The Association between Evidence-Based Healthcare Practices and Outcomes among Preterm Births in China

Risk Factors for Mortality or Major Morbidities of Very Preterm Infants: A Study from Thailand

OBJECTIVE

Very preterm neonates have high rates of composite outcomes featuring mortality and major morbidities. If the modifiable risk factors could be identified, perhaps the rates could be decreased especially in resource-limited settings.

STUDY DESIGN

We performed a prospective study in a Thai neonatal intensive care unit to identify the risk factors of composite outcomes between 2014 and 2021. The inclusion criterion was neonates who were born in our hospital at a gestational age (GA) of less than 32 weeks. The exclusion criteria were neonates who died in the delivery room or had major congenital anomalies. The composite outcomes were analyzed by multivariable logistic regression with adjusted odds ratios (aORs) and a 95% confidence interval (CI).

RESULTS

Over the 8-year study period, 555 very preterm inborn neonates without major birth defects were delivered. The composite outcomes were 29.4% (163/555). The medians (interquartile ranges) of GA and birth weights of the neonates were 29 (27-31) weeks and 1,180 (860-1,475) grams, respectively. By multivariable analysis, GA (aOR: 0.65; 95% CI: 0.55-0.77), small for GA (aOR: 4.93; 95% CI: 1.79-13.58), multifetal gestation (aOR: 2.23; 95% CI: 1.12-4.46), intubation within 24 hours (aOR: 5.39; 95% CI: 1.35-21.64), and severe respiratory distress syndrome (aOR: 5.00; 95% CI: 1.05-23.89) were significantly associated with composite outcomes.

CONCLUSION

Very preterm infants who had a lower GA were small for GA, twins or more, respiratory failure on the first day of life, and severe respiratory distress syndrome were associated with mortality and/or major morbidities.

KEY POINTS

· In very preterm neonates, the composite outcomes and mortality rate were 29.4 and 12.3%.. · Composite outcomes were associated with lower GA, SGA, multifetal gestation, intubation, and severe RDS.. · Mortality was associated with lower GA or Apgar score at 5 minutes, SGA, and PPHN..

A prediction model for short-term neurodevelopmental impairment in preterm infants with gestational age less than 32 weeks

Introduction

Early identification and intervention of neurodevelopmental impairment in preterm infants may significantly improve their outcomes. This study aimed to build a prediction model for short-term neurodevelopmental impairment in preterm infants using machine learning method.

Methods

Preterm infants with gestational age  < 32 weeks who were hospitalized in The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and were followed-up to 18 months corrected age were included to build the prediction model. The training set and test set are divided according to 8:2 randomly by Microsoft Excel. We firstly established a logistic regression model to screen out the indicators that have a significant effect on predicting neurodevelopmental impairment. The normalized weights of each indicator were obtained by building a Support Vector Machine, in order to measure the importance of each predictor, then the dimension of the indicators was further reduced by principal component analysis methods. Both discrimination and calibration were assessed with a bootstrap of 505 resamples.

Results

In total, 387 eligible cases were collected, 78 were randomly selected for external validation. Multivariate logistic regression demonstrated that gestational age(p = 0.0004), extrauterine growth restriction (p = 0.0367), vaginal delivery (p = 0.0009), and hyperbilirubinemia (0.0015) were more important to predict the occurrence of neurodevelopmental impairment in preterm infants. The Support Vector Machine had an area under the curve of 0.9800 on the training set. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the curve on the test set is 0.70. The external validation proves the reliability of the prediction model.

Conclusion

A support vector machine based on perinatal factors was developed to predict the occurrence of neurodevelopmental impairment in preterm infants with gestational age  < 32 weeks. The prediction model provides clinicians with an accurate and effective tool for the prevention and early intervention of neurodevelopmental impairment in this population.