Risk factors associated with preterm birth: identification, prediction and evaluation in the BRISA cohort

Problem: Preterm birth is the leading cause of death and can result in significant long-term loss of physical and psychological capacity among survivors.Background: An estimated 15 million babies are born preterm every year. Prediction models based on machine learning methods have reported promising results.Aims: To identify risk factors associated with preterm birth and to develop and validate a prediction model for this outcome in a Brazilian birth cohort.Methods: Cross-sectional study of all births that occurred in Ribeirão Preto-SP and of one in three births that occurred in São Luís-MA, Brazil, in 2010. Questionnaires were applied to obtain pregnancy-related data. Multivariate adaptive regression splines were used to determine the independent variables. Preterm birth, defined as birth before 37 weeks gestational age, was the dependent variable. A random forest model was developed and its performance was evaluated by ROC analysis.Findings: The preterm birth rates were 12.7% (RP) and 14.1% (SL). The prediction and validation accuracies of the RF-based model were 91.3% and 85.5% respectively. The model can be applied starting in the third month of gestation and is more effective in identifying preterm infants with GA<31 weeks and 6 days (AUC=0.98).Discussion: It was possible to build a prediction model based on easily accessible low-cost data, without the need for complementary tests, providing results similar to those of other studies.Conclusions: Previous preterm birth and prenatal care were determinants. The use of an application for individualized patient monitoring an early stage can have positive effects on the quality of life of mother and child.

Hypertension defined by the 2017 ACC/AHA guideline is more accurate than 2018 ESC/ESH for detecting early vascular aging in young adults.

ABSTRACT: Determine the most accurate diagnostic criteria of arterial hypertension (AH) for detecting early vascular aging (EVA) defined by pulse wave velocity (PWV) higher than ≥9.2 m/s.Cross-sectional study of a birth cohort started in 1978/79. The following data were collected between April 6, 2016 and August 31, 2017 from 1775 participants: demographic, anthropometric, office blood pressure (BP) measurement, biochemical risk factors, and PWV. A subsample of 454 participants underwent 24-hour ambulatory BP monitoring. The frequencies of AH, and BP phenotypes were calculated according to both guidelines. BP phenotypes (white-coat hypertension, masked hypertension (MHT), sustained hypertension (SH) and normotension) were correlated with risk factors and subclinical target organ damage after adjustment for confounders by multiple linear regression. Receiver operating characteristic curves were constructed to determine the best BP threshold for detecting EVA.A higher frequency of AH (45.1 vs 18.5%), as well as of SH (40.7 vs 14.8%) and MHT (28.9 vs 25.8%) was identified using the 2017 ACC/AHA criteria comparing with 2018 ESC/ESH. EVA was associated with the higher-risk BP phenotypes (SH and MHT, P < .0001) in both criteria. There was a higher accuracy in diagnosing EVA, with the 2017 ACC/AHA criteria. Analysis of the receiver operating characteristic curves showed office BP cutoff value (128/83 mm Hg) for EVA closer to the 2017 ACC/AHA threshold.The 2017 AHA/ACC guideline for the diagnosis of AH, and corresponding ambulatory BP monitoring values, is more accurate for discriminating young adults with EVA. Clinical application of PWV may help identify patients that could benefit from BP levels <130/80 mm Hg.

Association of preterm birth and intrauterine growth restriction with childhood motor development: Brisa cohort, Brazil.

The present study investigated the association between preterm birth PT conditions, intrauterine growth restriction IUGR and the combination of both PT-IUGR with infant motor development. A cohort with 1006 children was monitored during prenatal, at birth, and two years of age. Bayley-III screening was used to evaluate of fine and gross motor skills. The data did not indicate an increased risk for motor delays in the PT or IUGR, composed mainly by mild cases. However, the combination of the conditions PT-IUGR increased the risk of delays in motor, which emphasizes the importance of monitoring the motor development of the group.