The perinatal consequences of SARS-CoV-2 infection are still largely unknown. This study aimed to describe the features and outcomes of pregnant women with or without SARS-CoV-2 infection after the universal screening was established in a large tertiary care center admitting only obstetric related conditions without severe COVID-19 in Mexico City. This retrospective case-control study integrates data between April 22 and May 25, 2020, during active community transmission in Mexico, with one of the highest COVID-19 test positivity percentages worldwide. Only pregnant women and neonates with a SARS-CoV-2 result by quantitative RT-PCR were included in this study. Among 240 pregnant women, the prevalence of COVID-19 was 29% (95% CI, 24% to 35%); 86% of the patients were asymptomatic (95% CI, 76%-92%), nine women presented mild symptoms, and one patient moderate disease. No pregnancy baseline features or risk factors associated with severity of infection, including maternal age > 35 years, Body Mass Index >30 kg/m², and pre-existing diseases, differed between positive and negative women. The median gestational age at admission for both groups was 38 weeks. All women were discharged at home without complications, and no maternal death was reported. The proportion of preeclampsia was higher in positive women than negative women (18%, 95% CI, 10%-29% vs. 9%, 95% CI, 5%-14%, P<0.05). No differences were found for other perinatal outcomes. SARS-CoV-2 test result was positive for nine infants of positive mothers detected within 24h of birth. An increased number of infected neonates were admitted to the NICU, compared to negative neonates (44% vs. 22%, P<0.05) and had a longer length of hospitalization (2 [2–18] days vs. 2 [2–3] days, P<0.001); these are potential proxies for illness severity. This report highlights the importance of COVID-19 detection at delivery in pregnant women living in high transmission areas.

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis: (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were R² > 0.97. Critical variables for IP prediction were identified: neonatal platelets and neutrophils; orotracheal intubation; birth weight; sex; arterial blood gas parameters (pCO₂ and HCO₃); gestational age; use of fortifier; patent ductus arteriosus; maternal age and maternal morbidity. These models may allow quality improvement in medical practice.