Prognostic models for COVID-19 to support risk stratification in secondary care

As of mid-July 2020, almost 600,000 people have died with COVID-19 (coronavirus) worldwide. Some patients who are admitted to hospital with COVID-19 experience a rapid worsening of their symptoms and go on to need intensive care treatment, ventilation (to help them breathe) or die. Because the virus is new and affects different people in different ways, doctors find that their clinical experience is not enough to help them to predict which patients are most likely to develop severe symptoms or die, and there is no tool which can help them to do this.

Therefore, the aim of our study is to develop tools that will help healthcare professionals to identify patients at high risk of needing intensive care treatment or ventilation, or of dying, as well as patients at low risk who can be safely discharged from hospital. This may provide an early opportunity to treat patients at high risk, while also making best use of limited hospital resources. We will do this by using anonymised patient data from hospitals in the UK to:

1. Develop a model which uses patients' symptoms, test results, and other information to predict their risk of needing intensive care treatment/ventilation, or dying.

2. Find groups of patients with similar test results and explore how their condition progresses.

Aim: The overarching aim of this study is to develop tools to aid clinicians to appropriately risk stratify confirmed COVID-19 cases in a UK hospital setting. There are 2 objectives:
1. Develop and externally validate prognostic models for i) the composite outcome of ITU admission and/or mechanical ventilation, and ii) death in a UK secondary care setting.
2. Identify distinct clusters based on biomarkers in patients diagnosed with COVID-19 and map prognostic trajectories in these patient groups, particularly with respect to progression to requiring ITU admission, ventilation or death.

Design: Retrospective cohort analyses using routinely collected secondary care data. For both the prognostic models and cluster analysis participants will all be followed from index (COVID-19 test) date until the earliest of outcome date or study end (latest available data). Patients will be censored 30 days after index date.

Study population: Hospitalised patients of all ages diagnosed with COVID-19 (defined as a positive test result from one or more RT-PCR test).

Primary outcomes: Prognostic models: i) A composite outcome of ITU admission and/or mechanical ventilation; and ii) death. Cluster analysis: Clinically useful clusters of indicators and associated prognosis: probability of ITU admission, mechanical ventilation or death. We will also explore whether clustering gives valuable information on potential clinical course of the illness, for example if it resulted in myocardial involvement, thromboembolic events, gastrointestinal symptoms and severe pneumonia.

Candidate predictors for the prognostic model: Predictors will be explored based on availability of data and existing evidence/biological plausibility. These will include: demographic characteristics; symptoms; frailty; vitals; biomarkers; radiography; physiological tests; and medications/treatments.