Antidepressant Advisor (ADeSS): a decision support system for antidepressant treatment for depression in UK primary care - a feasibility study.

OBJECTIVES: To develop and probe the first computerised decision-support tool to provide antidepressant treatment guidance to general practitioners (GPs) in UK primary care. DESIGN: A parallel group, cluster-randomised controlled feasibility trial, where individual participants were blind to treatment allocation. SETTING: South London NHS GP practices. PARTICIPANTS: Ten practices and eighteen patients with treatment-resistant current major depressive disorder. INTERVENTIONS: Practices were randomised to two treatment arms: (a) treatment-as-usual, (b) computerised decision support tool. RESULTS: Ten GP practices participated in the trial, which was within our target range (8-20). However, practice and patient recruitment were slower than anticipated and only 18 of 86 intended patients were recruited. This was due to fewer than expected patients being eligible for the study, as well as disruption resulting from the COVID-19 pandemic. Only one patient was lost to follow-up. There were no serious or medically important adverse events during the trial. GPs in the decision tool arm indicated moderate support for the tool. A minority of patients fully engaged with the mobile app-based tracking of symptoms, medication adherence and side effects. CONCLUSIONS: Overall, feasibility was not shown in the current study and the following modifications would be needed to attempt to overcome the limitations found: (a) inclusion of patients who have only tried one Selective Serotonin Reuptake Inhibitor, rather than two, to improve recruitment and pragmatic relevance of the study; (b) approaching community pharmacists to implement tool recommendations rather than GPs; (c) further funding to directly interface between the decision support tool and self-reported symptom app; (d) increasing the geographic reach by not requiring detailed diagnostic assessments and replacing this with supported remote self-report. TRIAL REGISTRATION NUMBER: NCT03628027.

Prevalence of post-traumatic stress disorder and common mental disorders in health-care workers in England during the COVID-19 pandemic: a two-phase cross-sectional study.

BACKGROUND: Previous studies on the impact of the COVID-19 pandemic on the mental health of health-care workers have relied on self-reported screening measures to estimate the point prevalence of common mental disorders. Screening measures, which are designed to be sensitive, have low positive predictive value and often overestimate prevalence. We aimed to estimate prevalence of common mental disorders and post-traumatic stress disorder (PTSD) among health-care workers in England using diagnostic interviews. METHODS: We did a two-phase, cross-sectional study comprising diagnostic interviews within a larger multisite longitudinal cohort of health-care workers (National Health Service [NHS] CHECK; n=23 462) during the COVID-19 pandemic. In the first phase, health-care workers across 18 NHS England Trusts were recruited. Baseline assessments were done using online surveys between April 24, 2020, and Jan 15, 2021. In the second phase, we selected a proportion of participants who had responded to the surveys and conducted diagnostic interviews to establish the prevalence of mental disorders. The recruitment period for the diagnostic interviews was between March 1, 2021 and Aug 27, 2021. Participants were screened with the 12-item General Health Questionnaire (GHQ-12) and assessed with the Clinical Interview Schedule-Revised (CIS-R) for common mental disorders or were screened with the 6-item Post-Traumatic Stress Disorder Checklist (PCL-6) and assessed with the Clinician Administered PTSD Scale for DSM-5 (CAPS-5) for PTSD. FINDINGS: The screening sample contained 23 462 participants: 2079 participants were excluded due to missing values on the GHQ-12 and 11 147 participants due to missing values on the PCL-6. 243 individuals participated in diagnostic interviews for common mental disorders (CIS-R; mean age 42 years [range 21-70]; 185 [76%] women and 58 [24%] men) and 94 individuals participated in diagnostic interviews for PTSD (CAPS-5; mean age 44 years [23-62]; 79 [84%] women and 15 [16%] men). 202 (83%) of 243 individuals in the common mental disorders sample and 83 (88%) of 94 individuals in the PTSD sample were White. GHQ-12 screening caseness for common mental disorders was 52·8% (95% CI 51·7-53·8). Using CIS-R diagnostic interviews, the estimated population prevalence of generalised anxiety disorder was 14·3% (10·4-19·2), population prevalence of depression was 13·7% (10·1-18·3), and combined population prevalence of generalised anxiety disorder and depression was 21·5% (16·9-26·8). PCL-6 screening caseness for PTSD was 25·4% (24·3-26·5). Using CAPS-5 diagnostic interviews, the estimated population prevalence of PTSD was 7·9% (4·0-15·1). INTERPRETATION: The prevalence estimates of common mental disorders and PTSD in health-care workers were considerably lower when assessed using diagnostic interviews compared with screening tools. 21·5% of health-care workers met the threshold for diagnosable mental disorders, and thus might benefit from clinical intervention. FUNDING: UK Medical Research Council; UCL/Wellcome; Rosetrees Trust; NHS England and Improvement; Economic and Social Research Council; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at the Maudsley and King's College London (KCL); NIHR Protection Research Unit in Emergency Preparedness and Response at KCL.

