Machine learning-based prognostic model to predict criticality in patients - Evidencio
Machine learning-based prognostic model to predict criticality in patients with severe Covid-19 infection
Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan
Research authors: Yan L, Zhang H, Xiao Y, Wang M, Guo Y, Sun C, Tang X, Jing L, Li S, Zhang M, Xiao Y, Cao H, Chen Y, Ren T, Jin J, Wang F, Xiao Y, Huang S, Tan X, Huang N, Jiao B, Zhang Y, Luo A, Cao Z, Xu H, Yuan Y
Version: 1.10
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Predicted probability of mortality: %

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Yan et al. developed a machine learning-based a decision rule using three key features: LDH, hsCRP, and lymphocyte percentage. 

Context information on risk factors: 
The increase of LDH reflects tissue/cell destruction and is regarded as a common sign of tissue/cell damage. Serum LDH has been identified as an important biomarker for the activity and severity of Idiopathic Pulmonary Fibrosis (IPF). In patients with severe pulmonary interstitial disease, the increase of LDH is significant and is one of the most important prognostic markers of lung injury. For the critically ill patients with COVID-19, the rise of LDH level indicates an increase of the activity and extent of lung injury.

The analysis performed by Yan et al, showed that higher serum hs-CRP could be used to predict the risk of death in severe COVID-19 patients. The increase of hs-CRP, an important marker for poor prognosis in ARDS, reflects the persistent state of inflammation.

Previous results suggest that lymphocytes play vital role in forecasting of progression from mild to critically ill and may serve as a potential therapeutic target. The hypothesis is supported by the results of clinical studies. Moreover, lymphopenia is a common feature in the patients with COVID-19 and might be a critical factor associated with disease severity and mortality.

Study limitations: 
First of all, since the proposed machine learning method is purely data driven, its model may vary given a different set of training and validation dataset. Given the limit number of samples in this study, a balance between model complexity and performance was sought. Yet the whole procedure should follow when more data is available.
Secondly, the performed study was a single-centered, retrospective study, which provides a preliminary assessment of the clinical course and outcome of severe patients.

Source: 
Yan et al. Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. (Preprint). 

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