95% CI 1.17 to 1.37, P<0.001), PEEP upon commencement of IMV (HR 0.78 per 5-cmH 2 O increase, 95% CI 0.66-0.93, P=0.005). Time-dependent parameters associated with 28-day IMV mortality were serum creatinine (HR 1.30 per doubling, 95% CI 1.19-1.42, P<0.001), lactate (HR 1.16 per doubling, 95% CI 1.06-1.27 P=0.001), PaCO 2 (HR 1.31 per doubling, 95% CI 1.05-1.64, P=0.015), pH (HR 0.82 per 0.1 increase, 95% CI 0.74-0.91, P<0.001), PaO 2 /FiO 2 (HR 0.56 per doubling, 95% CI 0.50-0.62, P<0.001) and mean arterial pressure (HR 0.92 per 10 mmHg increase, 95% CI 0.88-0.97, P=0.002). Conclusions: : This international study establishes that in mechanically ventilated patients with COVID-19, older age and clinically relevant variables monitored at the bedside are risk factors for 28-day IMV mortality. Further investigation is warranted to validate any causative roles these parameters might play in influencing clinical outcomes.

Long Covid in adults discharged from UK hospitals after Covid-19: A prospective, multicentre cohort study using the ISARIC WHO Clinical Characterisation Protocol. (preprint)

Structured Abstract Objectives: The long-term consequences of severe Covid-19 requiring hospital admission are not well characterised. The objective of this study was to establish the long-term effects of Covid-19 following hospitalisation and the impact these may have on patient reported outcome measures. Design: A multicentre, prospective cohort study with at least 3 months follow-up of participants admitted to hospital between 5th February 2020 and 5th October 2020. Setting: 31 hospitals in the United Kingdom. Participants: 327 hospitalised participants discharged alive from hospital with confirmed/high likelihood SARS-CoV-2 infection. Main outcome measures and comparisons: The primary outcome was self-reported recovery at least ninety days after initial Covid-19 symptom onset. Secondary outcomes included new symptoms, new or increased disability (Washington group short scale), breathlessness (MRC Dyspnoea scale) and quality of life (EQ5D-5L). We compared these outcome measures across age, comorbidity status and in-hospital Covid-19 severity to identify groups at highest risk of developing long-term difficulties. Multilevel logistic and linear regression models were built to adjust for the effects of patient and centre level risk factors on these outcomes. Results: In total 53.7% (443/824) contacted participants responded, yielding 73.8% (327/443) responses with follow-up of 90 days or more from symptom onset. The median time between symptom onset of initial illness and completing the participant questionnaire was 222 days (Interquartile range (IQR) 189 to 269 days). In total, 54.7% (179/327) of participants reported they did not feel fully recovered. Persistent symptoms were reported by 93.3% (305/325) of participants, with fatigue the most common (82.8%, 255/308), followed by breathlessness (53.5%, 175/327). 46.8% (153/327) reported an increase in MRC dyspnoea scale of at least one grade. New or worse disability was reported by 24.2% (79/327) of participants. Overall (EQ5D-5L) summary index was significantly worse at the time of follow-up (median difference 0.1 points on a scale of 0 to 1, IQR: -0.2 to 0.0). Females under the age of 50 years were five times less likely to report feeling recovered (adjusted OR 5.09, 95% CI 1.64 to 15.74), were more likely to have greater disability (adjusted OR 4.22, 95% CI 1.12 to 15.94), twice as likely to report worse fatigue (adjusted OR 2.06, 95% CI 0.81 to 3.31) and seven times more likely to become more breathless (adjusted OR 7.15, 95% CI 2.24 to 22.83) than men of the same age. Conclusions: Survivors of Covid-19 experienced long-term symptoms, new disability, increased breathlessness, and reduced quality of life. These findings were present even in young, previously healthy working age adults, and were most common in younger females. Policymakers should fund further research to identify effective treatments for long-Covid and ensure healthcare, social care and welfare support is available for individuals with long-Covid.

