Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Abstract

Background: The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes.

Methods: Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die.

Results: SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q² = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors.

Conclusions: An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

Keywords: COVID-19; Machine learning; Mortality; Prediction model; SARS-CoV-2.

Fig. 1

SIMPLS-based scatter plot shows a good separation between hospital mortality of patients with COVID-19 from survivors. The figure illustrates only the training set-based scatter plot

Fig. 2

AUC for the separation of hospital mortality and survivors from COVID-19

Fig. 3

Predictive partition platform analysis shows the decision tree that predicts the hospital mortality in patients with COVID-19 from survivors. Blue square: survivors, red square: hospital mortality

Fig. 4

PCA plot illustrates the LCA-based clustering of patients with COVID-19. Clusters 2 and 3 are associated with a higher rate of mortality. Black circle: Survivors, red square: Hospital mortality

Fig. 5

SIMPLS-based scatter plot shows a very good separation between three clusters obtained by LCA. Clusters 1 includes the patients with a lower risk of dying, and clusters 2 and 3 include patients with a higher risk of dying