Med
Volume 2, Issue 4, 9 April 2021, Pages 435-447.e4
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Clinical and Translational Article
Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19 patients

https://doi.org/10.1016/j.medj.2020.12.013Get rights and content
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Highlights

  • The blood count-based PAWNN score can accurately predict mortality risk of COVID-19

  • PAWNN can properly stratify COVID-19 patients with limited medical resources

Context and significance

Researchers from Wuhan, China developed a complete blood count-based risk score (PAWNN score) that can predict mortality during the entire course of hospitalization in a large cohort with 13,138 COVID-19 patients. This risk score was validated in two independent cohorts from China and Italy and in a longitudinal model. The model can serve as a valuable tool for physicians with very limited medical resources to properly monitor and stratify COVID-19 patients and to reduce mortality.

Summary

Background

To develop a sensitive risk score predicting the risk of mortality in patients with coronavirus disease 2019 (COVID-19) using complete blood count (CBC).

Methods

We performed a retrospective cohort study from a total of 13,138 inpatients with COVID-19 in Hubei, China, and Milan, Italy. Among them, 9,810 patients with 2 CBC records from Hubei were assigned to the training cohort. CBC parameters were analyzed as potential predictors for all-cause mortality and were selected by the generalized linear mixed model (GLMM).

Findings

Five risk factors were derived to construct a composite score (PAWNN score) using the Cox regression model, including platelet counts, age, white blood cell counts, neutrophil counts, and neutrophil:lymphocyte ratio. The PAWNN score showed good accuracy for predicting mortality in 10-fold cross-validation (AUROCs 0.92–0.93) and subsets with different quartile intervals of follow-up and preexisting diseases. The performance of the score was further validated in 2,949 patients with only 1 CBC record from the Hubei cohort (AUROC 0.97) and 227 patients from the Italian cohort (AUROC 0.80). The latent Markov model (LMM) demonstrated that the PAWNN score has good prediction power for transition probabilities between different latent conditions.

Conclusions

The PAWNN score is a simple and accurate risk assessment tool that can predict the mortality for COVID-19 patients during their entire hospitalization. This tool can assist clinicians in prioritizing medical treatment of COVID-19 patients.

Funding

This work was supported by National Key R&D Program of China (2016YFF0101504, 2016YFF0101505, 2020YFC2004702, 2020YFC0845500), the Key R&D Program of Guangdong Province (2020B1111330003), and the medical flight plan of Wuhan University (TFJH2018006).

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Keywords

COVID-19
risk score
mortality
complete blood count
prediction model
latent markov model

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18

These authors contributed equally

19

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