A biomarker based severity progression indicator for COVID-19: the Kuwait prognosis indicator score

Abstract

Background

COVID-19 is a worldwide pandemic that is mild in most patients but can result in a pneumonia like illness with progression to acute respiratory distress syndrome and death. Predicting the disease severity at time of diagnosis can be helpful in prioritizing hospital admission and resources.

Methods

We prospectively recruited 1096 consecutive patients of whom 643 met the inclusion criterion with COVID-19 from Jaber Hospital, a COVID-19 facility in Kuwait, between 24 February and 20 April 2020. The primary endpoint of interest was disease severity defined algorithmically. Predefined risk variables were collected at the time of PCR based diagnosis of the infection. Prognostic model development used 5-fold cross-validated regularized logit regression. The model was externally validated against data from Wuhan, China.

Results

There were 643 patients with clinical course data of whom 94 developed severe COVID-19. In the final model, age, CRP, procalcitonin, lymphocyte percentage, monocyte percentages and serum albumin were independent predictors of a more severe illness course. The final prognostic model demonstrated good discrimination, and both discrimination and calibration were confirmed with an external dataset.

Conclusion

We developed and validated a simple score calculated at time of diagnosis that can predict patients with severe COVID-19 disease reliably and that has been validated externally. The KPI score calculator is now available online at covidkscore.com

Keywords:

• COVID-19
• adverse outcome
• mortality
• procalcitonin
• prognosis
• health policy

Disclosure statement

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

Additional information

Funding

Suhail A. Doi and Mohammed Chowdhury were supported by Qatar University COVID19 Emergency Response Grant [QUERG-CENG-2020-1]. The responsibility for the findings herein are solely the responsibility of the authors.Open Access funding provided by the Qatar National Library.