Indicators of recent COVID-19 infection status: findings from a large occupational cohort of staff and postgraduate research students from a UK university.

BACKGROUND: Researchers conducting cohort studies may wish to investigate the effect of episodes of COVID-19 illness on participants. A definitive diagnosis of COVID-19 is not always available, so studies have to rely on proxy indicators. This paper seeks to contribute evidence that may assist the use and interpretation of these COVID-indicators. METHODS: We described five potential COVID-indicators: self-reported core symptoms, a symptom algorithm; self-reported suspicion of COVID-19; self-reported external results; and home antibody testing based on a 'lateral flow' antibody (IgG/IgM) test cassette. Included were staff and postgraduate research students at a large London university who volunteered for the study and were living in the UK in June 2020. Excluded were those who did not return a valid antibody test result. We provide descriptive statistics of prevalence and overlap of the five indicators. RESULTS: Core symptoms were the most common COVID-indicator (770/1882 participants positive, 41%), followed by suspicion of COVID-19 (n = 509/1882, 27%), a positive symptom algorithm (n = 298/1882, 16%), study antibody lateral flow positive (n = 124/1882, 7%) and a positive external test result (n = 39/1882, 2%), thus a 20-fold difference between least and most common. Meeting any one indicator increased the likelihood of all others, with concordance between 65 and 94%. Report of a low suspicion of having had COVID-19 predicted a negative antibody test in 98%, but positive suspicion predicted a positive antibody test in only 20%. Those who reported previous external antibody tests were more likely to have received a positive result from the external test (24%) than the study test (15%). CONCLUSIONS: Our results support the use of proxy indicators of past COVID-19, with the caveat that none is perfect. Differences from previous antibody studies, most significantly in lower proportions of participants positive for antibodies, may be partly due to a decline in antibody detection over time. Subsequent to our study, vaccination may have further complicated the interpretation of COVID-indicators, only strengthening the need to critically evaluate what criteria should be used to define COVID-19 cases when designing studies and interpreting study results.

Evaluating the Efficacy of the Drinks:Ration Mobile App to Reduce Alcohol Consumption in a Help-Seeking Military Veteran Population: Randomized Controlled Trial.

