Possible Factors Related to Mortality of Pregnant Women with COVID-19: A Machine Learning Approach

Abstract

Background: The COVID-19 pandemic has affected people of all ages and backgrounds, but pregnant women are a particularly vulnerable group. They are at increased risk of severe illness and death from COVID-19, yet there is limited and conflicting data about the factors that contribute to mortality in this population. In this study, we sought to use machine learning (ML) to predict the possible factors associated with mortality in pregnant women with COVID-19. Methods: We collected data from a large cohort of pregnant women who had been diagnosed with COVID-19 at Hung Vuong Hospital. We included a range of demographic, clinical, and laboratory variables in our analysis, such as age, gestational age, comorbidities, symptoms, and laboratory test results. Our goal was to identify the critical factors that could be used to predict mortality in this vulnerable population. We applied several ML models and analyzed the results to determine the most useful predictors. Results: We analyzed a cohort of 550 pregnant women diagnosed with COVID-19, comprising 525 survivors and 25 deaths. Using ML algorithms, we identified key clinical and patient factors that correlated with mortality risk, such as infection severity, pulse rate, breathing patterns, SpO2 levels, rapid diagnostic tests, internal pathology, breathing difficulties, and changes in consciousness. Additionally, several medical test factors were found to be significant, including white blood cells, neutrophils, platelets, C-reactive protein (CRP), procalcitonin, and chest X-ray abnormalities. Our analysis revealed that the Gaussian Naive Bayes (NB) model was the most accurate, with an AUC score of 0.79, demonstrating its potential as a valuable tool for identifying high-risk patients. Conclusions: Our results suggest that the available data can capture the relationship between COVID-19 symptoms and mortality in pregnant women. More studies are needed to improve the accuracy and generalizability of models for predicting mortality risk in this population.