Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study

Abstract

Background: At the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic increased the hospital burden of COVID-19 caused by the SARS-Cov-2 and became the most significant health challenge for nations worldwide. The severity and high mortality of COVID-19 have been correlated with various demographic characteristics and clinical manifestations. Prediction of mortality rate, identification of risk factors, and classification of patients played a crucial role in managing COVID-19 patients. Our purpose was to develop machine learning (ML)-based models for the prediction of mortality and severity among patients with COVID-19. Identifying the most important predictors and unraveling their relationships by classification of patients to the low-, moderate- and high-risk groups might guide prioritizing treatment decisions and a better understanding of interactions between factors. A detailed evaluation of patient data is believed to be important since COVID-19 resurgence is underway in many countries.

Results: The findings of this study revealed that the ML-based statistically inspired modification of the partial least square (SIMPLS) method could predict the in-hospital mortality among COVID-19 patients. The prediction model was developed using 19 predictors including clinical variables, comorbidities, and blood markers with moderate predictability (Q² = 0.24) to separate survivors and non-survivors. Oxygen saturation level, loss of consciousness, and chronic kidney disease (CKD) were the top mortality predictors. Correlation analysis showed different correlation patterns among predictors for each non-survivor and survivor cohort separately. The main prediction model was verified using other ML-based analyses with a high area under the curve (AUC) (0.81-0.93) and specificity (0.94-0.99). The obtained data revealed that the mortality prediction model can be different for males and females with diverse predictors. Patients were classified into four clusters of mortality risk and identified the patients at the highest risk of mortality, which accentuated the most significant predictors correlating with mortality.

Conclusion: An ML model for predicting mortality among hospitalized COVID-19 patients was developed considering the interactions between factors that may reduce the complexity of clinical decision-making processes. The most predictive factors related to patient mortality were identified by assessing and classifying patients into different groups based on their sex and mortality risk (low-, moderate-, and high-risk groups).

Keywords: COVID-19; COVID-19 risk factors; clustering COVID-19 patients; machine learning; prediction model.

FIGURE 1

Statistically inspired modification of the partial least square (SIMPLS)-based scatter plot indicating a good separation between COVID-19 survivors and non-survivors.

FIGURE 2

Coefficient plot shows the relative correlation of 19 most differentiating variables to predict mortality. Loss of consciousness, oxygen saturation < 88 and chronic kidney disease shows the highest relative correlation with mortality.

FIGURE 3

Multivariate correlation heat map clearly indicating a different pattern between survivors and non-survivors. Age > 65, BMI > 24.8 and hypertension as well as oxygen saturation < 88, cardiovascular disease and chronic kidney disease are more correlated among non-survivors than survivors.

FIGURE 4

Principal component analysis (PCA) plot illustrates the Latent class analysis (LCA)-based clustering of COVID-19 patients. Clusters 3 and 4 are correlated with a higher mortality rate.

FIGURE 5

Principal component analysis (PCA) scatter plot shows a very good separation between four clusters obtained from Latent class analysis (LCA) analysis. Cluster 1 and 2 included the patients with a lower mortality risk, while clusters 3 included moderate risk of mortality, and cluster 4 included patients with higher mortality.