Risk of Early Childhood Obesity in Offspring of Women with Preeclampsia: A Population-Based Study.

Preeclampsia (PE) is a major disease of pregnancy, with various short- or long-term complications for both the mother and offspring. We focused on the body mass index (BMI) of offspring and compared the incidence of obesity during early childhood between PE- and non-PE-affected pregnancies. Women with singleton births (n = 1,697,432) were identified from the Korea National Health Insurance database. The outcomes of offspring at 30-80 months of age were analyzed. The effects of PE on BMI and the incidence of obesity in the offspring were compared. The incidence of low birth weight (LBW) offspring was higher in the PE group (n = 29,710) than that in the non-PE group (n = 1,533,916) (24.70% vs. 3.33%, p < 0.01). However, BMI was significantly higher in the PE-affected offspring than that in non-PE-affected offspring. After adjusting for various factors, the risk of obesity was higher in the PE-affected offspring (odds ratio = 1.34, 95% confidence interval = 1.30-1.38). The BMI and incidence of obesity were higher during early childhood in the PE-affected offspring, even though the proportion of LBW was higher. These results may support the basic hypotheses for the occurrence of various cardiovascular and metabolic complications in PE-affected offspring. In addition, early-age incidence of obesity could influence PE management and child consultation in clinical applications.

Paradoxical long-term impact of maternal influenza infection on neonates and infants.

BACKGROUND: Pregnant women are at high risk of influenza-related morbidity and mortality. In addition, maternal influenza infection may lead to adverse birth outcomes. However, there is insufficient data on long-term impact of maternal influenza infection. METHODS: This study was conducted to assess the impact of maternal influenza infection on birth outcomes and long-term influence on infants by merging the Korea National Health Insurance (KNHI) claims database and National Health Screening Program for Infants and Children (NHSP-IC). Mother-offspring pairs were categorized by maternal influenza infection based on the ICD-10 code. RESULTS: Multivariate analysis revealed that maternal influenza infection significantly increased the risk of preterm birth (OR 1.408) and low birth weight (OR 1.198) irrespective of gestational age. The proportion of low birth weight neonates was significantly higher in influenza-infected women compared to those without influenza. However, since the fourth health screening (30-80 months after birth), the fraction of underweight was no longer different between children from influenza-infected and non-infected mothers, whereas the rates of overweight increased paradoxically in those born to mothers with influenza infection. CONCLUSIONS: Maternal influenza infection might have long-term effects on the health of children and adolescents even after infancy.

Peripartum Blood Transfusions are Associated with Increased Risk of Cancer: A National Retrospective Cohort Study.

BACKGROUND: The effect of blood transfusions on the risk of developing primary cancer remains unclear, especially when administered in the peripartum period. MATERIALS AND METHODS: We conducted a retrospective cohort study of 270,529 pregnant women who delivered between January 1, 2007 and December 31, 2009, with data obtained from three national databases in South Korea. From this cohort, we identified 4569 patients who received peripartum blood transfusions. We calculated hazard ratios (HRs) for new diagnoses of cancer and adjusted them for relevant clinical factors using a Cox proportional hazards model. RESULTS: During follow-up, patients who received peripartum transfusions had an increased risk of developing cancer, with an adjusted HR of 1.16 (95% confidence interval [CI], 1.01-1.34). In a subgroup analysis, this risk was significant only among patients who received 3 or more units of blood, with an adjusted HR of 1.40 (95% CI, 1.10-1.79). Increased risk after transfusions were seen with brain, lung, ovarian, and gallbladder cancers. The difference in cancer risk between the transfusion and no-transfusion groups remained significant during both the first (1.29% vs 1.07%, p < 0.01) and second year (0.74% vs 0.56%, p < 0.01) after delivery. CONCLUSION: Receipt of 3 or more blood transfusions in the peripartum period was associated with a significantly increased risk of developing cancer. Prospective studies should be pursued to further understand the link between blood transfusions and long-term oncologic risks.

Machine Learning Models for Predicting Hearing Prognosis in Unilateral Idiopathic Sudden Sensorineural Hearing Loss.

OBJECTIVES: Prognosticating idiopathic sudden sensorineural hearing loss (ISSNHL) is an important challenge. In our study, a dataset was split into training and test sets and cross-validation was implemented on the training set, thereby determining the hyperparameters for machine learning models with high test accuracy and low bias. The effectiveness of the following five machine learning models for predicting the hearing prognosis in patients with ISSNHL after 1 month of treatment was assessed: adaptive boosting, K-nearest neighbor, multilayer perceptron, random forest (RF), and support vector machine (SVM). METHODS: The medical records of 523 patients with ISSNHL admitted to Korea University Ansan Hospital between January 2010 and October 2017 were retrospectively reviewed. In this study, we analyzed data from 227 patients (recovery, 106; no recovery, 121) after excluding those with missing data. To determine risk factors, statistical hypothesis tests (e.g., the two-sample t-test for continuous variables and the chi-square test for categorical variables) were conducted to compare patients who did or did not recover. Variables were selected using an RF model depending on two criteria (mean decreases in the Gini index and accuracy). RESULTS: The SVM model using selected predictors achieved both the highest accuracy (75.36%) and the highest F-score (0.74) on the test set. The RF model with selected variables demonstrated the second-highest accuracy (73.91%) and F-score (0.74). The RF model with the original variables showed the same accuracy (73.91%) as that of the RF model with selected variables, but a lower F-score (0.73). All the tested models, except RF, demonstrated better performance after variable selection based on RF. CONCLUSION: The SVM model with selected predictors was the best-performing of the tested prediction models. The RF model with selected predictors was the second-best model. Therefore, machine learning models can be used to predict hearing recovery in patients with ISSNHL.