prognostic multivariable model on admission for hospitalized patients with - Evidencio
prognostic multivariable model on admission for hospitalized patients with COVID-19
This prediction model was developed by Xie J et al. (2020) to identify patients with lethal COVID19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate.
Research authors: Xie J, Hungerford D, Chen H, Abrams ST, Shusheng L, Wang G, Wang Y, Kang H, Bonnett L, Zheng R, Li X, Tong Z, Du B, Qiu H, Toh C
Version: 1.29
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Predicted probability of mortality:

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Intended use: 
There is an urgent need for effective prediction models to identify patients who are most likely to develop respiratory failure and poor outcomes. This model was developed to predict mortality of COVID-19 hospitalized patients to guide decision-making between clinicians, patients and families.

Model performance: 
The model has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c-statistic=0.89) and external (c=0.98) validation. When applied to new patients, the model yielded probabilities of mortality that were too high for low risk patients and too low for high risk patients (calibration slope >1). After recalibration of the model to account for underlying differences in the risk profile of the datasets, calibration plots showed excellent agreement between predicted and observed probabilities of mortality. 

Model limitations: 
Recruited patients were mainly self-referrals, who are more likely to have severe symptoms that cause them to seek emergency medical help. Other hospitals are more likely to test patients who have more pronounced symptoms, thus creating an inherent bias in the patient sample. Therefore, the cohort may not accurately represent patients with mild or asymptomatic COVID-19. These results need to be further validated with patients who are hospitalized at different locations and for different severities of illness to confirm generalizability and robustness of the model. Finally, the accompanying publication describing the development and validation of the model has not yet been peer reviewed. 

References: 
Preprint article by Xie et al, 2020 avaialble at MedRxiv. 

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This algorithm is provided for educational, training and information purposes. It must not be used to support medical decision making, or to provide medical or diagnostic services. Read our full disclaimer.

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