Risk factors for long-term consequences of COVID-19 in hospitalised adults in Moscow using the ISARIC Global follow-up protocol: StopCOVID cohort study (preprint)

BackgroundThe long-term sequalae of COVID-19 remain poorly characterised. In this study, we aimed to assess long-standing symptoms (LS) (symptoms lasting from the time of discharge) in previously hospitalised patients with COVID-19 and assess associated risk factors. MethodsThis is a longitudinal cohort study of adults ([≥]18 years of age) with clinically diagnosed or laboratory-confirmed COVID-19 admitted to Sechenov University Hospital Network in Moscow, Russia. Data were collected from patients discharged between April 8 and July 10, 2020. Participants were interviewed via telephone using Tier 1 ISARIC Long-term Follow-up Study CRF and the WHO CRF for Post COVID conditions. Reported symptoms were further categorised based on the system(s) involved. Additional information on dyspnoea, quality of life and fatigue was collected using validated instruments. Multivariable logistic regressions were performed to investigate risk factors for development of LS categories. FindingsOverall, 2,649 of 4,755 patients discharged from the hospitals were available for the follow-up and included in the study. The median age of the patients was 56 years (IQR, 46-66) and 1,353 (51.1%) were women. The median follow-up time since hospital discharge was 217.5 (200.4-235.5) days. At the time of the follow-up interview 1247 (47.1%) participants reported LS. Fatigue (21.2%, 551/2599), shortness of breath (14.5%, 378/2614) and forgetfulness (9.1%, 237/2597) were the most common LS reported. Chronic fatigue (25%, 658/2593) and respiratory (17.2% 451/2616) were the most common LS categories. with reporting of multi-system involvement (MSI) less common (11.3%; 299). Female sex was associated with LS categories of chronic fatigue with an odds ratio of 1.67 (95% confidence interval 1.39 to 2.02), neurological (2.03, 1.60 to 2.58), mood and behaviour (1.83, 1.41 to 2.40), dermatological (3.26, 2.36 to 4.57), gastrointestinal (2.50, 1.64 to 3.89), sensory (1.73, 2.06 to 2.89) and respiratory (1.31, 1.06 to 1.62). Pre-existing asthma was associated with neurological (1.95, 1.25 to 2.98) and mood and behavioural changes (2.02, 1.24 to 3.18) and chronic pulmonary disease was associated with chronic fatigue (1.68, 1.21 to 2.32). Interpretation6 to 8 months after acute infection episode almost a half of patients experience symptoms lasting since hospital discharge. One in ten individuals experiences MSI. Female sex is the main risk factor for majority of the LS categories. chronic pulmonary disease is associated with a higher risk of chronic fatigue development, and asthma with neurological and mood and behaviour changes. Individuals with LS and MSI should be the main target for future research and intervention strategies. FundingThis study is supported by Russian Fund for Basic Research and UK Embassy in Moscow. The ISARIC work is supported by grants from: the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford [award 200907], Wellcome Trust and Department for International Development [215091/Z/18/Z], and the Bill and Melinda Gates Foundation [OPP1209135], EU Platform for European Preparedness Against (Re-) emerging Epidemics (PREPARE) [FP7 project 602525] This research was funded in part, by the Wellcome Trust. The views expressed are those of the authors and not necessarily those of the DID, NIHR, Wellcome Trust or PHE. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSEvidence suggests that COVID-19 may result in short- and long-term consequences to health. Most studies do not provide definitive answers due to a combination of short follow-up (2-3 months), small sample size, and use of non-standardised tools. There is a need to study the longer-term health consequences of previously hospitalised patients with COVID-19 infection and to identify risk factors for sequalae. Added value of this studyTo our knowledge, this is the largest cohort study (n=2,649) with the longest follow-up since hospital discharge (6-8 months) of previously hospitalised adult patients. We found that 6-8 months after discharge from the hospital, around a half (47.1%) of patients reported at least one long-standing symptom since discharge. Once categories of symptoms were assessed, chronic fatigue and respiratory problems were the most frequent clusters of long-standing symptoms in our patients. Of those patients having long-term symptoms, a smaller proportion (11.3%) had multisystem involvement, with three or more categories of long-standing symptoms present. Although most patients developed symptoms since discharge, a smaller number of individuals experienced symptom beginning symptom appearing weeks or months after the acute phase. Female sex was a predictor for most of the symptom categories at the time of the follow-up interview, with chronic pulmonary disease associated with chronic fatigue-related symptoms, and asthma with a higher risk of neurological symptoms, mood and behaviour problems. Implications of all the available evidenceThe majority of patients experienced long-lasting symptoms 6 to 8 months after hospital discharge and almost half reported at least one long-standing symptom, with chronic fatigue and respiratory problems being the most frequent. A smaller number reported multisystem impacts with three or more long-standing categories present at follow-up. A higher risk was found for women, for chronic pulmonary disease with chronic fatigue, and neurological symptoms and mood and behaviour problems with asthma. Patterns of the symptom development following COVID-19 should be further investigated in future research.

