A Prediction Model for Severe COVID-19 Infection and Intensive Care Unit Admission in Pregnant Women

Abstract

Objective:This study developed a prediction model that can predict the intensive care admission of coronavirus disease-2019 (COVID-19) pregnant and postpartum women. Materials and Methods: The study was retrospective and single -center and was conducted with pregnant and postpartum patients 18 years of age and older who had been diagnosed with COVID-19 and were admitted to the obstetrics clinic between April 2020 and December 2021. The clinical and radiological featuresand laboratory values of the patients were recorded to develop a prediction model. Two different multivariate logistic regression models and the Naive Bayes classification algorithm were used for estimation. The results of the developed prediction models were summarized with the nomogram, and the prediction successes were evaluated with the receiver operating characteristic (ROC) curve. Results: The study included 436 pregnant and postpartum patients. Twelve of 51 patients admitted to the intensive care unit died. The specificities of the three different classification models that we developed to determine the risk factors for intensive care admission were found to be over 95% and their sensitivities were 70.6%, 86.3%, and 87%, respectively. Additionally, the area under the ROC values were found to be 0.94, 0.941 and 0.978 for the models, respectively. High procalcitonin level, fever, dyspnea, and moderate-to-severe radiological involvement were determined as risk factors for admission to intensive care in pregnant and postpartum women patients. Conclusion: It is thought that the risk models we have developed will be easy to implement and will help identify pregnant women who are at risk of severe COVID-19 disease in the early period and to take measures.