BACKGROUND: Alcohol misuse is higher in the UK armed forces (AF) than in the general population. Research demonstrates that alcohol misuse persists after an individual leaves service, and this is notably the case for those who are seeking help for a mental health difficulty. Despite this, there is no work on testing a mobile alcohol reduction intervention that is personalized to support the UK AF. OBJECTIVE: To address this gap, we investigated the efficacy of a 28-day brief alcohol intervention delivered via a mobile app in reducing weekly self-reported alcohol consumption among UK veterans seeking help for mental health difficulties. METHODS: We performed a 2-arm participant-blinded randomized controlled trial (RCT). We compared a mobile app that included interactive features designed to enhance participants' motivation and personalized messaging (intervention arm) with a version that provided government guidance on alcohol consumption only (control arm). Adults were eligible if they had served in the UK AF, were currently receiving or had received clinical support for mental health symptoms, and consumed 14 units (approximately 112 g of ethanol) or more of alcohol per week. Participants received the intervention or the control mobile app (1:1 ratio). The primary outcome was a change in self-reported weekly alcohol consumption between baseline and day 84 assessed using the validated Timeline Follow Back for Alcohol Consumption (TLFB) (prior 7 days), with a secondary outcome exploring self-reported change in the Alcohol Use Disorder Identification Test (AUDIT) score. RESULTS: Between October 2020 and April 2021, 2708 individuals were invited to take part, of which 2531 (93.5%) did not respond, 54 (2%) were ineligible, and 123 (4.5%) responded and were randomly allocated (62, 50.4%, intervention; 61, 49.6%, control). At day 84, 41 (66.1%) participants in the intervention arm and 37 (60.7%) in the control arm completed the primary outcome assessment. Between baseline and day 84, weekly alcohol consumption reduced by -10.5 (95% CI -19.5 to -1.5) units in the control arm and -28.2 (95% CI -36.9 to -19.5) units in the intervention arm (P=.003, Cohen d=0.35). We also found a significant reduction in the AUDIT score of -3.9 (95% CI -6.2 to -1.6) in the intervention arm (Cohen d=0.48). Our primary and secondary effects did not persist over the longer term (day 168). Two adverse events were detected during the trial. CONCLUSIONS: This study examined the efficacy of a fully automated 28-day brief alcohol intervention delivered via a mobile app in a help-seeking sample of UK veterans with hazardous alcohol consumption. We found that participants receiving Drinks:Ration reduced their alcohol consumption more than participants receiving guidance only (at day 84). In the short term, we found Drinks:Ration is efficacious in reducing alcohol consumption in help-seeking veterans. TRIAL REGISTRATION: ClinicalTrials.gov NCT04494594; https://tinyurl.com/34em6n9f. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19720.

Trajectories of mental health among UK university staff and postgraduate students during the pandemic.

OBJECTIVES: The COVID-19 pandemic has disrupted the social and working lives of many. Past studies have highlighted worsening mental health during the pandemic, but often rely on small samples or infrequent follow-up. This study draws on fortnightly assessments from a large occupational cohort to describe differing trajectories of mental health between April 2020 and April 2021 and individual characteristics associated with these trajectory types. METHODS: King's College London Coronavirus Health and Experiences of Colleagues at King's is an occupational cohort study at a large university in London, UK. Participants (n=2241) completed online questionnaires fortnightly between April 2020 and April 2021. Symptoms of anxiety and depression were assessed using Generalised Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9). RESULTS: On average, participants reported low levels of anxiety and depression (GAD-7 and PHQ-9 scores of 0-9, consistent with 'none', 'minimal' or 'mild' symptoms) throughout the year, with symptoms highest in April 2020 and decreasing over the summer months when no lockdown measures were in place. However, we observed more severe and variable symptoms among subgroups of participants. Four trajectory types for anxiety and depression were identified: 'persistent high severity' (6%-7% of participants), 'varying symptoms, opposing national cases' (4%-8%), 'varying symptoms, consistent with national cases' (6%-11%) and 'persistent low severity' (74%-84%). Younger age, female gender, caring responsibilities and shielding were associated with higher severity trajectory types. CONCLUSIONS: These data highlight differing individual responses to the pandemic and underscore the need to consider individual circumstances when assessing and treating mental health. Aggregate trends in anxiety and depression may hide greater variation and symptom severity among subgroups.

Exploring the Effects of In-App Components on Engagement With a Symptom-Tracking Platform Among Participants With Major Depressive Disorder (RADAR-Engage): Protocol for a 2-Armed Randomized Controlled Trial.

