Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care

Abstract

Background

There is a paucity of prognostic models for COVID-19 that are usable for in-office patient assessment in general practice (GP).

Objectives

To develop and validate a risk prediction model for hospital admission with readily available predictors.

Methods

A retrospective cohort study linking GP records from 8 COVID-19 centres and 55 general practices in the Netherlands to hospital admission records. The development cohort spanned March to June 2020, the validation cohort March to June 2021. The primary outcome was hospital admission within 14 days. We used geographic leave-region-out cross-validation in the development cohort and temporal validation in the validation cohort.

Results

In the development cohort, 4,806 adult patients with COVID-19 consulted their GP (median age 56, 56% female); in the validation cohort 830 patients did (median age 56, 52% female). In the development and validation cohort respectively, 292 (6.1%) and 126 (15.2%) were admitted to the hospital within 14 days, respectively. A logistic regression model based on sex, smoking, symptoms, vital signs and comorbidities predicted hospital admission with a c-index of 0.84 (95% CI 0.83 to 0.86) at geographic cross-validation and 0.79 (95% CI 0.74 to 0.83) at temporal validation, and was reasonably well calibrated (intercept −0.08, 95% CI −0.98 to 0.52, slope 0.89, 95% CI 0.71 to 1.07 at geographic cross-validation and intercept 0.02, 95% CI −0.21 to 0.24, slope 0.82, 95% CI 0.64 to 1.00 at temporal validation).

Conclusion

We derived a risk model using readily available variables at GP assessment to predict hospital admission for COVID-19. It performed accurately across regions and waves. Further validation on cohorts with acquired immunity and newer SARS-CoV-2 variants is recommended.

KEY MESSAGES

• A general practice prediction model based on signs and symptoms of COVID-19 patients reliably predicted hospitalisation.

• The model performed well in second-wave data with other dominant variants and changed testing and vaccination policies.

• In an emerging pandemic, GP data can be leveraged to develop prognostic models for decision support and to predict hospitalisation rates.

Keywords:

• Covid-19
• prognosis
• clinical risk prediction model
• general practice

Acknowledgements

We thank Ron Kusters, Linda Grossfeldt, Anita Speetgens and Joost Frenken for contributing data. We thank Mascha Twellaar for her assistance in preparing the data for analysis and Luc Smits for his advice on study design and analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The Netherlands Organisation for Health Research and Development [ZonMW grant 10430012010001] funded this study. The funders had no role in study design, data collection, analysis, interpretation, or report writing.