Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care (preprint)

Objectives Existing UK prognostic models for patients admitted to hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death, intensive therapy unit (ITU) admission) in UK secondary care; and externally validate the existing 4C score. Design Candidate predictors included demographic variables, symptoms, physiological measures, imaging, laboratory tests. Final models used logistic regression with stepwise selection. Setting Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. Participants Patients with COVID-19 admitted to UHB January-August 2020 were included. Main outcome measures Death and ITU admission within 28 days of admission. Results 1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating curve (AUROC) for mortality was 0.791 (95%CI 0.761-0.822) in UHB and 0.767 (95%CI 0.754-0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95%CI 0.883-0.929) in UHB and 0.811 (95%CI 0.795-0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the 4C score in the UHB dataset was 0.754 (95%CI 0.721-0.786). Conclusions The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and outperformed the existing 4C score. The models can be integrated into electronic medical records systems to calculate each individual patients probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.

Development and External Validation of Prognostic Models for COVID-19 to Support Risk Stratification in Secondary Care (preprint)

Background: Existing UK prognostic models for patients admitted to hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death, intensive therapy unit (ITU) admission) in UK secondary care; and externally validate the existing 4C score. Methods: Patients with COVID-19 admitted to University Hospitals Birmingham (UHB) January-August 2020 were included. Candidate predictors included demographic variables, symptoms, physiological measures, imaging, laboratory tests. Final models used logistic regression with stepwise selection. External validation was performed in the CovidCollab dataset. Findings: 1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating curve (AUROC) for mortality was 0.791 (95%CI 0.761-0.822) in UHB and 0.767 (95%CI 0.754-0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95%CI 0.883-0.929) in UHB and 0.811 (95%CI 0.795-0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the 4C score in the UHB dataset was 0.754 (95%CI 0.721-0.786). Interpretation: The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and outperformed the existing 4C score. The models can be integrated into electronic medical records systems to calculate each individual patient’s probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated. Funding: Medical Research Council UK Research and Innovation.Declaration of Interests: NJA, ES, KN, MP, AD, CS, TT and YT report a grant from UKRI MRC during the conduct of the study. ES reports grants from National Institute for Health Research (NIHR), Wellcome Trust, MRC, Health Data Research UK (HDR-UK), British Lung Foundation, and Alpha 1 Foundation outside the submitted work. KN reports grants from MRC and HDR-UK outside the submitted work. DP reports grants from NIHR, MRC, and Chernakovsky Foundation outside the submitted work. All other authors have nothing to declare.Ethics Approval Statement: Ethical approval was provided by the East Midlands – Derby REC (reference: 20/EM/0158) for the PIONEER Research Database.