Development and validation of the 4C Deterioration model for adults hospitalised with COVID-19 (preprint)

Prognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions. Among 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables. We used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions. We further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (-0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)). Importantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making. Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.

Robust, reproducible clinical patterns in hospitalised patients with COVID-19 (preprint)

Severe COVID-19 is characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. The clinical associations of different patterns of symptoms can influence diagnostic and therapeutic decision-making: for example, significant differential therapeutic effects in sub-groups of patients with different severities of respiratory failure have already been reported for the only treatment so far shown to reduce mortality in COVID-19, dexamethasone. We obtained structured clinical data on 68914 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 33468 cases according to symptoms reported at recruitment. We validated our findings in a second group of 35446 cases recruited to ISARIC-4C, and in separate cohort of community cases. A core symptom set of fever, cough, and dyspnoea co-occurred with additional symptoms in three patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were common, and a subgroup of patients reported few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom clusters were highly consistent in replication analysis using a further 35446 individuals subsequently recruited to ISARIC-4C. Similar patterns were externally verified in 4445 patients from a study of self-reported symptoms of mild disease. The large scale of ISARIC-4C study enabled robust, granular discovery and replication of patient clusters. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four patterns are usefully distinct from the core symptom groups: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms. These observations deepen our understanding of COVID-19 and will influence clinical diagnosis, risk prediction, and future mechanistic and clinical studies.

Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score (preprint)

ObjectivesTo develop and validate a pragmatic risk score to predict mortality for patients admitted to hospital with covid-19. DesignProspective observational cohort study: ISARIC WHO CCP-UK study (ISARIC Coronavirus Clinical Characterisation Consortium [4C]). Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited between 21 May and 29 June 2020. Setting260 hospitals across England, Scotland, and Wales. ParticipantsAdult patients ([≥]18 years) admitted to hospital with covid-19 admitted at least four weeks before final data extraction. Main outcome measuresIn-hospital mortality. ResultsThere were 34 692 patients included in the derivation dataset (mortality rate 31.7%) and 22 454 in the validation dataset (mortality 31.5%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea, and C-reactive protein (score range 0-21 points). The 4C risk stratification score demonstrated high discrimination for mortality (derivation cohort: AUROC 0.79; 95% CI 0.78 - 0.79; validation cohort 0.78, 0.77-0.79) with excellent calibration (slope = 1.0). Patients with a score [≥]15 (n = 2310, 17.4%) had a 67% mortality (i.e., positive predictive value 67%) compared with 1.0% mortality for those with a score [≤]3 (n = 918, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (AUROC range 0.60-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). ConclusionsWe have developed and validated an easy-to-use risk stratification score based on commonly available parameters at hospital presentation. This outperformed existing scores, demonstrated utility to directly inform clinical decision making, and can be used to stratify inpatients with covid-19 into different management groups. The 4C Mortality Score may help clinicians identify patients with covid-19 at high risk of dying during current and subsequent waves of the pandemic. Study registrationISRCTN66726260

