Association between preterm births and socioeconomic development: analysis of national data

Podocyte involvement in the pathogenesis of preterm-related long-term chronic kidney disease

With the continuous advancement of neonatal intensive care technology, the survival rate of preterm infants is gradually increasing. However, this improvement in survival is accompanied by long-term prognostic implications in various systems. In the field of renal diseases, current epidemiological data indicate that preterm birth is a significant risk factor for the development of long-term chronic kidney disease (CKD). This not only imposes an economic burden on patients families but also severely impacts their quality of life. Understanding the underlying mechanisms involved in this process could offer potential strategies for early prevention and management of CKD. Although the nephron number hypothesis is currently widely accepted as a mechanism, there has been limited exploration regarding podocytes - one of the most important structures within nephrons - in relation to long-term CKD associated with preterm birth. Therefore, this review aims to summarize current knowledge on how prematurity influences CKD development overall, while specifically focusing on our current understanding of podocytes in relation to prematurity.

Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data

Background

To develop and compare different AutoML frameworks and machine learning models to predict premature birth.

Methods

The study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. Three Automatic Machine Learning (AutoML) were used to construct machine learning models including tree-based models, ensembled models, and deep neural networks on the training sample (N = 536,971). The area under the curve (AUC) and training times were used to assess the performance of the prediction models, and feature importance was computed via permutation-shuffling.

Results

The H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Auto-sklearn (median AUC: 0.820), and the median training time was the lowest for H2O AutoML (0.14 min), followed by AutoGluon (0.16 min) and Auto-sklearn (4.33 min). Among different types of machine learning models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XGBoost), stacked ensemble, and random forrest models had better predictive performance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Important features related to preterm birth included premature rupture of membrane (PROM), incompetent cervix, occupation, and preeclampsia.

Conclusions

Our study highlights the potential of machine learning models in predicting the risk of preterm birth using readily available electronic medical record data, which have significant implications for improving prenatal care and outcomes.