BACKGROUND: Multi-parametric remote measurement technologies (RMTs) comprise smartphone apps and wearable devices for both active and passive symptom tracking. They hold potential for understanding current depression status and predicting future depression status. However, the promise of using RMTs for relapse prediction is heavily dependent on user engagement, which is defined as both a behavioral and experiential construct. A better understanding of how to promote engagement in RMT research through various in-app components will aid in providing scalable solutions for future remote research, higher quality results, and applications for implementation in clinical practice. OBJECTIVE: The aim of this study is to provide the rationale and protocol for a 2-armed randomized controlled trial to investigate the effect of insightful notifications, progress visualization, and researcher contact details on behavioral and experiential engagement with a multi-parametric mobile health data collection platform, Remote Assessment of Disease and Relapse (RADAR)-base. METHODS: We aim to recruit 140 participants upon completion of their participation in the RADAR Major Depressive Disorder study in the London site. Data will be collected using 3 weekly tasks through an active smartphone app, a passive (background) data collection app, and a Fitbit device. Participants will be randomly allocated at a 1:1 ratio to receive either an adapted version of the active app that incorporates insightful notifications, progress visualization, and access to researcher contact details or the active app as usual. Statistical tests will be used to assess the hypotheses that participants using the adapted app will complete a higher percentage of weekly tasks (behavioral engagement: primary outcome) and score higher on self-awareness measures (experiential engagement). RESULTS: Recruitment commenced in April 2021. Data collection was completed in September 2021. The results of this study will be communicated via publication in 2022. CONCLUSIONS: This study aims to understand how best to promote engagement with RMTs in depression research. The findings will help determine the most effective techniques for implementation in both future rounds of the RADAR Major Depressive Disorder study and, in the long term, clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT04972474; http://clinicaltrials.gov/ct2/show/NCT04972474. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32653.

Mental health among UK university staff and postgraduate students in the early stages of the COVID-19 pandemic.

OBJECTIVES: To characterise the baseline King's College London Coronavirus Health and Experiences of Colleagues at King's cohort and describe patterns of probable depression and anxiety among staff and postgraduate research students at a large UK university in April/May 2020. METHODS: An online survey was sent to current staff and postgraduate research students via email in April 2020 (n=2590). Primary outcomes were probable depression and anxiety, measured with the Patient Health Questionnaire-9 and Generalised Anxiety Disorder-7, respectively. Secondary outcomes were alcohol use and perceived change in mental health. Outcomes were described using summary statistics and multivariable Poisson regression was used to explore associations with six groups of predictors: demographics and prior mental health, living arrangements, caring roles, healthcare, occupational factors and COVID-19 infection. All analyses were weighted to account for differences between the sample and target population in terms of age, gender, and ethnicity. RESULTS: Around 20% of staff members and 30% of postgraduate research students met thresholds for probable depression or anxiety on the questionnaires. This doubled to around 40% among younger respondents aged <25. Other factors associated with probable depression and anxiety included female gender, belonging to an ethnic minority group, caregiving responsibilities and shielding or isolating. Around 20% of participants were found to reach cut-off for hazardous drinking on Alcohol Use Disorders Identification Test, while 30% were drinking more than before the pandemic. CONCLUSIONS: Our study shows worrying levels of symptoms of depression, anxiety and alcohol use disorder in an occupational sample from a large UK university in the months following the outbreak of the COVID-19 pandemic.

Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study.