ISARIC COVID-19 Clinical Data Report: 8 June 2020 (preprint)

ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global uptake of this resource has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report is a part of a series and includes the results of data analysis on 8 June 2020. We thank all of the data contributors for their ongoing support. As of 8JUN20, data have been entered for 67,130 patients from 488 sites across 37 countries. For this report, we show data for 42,656 patients with confirmed disease who were enrolled >14 days prior. This update includes about 2,400 new cases from France, and we thank these collaborators for this significant addition to the dataset. Some highlights from this report The median time from onset of symptoms to hospital admission is 5 days, but a proportion of patients take longer to get to the hospital (average 14.6 days, standard deviation 8.1). COVID-19 patients tend to require prolonged hospitalisation; of the 88% with a known outcome, the median length of admission to death or discharge is 8 days and the mean 11.5. 17% of patients were admitted to ICU/HDU, about 40% of these on the very day of hospital admission. Antibiotics were given to 83% of patients, antivirals to 9%, steroids to 15%, which becomes 93%, 50% and 27%, respectively for those admitted to ICU/HDU. Attention has been called on overuse of antibiotics and need to adhere to antibiotic stewardship principles. 67% of patients received some degree of oxygen supplementation: of these 23.4% received NIV and 15% IMV. This relatively high proportion of oxygen use will have implications for oxygen surge planning in healthcare facilities. Some centres may need to plan to boost capacity to deliver oxygen therapy if this is not readily available. WHO provides operational advice on surge strategy here https://apps.who.int/iris/bitstream/handle/10665/331746/WHO-2019-nCoV-Oxygen_sources-2020.1-eng.pdf

Clinical characteristics of children and young people hospitalised with covid-19 in the United Kingdom: prospective multicentre observational cohort study (preprint)

ObjectiveTo characterise the clinical features of children and young people admitted to hospital with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the UK, and explore factors associated with admission to critical care, mortality, and development of multisystem inflammatory syndrome in children and adolescents temporarily related to covid-19 (MIS-C). DesignProspective observational cohort study with rapid data gathering and near real time analysis. Setting260 acute care hospitals in England, Wales, and Scotland between 17th January and 5th June 2020, with a minimal follow-up time of two weeks (to 19th June 2020). Participants451 children and young people aged less than 19 years admitted to 116 hospitals and enrolled into the International Severe Acute Respiratory and emergency Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK study with laboratory-confirmed SARS-CoV-2. Main Outcome MeasuresAdmission to critical care (high dependency or intensive care), in-hospital mortality, or meeting the WHO preliminary case definition for MIS-C. ResultsMedian age was 3.9 years [interquartile range (IQR) 0.3-12.9 years], 36% (162/451) were under 12 months old, and 57% (256/450) were male. 56% (224/401) were White, 12% (49/401) South Asian and 10% (40/401) Black. 43% (195/451) had at least one recorded comorbidity. A muco-enteric cluster of symptoms was identified, closely mirroring the WHO MIS-C criteria. 17% of children (72/431) were admitted to critical care. On multivariable analysis this was associated with age under one month odds ratio 5.05 (95% confidence interval 1.69 to 15.72, p=0.004), age 10 to 14 years OR 3.11 (1.21 to 8.55, p=0.022) and Black ethnicity OR 3.02 (1.30 to 6.84, p=0.008). Three young people died (0.7 %, 3/451) aged 16 to 19 years, all of whom had profound comorbidity. Twelve percent of children (36/303) met the WHO MIS-C criteria, with the first patient developing symptoms in mid-March. Those meeting MIS-C criteria were older, (median age 10.8 years ([IQR 8.4-14.1] vs 2.0 [0.2-12.6]), p<0.001) and more likely to be of non-White ethnicity (70% (23/33) vs 43% (101/237), p=0.005). Children with MIS-C were four times more likely to be admitted to critical care (61% (22/36) vs 15% (40/267, p<0.001). In addition to the WHO criteria, children with MIS-C were more likely to present with headache (45% (13/29) vs 11% (19/171), p<0.001), myalgia (39% (11/28) vs 7% (12/170), p<0.001), sore throat (37% (10/27) vs (13% (24/183, p = 0.004) and fatigue (57% (17/30) vs 31% (60/192), p =0.012) than children who did not and to have a platelet count of less than 150 x109/L (30% (10/33) vs 10% (24/232), p=0.004). ConclusionsOur data confirms less severe covid-19 in children and young people than in adults and we provide additional evidence for refining the MIS-C case definition. The identification of a muco-enteric symptom cluster also raises the suggestion that MIS-C is the severe end of a spectrum of disease. Study registrationISRCTN66726260

