Neonatal mortality prediction with routinely collected data: a machine learning approach.

BACKGROUND: Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas. METHODS: A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data. RESULTS: The best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO's five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records. CONCLUSION: Machine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.

Risk factors for adverse outcomes among pregnant and postpartum women with acute respiratory distress syndrome due to COVID-19 in Brazil.

OBJECTIVE: To evaluate whether clinical and social risk factors are associated with negative outcomes for COVID-19 disease among Brazilian pregnant and postpartum women. METHODS: A secondary analysis was conducted of the official Acute Respiratory Syndrome Surveillance System database. Pregnant and postpartum women diagnosed with COVID-19 ARDS until July 14, 2020, were included. Adverse outcomes were a composite endpoint of either death, admission to the intensive care unit (ICU), or mechanical ventilation. Risk factors were examined by multiple logistic regression. RESULTS: There were 2475 cases of COVID-19 ARDS. Among them, 23.8% of women had the composite endpoint and 8.2% died. Of those who died, 5.9% were not hospitalized, 39.7% were not admitted to the ICU, 42.6% did not receive mechanical ventilation, and 25.5% did not have access to respiratory support. Multivariate analysis showed that postpartum period, age over 35 years, obesity, diabetes, black ethnicity, living in a peri-urban area, no access to Family Health Strategy, or living more than 100 km from the notification hospital were associated with an increased risk of adverse outcomes. CONCLUSION: Clinical and social risk factors and barriers to access health care are associated with adverse outcomes among maternal cases of COVID-19 ARDS in Brazil.