ABSTRACT Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. “Basic profile” was considered with clinical and demographic variables (33 variables), whereas in the “extended profile,” metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

Objective: to evaluate the risk of obstetric and perinatal outcomes in teenage pregnancy in comparison with adult pregnancy. Methods: retrospective cohort study of 67.693 pregnant women attended in a public hospital in Lima between 2000 and 2010. Obstetric and perinatal outcomes were evaluated. The adolescent group was divided in late adolescents (15-20 years), and early adolescents (<15 years) and was compared among the adult group (20-35 years). Adjusted odds ratios were calculated by education, civil status, prenatal care, previous pregnancies, parity, and pre-gesta-tional BMI. Results: higher risk of cesarean (OR=1,28; CI95%=1,07-1,53) and puerperal infection (OR=1,72; CI95%=1,17-2,53) was found in teenager under 15 years old; as well as higher risk of episiotomy (OR=1,34; CI95%=1,29-1,40) in late teenagers. In addition, this study identified a lower risk of teenage pregnancy for preeclampsia (OR=0,90; CI95%=0,85-0,97), 2nd half-pregnancy bleeding (OR=0,80; CI95%=0,71-0,92), premature rupture of membranes(OR=0,83; CI95%=0,79-0,87), preterm labor (OR=0,87; CI95%=0,80-0,94) and vaginal tearing (OR=0,86; CI95%=0,79-0,93). Conclusion: pregnancy behaves as a risk factor for some obstetric outcomes in the adolescent group, especially in the youngest ones. In addition to maternal age, there are other factors that constitute the need to form multidisciplinary teams to reduce obstetric outcomes in this population.

Objetivos: analizar el riesgo de complicaciones obstétricas y perinatales en adolescentes embarazadas en un hospital de Lima, Perú. Métodos: estudio de cohorte retrospectiva de 67.693 gestantes atendidas en el período 2000-2010. Se evaluó complicaciones obstétricas y perinatales. Las adolescentes se clasificaron en tardías (15-19 años) y tempranas (< 15 años) y se compararon con las adultas (20-35 años). Se calculó OR ajustados por educación, estado civil, control prenatal, gestaciones previas, paridad e IMC pregestacional. Resultados: se encontró mayor riesgo de cesárea (OR=1,28; IC95%=1,07-1,53) e infección puerperal (OR=1,72; IC95%=1,17-2,53) en las adolescentes menores de 15 años, así como mayor riesgo (OR=1,34; IC95%=1,29-1,40)de episiotomía en las adolescentes tardías. Asimismo, se identificó un menor riesgo del embarazo adolescente para preeclampsia (OR=0,90; IC95%=0,85-0,97), hemorragia de la 2da mitad del embarazo (OR=0,80; IC95%=0,71-0,92), ruptura prematura de membranas (OR=0,83; IC95%=0,79-0,87), amenaza de parto pretérmino (OR=0,87; IC95%=0,80-0,94) y desgarro vaginal (OR= 0,86; IC95%=0,79-0,93). Conclusión: el embarazo se comporta como factor de riesgo para ciertas complicaciones obstétricas en la población adolescente, especialmente en las adolescentes tempranas. Existen además otros factores, que sumados a la edad materna, constituyen la necesidad de formar equipos multidis-ciplinarios para reducir complicaciones obstétricas en esta población.