Ethnicity and Outcomes from COVID-19: The ISARIC CCP-UK Prospective Observational Cohort Study of Hospitalised Patients (preprint)

Background: Reports of ethnic inequalities in COVID-19 outcomes are conflicting and the reasons for any differences in outcomes are unclear. We investigated ethnic inequalities in critical care admission patterns, the need for invasive mechanical ventilation (IMV), and in-hospital mortality, among hospitalised patients with COVID-19. Methods: We undertook a prospective cohort study in which dedicated research staff recruited hospitalised patients with suspected/confirmed COVID-19 from 260 hospitals across England, Scotland and Wales, collecting data directly and from records between 6th February and 8th May 2020 with follow-up until 22nd May 2020. Analysis used hierarchical regression models accounting for confounding, competing risks, and clustering of patients in hospitals. Potential mediators for death were explored with a three-way decomposition mediation analysis. Findings: Of 34,986 patients enrolled, 30,693 (88%) had ethnicity recorded: South Asian (1,388, 5%), East Asian (266, 1%), Black (1,094, 4%), Other Ethnic Minority (2,398, 8%) (collectively Ethnic Minorities), and White groups (25,547, 83%). Ethnic Minorities were younger and more likely to have diabetes (type 1/type 2) but had fewer other comorbidities such as chronic heart disease or dementia than the White group. No difference was seen between ethnic groups in the time from symptom onset to hospital admission, nor in illness severity at admission. Critical care admission was more common in South Asian (odds ratio 1.28, 95% confidence interval 1.09 to 1.52), Black (1.36, 1.14 to 1.62), and Other Ethnic Minority (1.29, 1.13 to 1.47) groups compared to the White group, after adjusting for age, sex and location. This was broadly unchanged after adjustment for deprivation and comorbidities. Patterns were similar for IMV. Higher adjusted mortality was seen in the South Asian (hazard ratio 1.19, 1.05 to 1.36), but not East Asian (1.00, 0.74 to 1.35), Black (1.05, 0.91 to 1.26) or Other Ethnic Minority (0.99, 0.89 to 1.10) groups, compared to the White group. 18% (95% CI, 9% to 56%) of the excess mortality in South Asians was mediated by pre-existing diabetes. Interpretation: Ethnic Minorities in hospital with COVID-19 were more likely to be admitted to critical care and receive IMV than Whites, despite similar disease severity on admission, similar duration of symptoms, and being younger with fewer comorbidities. South Asians are at greater risk of dying, due at least in part to a higher prevalence of pre-existing diabetes. Trial Registration: The study was registered at https://www.isrctn.com/ISRCTN66726260. Funding Statement: This work is supported by grants from: the National Institute for Health Research [award CO-CIN-01], the Medical Research Council [grant MC_PC_19059] and by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford [NIHR award 200907], Wellcome Trust and Department for International Development [215091/Z/18/Z], and the Bill and Melinda Gates Foundation [OPP1209135], and Liverpool Experimental Cancer Medicine Centre for providing infrastructure support for this research (Grant Reference: C18616/A25153). JSN-V-T is seconded to the Department of Health and Social Care, England (DHSC).Declaration of Interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: AB Docherty reports grants from Department of Health and Social Care, during the conduct of the study; grants from Wellcome Trust, outside the submitted work; CA Green reports grants from DHSC National Institute of Health Research UK, during the conduct of the study; PW Horby reports grants from Wellcome Trust / Department for International Development / Bill and Melinda Gates Foundation, grants from NIHR , during the conduct of the study; JS Nguyen-Van-Tam reports grants from Department of Health and Social Care, England, during the conduct of the study; and is seconded to the Department of Health and Social Care, England (DHSC); PJM Openshaw reports personal fees from consultancies and from European Respiratory Society; grants from MRC, MRC Global Challenge Research Fund, EU, NIHR Biomedical Research Centre, MRC/GSK, Wellcome Trust, NIHR (HPRU in Respiratory Infection), and NIHR Senior Investigator outside the submitted work. His role as President of the British Society for Immunology was unpaid but travel and accommodation at some meetings was provided by the Society. JK Baillie reports grants from Medical Research Council UK; MG Semple reports grants from DHSC National Institute of Health Research UK, grants from Medical Research Council UK, grants from Health Protection Research Unit in Emerging & Zoonotic Infections, University of Liverpool, during the conduct of the study; other from Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work. EM Harrison, H Ardwick, J Dunning, R Pius, L Norman, KA Holden, JM Read, G Carson, L Merson, J Lee, D Plotkin, L Sigfrid, S Halpin, C Jackson, and C Gamble, all declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.Ethics Approval Statement: Ethical approval was given by the South Central – Oxford C Research Ethics Committee in England (Ref: 13/SC/0149), and by the Scotland A Research Ethics Committee (Ref: 20/SS/0028).