BACKGROUND: Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors. METHODS: Prediction models fitting relevant inclusion criteria were chosen for validation. The outcome was either mortality or a composite outcome of mortality and ICU admission (severe disease). 1295 patients admitted with symptoms of COVID-19 at Kings Cross Hospital (KCH) in London, United Kingdom, and 307 patients at Oslo University Hospital (OUH) in Oslo, Norway were included. The performance of the models was assessed in terms of discrimination and calibration. RESULTS: We identified two models for prediction of mortality (referred to as Xie and Zhang1) and two models for prediction of severe disease (Allenbach and Zhang2). The performance of the models was variable. For prediction of mortality Xie had good discrimination at OUH with an area under the receiver-operating characteristic (AUROC) 0.87 [95% confidence interval (CI) 0.79-0.95] and acceptable discrimination at KCH, AUROC 0.79 [0.76-0.82]. In prediction of severe disease, Allenbach had acceptable discrimination (OUH AUROC 0.81 [0.74-0.88] and KCH AUROC 0.72 [0.68-0.75]). The Zhang models had moderate to poor discrimination. Initial calibration was poor for all models but improved with recalibration. CONCLUSIONS: The performance of the four prediction models was variable. The Xie model had the best discrimination for mortality, while the Allenbach model had acceptable results for prediction of severe disease.

NHS CHECK: protocol for a cohort study investigating the psychosocial impact of the COVID-19 pandemic on healthcare workers.

INTRODUCTION: The COVID-19 pandemic has had profound effects on the working lives of healthcare workers (HCWs), but the extent to which their well-being and mental health have been affected remains unclear. This longitudinal cohort study aims to recruit a cohort of National Health Service (NHS) HCWs, conducting surveys at regular intervals to provide evidence about the prevalence of symptoms of mental disorders, and investigate associated factors such as occupational contexts and support interventions available. METHODS AND ANALYSIS: All staff, students and volunteers working in the 18 participating NHS Trusts in England will be sent emails inviting them to complete a survey at baseline, with email invitations for the follow-up surveys sent 6 months and 12 months later. Opening in late April 2020, the baseline survey collects data on demographics, occupational/organisational factors, experiences of COVID-19, validated measures of symptoms of poor mental health (eg, depression, anxiety, post-traumatic stress disorder), and constructs such as resilience and moral injury. These surveys will be complemented by in-depth psychiatric interviews with a sample of HCWs. Qualitative interviews will also be conducted, to gain deeper understanding of the support programmes used or desired by staff, and facilitators and barriers to accessing such programmes. ETHICS AND DISSEMINATION: Ethical approval for the study was granted by the Health Research Authority (reference: 20/HRA/210, IRAS: 282686) and local Trust Research and Development approval. Cohort data are collected via Qualtrics online survey software, pseudonymised and held on secure university servers. Participants are aware that they can withdraw from the study at any time, and there is signposting to support services if participants feel they need it. Only those consenting to be contacted about further research will be invited to participate in further components. Findings will be rapidly shared with NHS Trusts, and via academic publications in due course.

Psychosocial impact of the COVID-19 pandemic on 4378 UK healthcare workers and ancillary staff: initial baseline data from a cohort study collected during the first wave of the pandemic.

OBJECTIVES: This study reports preliminary findings on the prevalence of, and factors associated with, mental health and well-being outcomes of healthcare workers during the early months (April-June) of the COVID-19 pandemic in the UK. METHODS: Preliminary cross-sectional data were analysed from a cohort study (n=4378). Clinical and non-clinical staff of three London-based NHS Trusts, including acute and mental health Trusts, took part in an online baseline survey. The primary outcome measure used is the presence of probable common mental disorders (CMDs), measured by the General Health Questionnaire. Secondary outcomes are probable anxiety (seven-item Generalised Anxiety Disorder), depression (nine-item Patient Health Questionnaire), post-traumatic stress disorder (PTSD) (six-item Post-Traumatic Stress Disorder checklist), suicidal ideation (Clinical Interview Schedule) and alcohol use (Alcohol Use Disorder Identification Test). Moral injury is measured using the Moray Injury Event Scale. RESULTS: Analyses showed substantial levels of probable CMDs (58.9%, 95% CI 58.1 to 60.8) and of PTSD (30.2%, 95% CI 28.1 to 32.5) with lower levels of depression (27.3%, 95% CI 25.3 to 29.4), anxiety (23.2%, 95% CI 21.3 to 25.3) and alcohol misuse (10.5%, 95% CI 9.2 to 11.9). Women, younger staff and nurses tended to have poorer outcomes than other staff, except for alcohol misuse. Higher reported exposure to moral injury (distress resulting from violation of one's moral code) was strongly associated with increased levels of probable CMDs, anxiety, depression, PTSD symptoms and alcohol misuse. CONCLUSIONS: Our findings suggest that mental health support for healthcare workers should consider those demographics and occupations at highest risk. Rigorous longitudinal data are needed in order to respond to the potential long-term mental health impacts of the pandemic.

