Prospective validation of the 4C prognostic models for adults hospitalised with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol

Purpose To prospectively validate two risk scores to predict mortality (4C Mortality) and in-hospital deterioration (4C Deterioration) among adults hospitalised with COVID-19. Methods Prospective observational cohort study of adults (age ≥18 years) with confirmed or highly suspected COVID-19 recruited into the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK) study in 306 hospitals across England, Scotland and Wales. Patients were recruited between 27 August 2020 and 17 February 2021, with at least 4 weeks follow-up before final data extraction. The main outcome measures were discrimination and calibration of models for in-hospital deterioration (defined as any requirement of ventilatory support or critical care, or death) and mortality, incorporating predefined subgroups. Results 76 588 participants were included, of whom 27 352 (37.4%) deteriorated and 12 581 (17.4%) died. Both the 4C Mortality (0.78 (0.77 to 0.78)) and 4C Deterioration scores (pooled C-statistic 0.76 (95% CI 0.75 to 0.77)) demonstrated consistent discrimination across all nine National Health Service regions, with similar performance metrics to the original validation cohorts. Calibration remained stable (4C Mortality: pooled slope 1.09, pooled calibration-in-the-large 0.12; 4C Deterioration: 1.00, –0.04), with no need for temporal recalibration during the second UK pandemic wave of hospital admissions. Conclusion Both 4C risk stratification models demonstrate consistent performance to predict clinical deterioration and mortality in a large prospective second wave validation cohort of UK patients. Despite recent advances in the treatment and management of adults hospitalised with COVID-19, both scores can continue to inform clinical decision making. Trial registration number ISRCTN66726260.

[1]  A. Torres,et al.  Community-acquired pneumonia severity assessment tools in patients hospitalized with COVID-19: a validation and clinical applicability study , 2021, Clinical Microbiology and Infection.

[2]  M. Duong,et al.  External Validation of the 4C Mortality Score among COVID-19 Patients Visiting the Emergency Department or admitted to Hospital in Ontario, Canada , 2021 .

[3]  M. Biehl,et al.  Severe covid-19 pneumonia: pathogenesis and clinical management , 2021, BMJ.

[4]  L. Danon,et al.  Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: matched cohort study , 2021, BMJ.

[5]  G. Wainrib,et al.  Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients , 2021, Nature Communications.

[6]  Jordan J. Clark,et al.  Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study , 2021 .

[7]  L. Wynants,et al.  Improving clinical management of COVID-19: the role of prediction models , 2021, The Lancet Respiratory Medicine.

[8]  SARS-CoV-2 - increased circulation of variants of concern and vaccine rollout in the EU/EEA, 14th update , 2021 .

[9]  W. Lim,et al.  Dexamethasone in Hospitalized Patients with Covid-19 , 2021 .

[10]  Ayesha Zahid,et al.  Isaric 4c Mortality Score As A Predictor Of In-Hospital Mortality In Covid-19 Patients Admitted In Ayub Teaching Hospital During First Wave Of The Pandemic. , 2021, Journal of Ayub Medical College, Abbottabad : JAMC.

[11]  B. Tom,et al.  Changes in UK hospital mortality in the first wave of COVID-19: the ISARIC WHO Clinical Characterisation Protocol prospective multicentre observational cohort study , 2020, medRxiv.

[12]  S. V. van Kuijk,et al.  Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study , 2020, medRxiv.

[13]  Matthew Sperrin,et al.  Prediction models for covid-19 outcomes , 2020, BMJ.

[14]  Arthur S Slutsky,et al.  Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19: A Meta-analysis. , 2020, JAMA.

[15]  Jordan J. Clark,et al.  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 , 2020, BMJ.

[16]  P. Horby,et al.  Global outbreak research: harmony not hegemony , 2020, The Lancet Infectious Diseases.

[17]  P. Horby,et al.  Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study , 2020, BMJ.

[18]  Sonja A. Swanson,et al.  Prediction meets causal inference: the role of treatment in clinical prediction models , 2020, European Journal of Epidemiology.

[19]  M. Nishimura,et al.  Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations , 2020, The Lancet Respiratory Medicine.

[20]  Ting Yu,et al.  Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study , 2020, The Lancet Respiratory Medicine.

[21]  Andrew J. Vickers,et al.  A simple, step-by-step guide to interpreting decision curve analysis , 2019, Diagnostic and Prognostic Research.

[22]  Lena Osterhagen,et al.  Multiple Imputation For Nonresponse In Surveys , 2016 .

[23]  Johannes B. Reitsma,et al.  Individual Participant Data (IPD) Meta-analyses of Diagnostic and Prognostic Modeling Studies: Guidance on Their Use , 2015, PLoS medicine.

[24]  G. Collins,et al.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement , 2015, BMC Medicine.

[25]  Yvonne Vergouwe,et al.  Towards better clinical prediction models: seven steps for development and an ABCD for validation. , 2014, European heart journal.

[26]  P. Horby,et al.  Open source clinical science for emerging infections , 2013, The Lancet Infectious Diseases.

[27]  Stef van Buuren,et al.  MICE: Multivariate Imputation by Chained Equations in R , 2011 .

[28]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.