Features of 16,749 hospitalised UK patients with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol (preprint)

Objective: To characterize the clinical features of patients with severe COVID-19 in the UK. Design: Prospective observational cohort study with rapid data gathering and near real-time analysis, using a pre-approved questionnaire adopted by the WHO. Setting: 166 UK hospitals between 6th February and 18th April 2020. Participants: 16,749 people with COVID-19. Interventions: No interventions were performed, but with consent samples were taken for research purposes. Many participants were co-enrolled in other interventional studies and clinical trials. Results: The median age was 72 years [IQR 57, 82; range 0, 104], the median duration of symptoms before admission was 4 days [IQR 1,8] and the median duration of hospital stay was 7 days [IQR 4,12]. The commonest comorbidities were chronic cardiac disease (29%), uncomplicated diabetes (19%), non-asthmatic chronic pulmonary disease (19%) and asthma (14%); 47% had no documented reported comorbidity. Increased age and comorbidities including obesity were associated with a higher probability of mortality. Distinct clusters of symptoms were found: 1. respiratory (cough, sputum, sore throat, runny nose, ear pain, wheeze, and chest pain); 2. systemic (myalgia, joint pain and fatigue); 3. enteric (abdominal pain, vomiting and diarrhoea). Overall, 49% of patients were discharged alive, 33% have died and 17% continued to receive care at date of reporting. 17% required admission to High Dependency or Intensive Care Units; of these, 31% were discharged alive, 45% died and 24% continued to receive care at the reporting date. Of those receiving mechanical ventilation, 20% were discharged alive, 53% died and 27% remained in hospital. Conclusions: We present the largest detailed description of COVID-19 in Europe, demonstrating the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks. Trial documentation: Available at https://isaric4c.net/protocols . Ethical approval in England and Wales (13/SC/0149), and Scotland (20/SS/0028). ISRCTN (pending).