Changes in Physical Activity among United Kingdom University Students Following the Implementation of Coronavirus Lockdown Measures.

Coronavirus disease (COVID-19) and resulting restrictions have significantly impacted physical activity levels. However, objectively measured changes in physical activity levels among UK university students during lockdown are understudied. Using data collected via remote measurement technology from a mobile physical activity tracker, this study aimed to describe the longitudinal trajectories of physical activity following the start of lockdown among students at a large UK university, and to investigate whether these trajectories varied according to age, gender, and ethnicity. Continuous physical activity data for steps walked per week (n = 730) and miles run per week (n = 264) were analysed over the first period of lockdown and subsequent restriction easing using negative binomial mixed models for repeated measures. Throughout the observation period, more steps were walked by males compared to females, and by White groups compared to all other ethnic groups combined. However, there was a gradual increase in the number of steps walked per week following the commencement of lockdown, irrespective of sociodemographic characteristics. For females only, there was a decrease in the number of miles run per week following lockdown. The long-term impact of the pandemic on physical health is unknown, but our results highlight changes in physical activity which could have implications for physical health.

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

BACKGROUND: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. METHODS: Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy's and St Thomas' Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. RESULTS: A baseline model of 'NEWS2 + age' had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. CONCLUSIONS: NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Biological responses to COVID-19: Insights from physiological and blood biomarker profiles.

BACKGROUND: Understanding the spectrum and course of biological responses to coronavirus disease 2019 (COVID-19) may have important therapeutic implications. We sought to characterise biological responses among patients hospitalised with severe COVID-19 based on serial, routinely collected, physiological and blood biomarker values. METHODS AND FINDINGS: We performed a retrospective cohort study of 1335 patients hospitalised with laboratory-confirmed COVID-19 (median age 70 years, 56 % male), between 1st March and 30th April 2020. Latent profile analysis was performed on serial physiological and blood biomarkers. Patient characteristics, comorbidities and rates of death and admission to intensive care, were compared between the latent classes. A five class solution provided the best fit. Class 1 "Typical response" exhibited a moderately elevated and rising C-reactive protein (CRP), stable lymphopaenia, and the lowest rates of 14-day adverse outcomes. Class 2 "Rapid hyperinflammatory response" comprised older patients, with higher admission white cell and neutrophil counts, which declined over time, accompanied by a very high and rising CRP and platelet count, and exibited the highest mortality risk. Class 3 "Progressive inflammatory response" was similar to the typical response except for a higher and rising CRP, though similar mortality rate. Class 4 "Inflammatory response with kidney injury" had prominent lymphopaenia, moderately elevated (and rising) CRP, and severe renal failure. Class 5 "Hyperinflammatory response with kidney injury" comprised older patients, with a very high and rising CRP, and severe renal failure that attenuated over time. Physiological measures did not substantially vary between classes at baseline or early admission. CONCLUSIONS AND RELEVANCE: Our identification of five distinct classes of biomarker profiles provides empirical evidence for heterogeneous biological responses to COVID-19. Early hyperinflammatory responses and kidney injury may signify unique pathophysiology that requires targeted therapy.