Conventional and Bayesian workflows for clinical prediction modelling of severe Covid-19 outcomes based on clinical biomarker test results: LabMarCS: Laboratory Markers of COVID-19 Severity - Bristol Cohort

We describe several regression models to predict severe outcomes in COVID-19 and challenges present in complex observational medical data. We demonstrate best practices for data curation, cross-validated statistical modelling, and variable selection emphasizing recent Bayesian methods. The study follows a retrospective observational cohort design using multicentre records across National Health Service (NHS) trusts in southwest England, UK. Participants included hospitalised adult patients positive for SARS-CoV 2 during March to October 2020, totalling 843 patients (mean age 71, 45% female, 32% died or needed ICU stay), split into training (n=590) and validation groups (n=253). Models were fit to predict severe outcomes (ICU admission or death within 28-days of admission to hospital for COVID-19, or a positive PCR result if already admitted) using demographic data and initial results from 30 biomarker tests collected within 3 days of admission or testing positive if already admitted. Cross-validation results showed standard logistic regression had an internal validation median AUC of 0.74 (95% Interval [0.62,0.83]), and external validation AUC of 0.68 [0.61, 0.71]; a Bayesian logistic regression (with horseshoe prior) internal AUC of 0.79 [0.71, 0.87], and external AUC of 0.70 [0.68, 0.71]. Variable selection performed using Bayesian predictive projection determined a four variable model using Age, Urea, Prothrombin time and Neutrophil-Lymphocyte ratio, with a median internal AUC of 0.79 [0.78, 0.80], and external AUC of 0.67 [0.65, 0.69]. We illustrate best-practices protocol for conventional and Bayesian prediction modelling on complex clinical data and reiterate the predictive value of previously identified biomarkers for COVID-19 severity assessment. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work is funded by Health Data Research UK via the Better Care Partnership Southwest (HDR CF0129), Medical Research Council Research Grant MR/T005408/1, and the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study [IRAS project ID: 283439] underwent a rigorous ethical and regulatory approval process, and a favourable opinion was gained from Research Ethics Service, Wales REC 7, c/o Public Health Wales, Building 1, Jobswell Road, St Davids Park, SA31 3HB on 11/09/2020. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Due to NHS data governance, all data produced in the present study are unavailable directly through the authors, but reasonable requests for data to Southwest England NHS can be arranged via the authors.