Abstract
Background: Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established, while this mortality prediction model could help for early triaging, optimizing the medical resource and adjust the treatments in time. Methods: To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from Wuhan cohort, generating a mortality prediction model based on their clinical features. Then, the 81 patients from Würzburg cohort were used as independent test data for this model. Findings: We identified five clinical features, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. Interpretation: This study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients. Funding: National Science Foundation of China, National Key Research and Development Program of China and Urgent projects of scientific and technological research on COVID-19 funded by Hubei province.Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: This study was approved by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Due to the retrospective nature of this study, the local institutional review board of the University of Würzburg waived the requirement for additional approval.
Highlights
The pandemic of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern (Salyer et al, 2021; Sirleaf and Clark, 2021; Watson and Lilford, 2021)
The Wuhan cohort contained 1,352 COVID-19 patients from Wuhan Union Hospital, and it has been utilized for establishing a multi-feature and time-series aware machine learning models
The Würzburg cohort consists of 81 COVID-19 patients and has been used as an independent validation cohort
Summary
The pandemic of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern (Salyer et al, 2021; Sirleaf and Clark, 2021; Watson and Lilford, 2021). Liang et al developed a deep learning survival Cox model for 1,590 patients’ triage, which was based on four clinical features and six phenotypic characteristics, to ensure patients at the greatest risk for severe illness receive appropriate care as early as possible (Liang et al, 2020). Wu et al used the Cox model to investigate the key risk factors and predicted the mortality rate of 21,392 COVID-19 patients based on demographic, clinical, and laboratory features and found that the mortality rate increased with time, especially for these critically ill patients (Wu et al, 2020)
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