Machine learning based early warning system enables accurate mortality risk prediction for COVID-19

Abstract

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

“…Results indicate that by focusing on a limited number of demographic variables, including age, gender and BMI, it is possible to predict the risk of hospital and ICU admission, use of mechanical ventilation and death as early as at the time of diagnosis. Using these parameters only, our model achieved a ROC-AUC of 0.902 for mortality prediction, which is slightly inferior to a model reported by Gao et al achieving a ROC-AUC of 0.962 using more complex clinical data points on admission 3 .…”

“…Several studies have now proposed prediction models based on a variety of clinical features 1 – 3 , indicating good predictive ability of machine learning (ML) models, including combinations of Support Vector Machines, Logistic Regression, Gradient Boosted Decision trees, Neural Networks and Random Forests, on mortality prediction 3 , 4 . Tasks such as respiratory decompensation 5 , X-ray and clinical feature detection 6 , 7 and SARS-CoV-2 detection optimisation 8 , 9 have also been addressed.…”

“…As such, a recent review of ML models 10 , aimed at risk prediction in SARS-CoV-2 positive patients found that the majority of studies were constructed on Chinese data with a high risk of bias as assessed by the Prediction model Risk of Bias Assessment tool (PROBAST) 11 . Furthermore, models often utilize data from hospital admitted SARS-CoV-2 positive patients only 3 , which may skew results due to the lack of data from patients with milder disease trajectories. Finally, models are often developed for predictions at hospital admission 1 – 3 , providing little insight into the effects of in-hospital management.…”

“…Furthermore, models often utilize data from hospital admitted SARS-CoV-2 positive patients only 3 , which may skew results due to the lack of data from patients with milder disease trajectories. Finally, models are often developed for predictions at hospital admission 1 – 3 , providing little insight into the effects of in-hospital management.…”

Proposed Mechanisms of Targeting COVID-19 by Delivering Mesenchymal Stem Cells and Their Exosomes to Damaged Organs

“…The first issue in applying MSCs in COVID-19 is the eligibility of the patients for CBT. There is insufficient information on use of MSCbased therapy in patients with a history of chronic diseases or excluding conditions such as malignancies, immune system disorders, allergies, pregnancy, and lactating mothers [18,169]. As the second problem, the majority of clinical trials using MSCs or their exosomes in COVID-19 are in phase I/ II which led to insufficient outcomes.…”