Unveiling the hidden burden of COVID-19 in Brazil’s obstetric population with severe acute respiratory syndrome: A machine learning model

Objective: To predict the actual number of COVID-19 cases in Brazilian pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome using a predictive model created based on data from Brazilian database. Methods: This is a cross-sectional study with pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome (SARS) between January 2016 and November 2021 in Brazil. Patients were divided into two groups (COVID-19 and non-COVID-19) for comparative analysis, and a predictive model was constructed to classify cases without a defined causative agent. Main outcome measures: Estimated number of COVID cases in obstetric patients with SARS and no defined agent. Results: 38,774 pregnant and postpartum women diagnosed with SARS were identified and categorized by date and causative agent. Women in the COVID-19 group (19.138) were older (29.86 ± 7.20 years), self-reported more frequently as non-white race (50.9%), and more often had educational status marked as blank or ignored (29.6% and 26.9%, respectively) compared to the Other confirmed agents’ group (2.233). The groups differed in all presented variables, and patients in the COVID-19 group were diagnosed more often in the third trimester of pregnancy or in the postpartum period. Using the XGBoost model, 13,978 cases of SARS with undefined etiology from 2020 and 2021 were reclassified: 13,799 (98.7%) as predicted COVID-19 and 179 (1.3%) as predicted non-COVID-19. Conclusions: The number of COVID-19 cases and deaths in the obstetric population were even higher than reported by authorities, indicating a significant impact on the maternal mortality ratio during this period.