Maternal pregnancy-induced hypertension increases the subsequent risk of neonatal candidiasis: A nationwide population-based cohort study

Subsequent Development of Epithelial Ovarian Cancer After Ovarian Surgery for Benign Ovarian Tumor: A Population-Based Cohort Study

Purpose

The goal of the current study is to determine the risk of subsequent development of epithelial ovarian cancer (EOC) in women after ovarian surgery for benign ovarian tumors.

Patients and Methods

We conducted the nationwide population-based historic cohort study using the National Health Insurance Research Database (NHIRD) of Taiwan. Eleven thousand six hundred twenty women who underwent ovarian surgery for ovarian benign diseases were analyzed. The collected data included age, types of ovarian surgery, medical history by Charlson comorbidity index (CCI), infertility (yes/no), pelvic inflammatory disease (PID) (yes/no), tubal ligation (yes/no), total/subtotal hysterectomy (TH/STH) (yes/no), and endometrioma (yes/no). We used the Kaplan-Meier method and the Log-rank test to evaluate the risk factors. Cox proportional hazard methods were used to evaluate risk factors for the subsequent development of EOC. Multivariate analysis using Cox stepwise forward regression was conducted for the covariate selected in univariate analysis. Hazard ratio (HR) and 95% confidence interval (CI) were calculated using the Wald test.

Results

Subsequent EOC incidence rate (IR, incidence per 10,000 person-years) of women after ovarian surgery for benign ovarian tumors was 2.98. Separating into four groups based on different age, IR of EOC was 1.57 (<30 years), 4.71 (30-39 years), 3.59 (40-49 years) and 0.94 (≥50 years), respectively. Univariate and multivariate analyses identified only high level of CCI (≥2 or more) as an independent risk factor for subsequent development of EOC in women after ovarian surgery for benign ovarian tumors (HR 59.17, 95% CI 7.50-466.80 in women with CCI level of 2 and HR 190.68, 95% CI 24.33-2494.19, in women with CCI level ≥3, respectively).

Conclusion

Our results, if confirmed, suggest that women with other comorbidities (CCI) should be well informed that they may have a higher risk of subsequent development of EOC when ovarian surgery is planned even though the final pathology showed a benign ovarian tumor.

Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia

Background

We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia.

Methods

The BPJS Kesehatan dataset have been preprocessed using a nested case-control design into preeclampsia/eclampsia (n = 3318) and normotensive pregnant women (n = 19,883) from all women with one pregnancy. The dataset provided 95 features consisting of demographic variables and medical histories started from 24 months to event and ended by delivery as the event. Six algorithms were compared by area under the receiver operating characteristics curve (AUROC) with a subgroup analysis by time to the event. We compared our model to similar prediction models from systematically reviewed studies. In addition, we conducted a text mining analysis based on natural language processing techniques to interpret our modeling results.

Findings

The best model consisted of 17 predictors extracted by a random forest algorithm. Nine∼12 months to the event was the period that had the best AUROC in external validation by either geographical (0.88, 95% confidence interval (CI) 0.88–0.89) or temporal split (0.86, 95% CI 0.85–0.86). We compared this model to prediction models in seven studies from 869 records in PUBMED, EMBASE, and SCOPUS. This model outperformed the previous models in terms of the precision, sensitivity, and specificity in all validation sets.

Interpretation

Our low-cost model improved preliminary prediction to decide pregnant women that will be predicted by the models with high specificity and advanced predictors.

Funding

This work was supported by grant no. MOST108-2221-E-038-018 from the Ministry of Science and Technology of Taiwan.

Low plasma levels of decoy receptor 3 (DcR3) in the third trimester of pregnancy with preeclampsia

OBJECTIVE

The pathophysiology of preeclampsia, a major threat during pregnancy characterized by excessive inflammatory status, remains unclear. Decoy receptor 3 (DcR3), a soluble member of the tumor necrosis factor receptor (TNFR) superfamily, is capable of inducing anti-apoptosis via binding with TL1A and anti-inflammation by driving Th2 immune reactions. DcR3 may, therefore, play a role in immune modulation during pregnancy. The purpose of this study is to explore the role of DcR3 in normal and preeclamptic pregnancies.

MATERIALS AND METHODS

Plasma samples from 104 normal pregnant women (26, 42, and 36 in the first, second, and third trimester, respectively) and 10 patients with preeclampsia in the third trimester were collected. Plasma DcR3 levels were determined by using commercial ELISA kits. ANOVA and linear regression analysis were performed to analyze the relationship between gestational age and DcR3 levels. After adjusting for gestational days, the levels of plasma DcR3 in preeclamptic and non-preeclamptic women in the third trimester were compared.

RESULTS

The plasma levels of DcR3 gradually decreased as the gestational days increased during pregnancy (p < 0.05). In the third trimester, pregnant women with preeclampsia had significantly lower plasma DcR3 levels compared to non-preeclamptic women (p < 0.05).

CONCLUSIONS

We found that plasma DcR3 levels gradually decreased as gestation progressed. The levels of plasma DcR3 in preeclamptic women were significantly lower than those of normal pregnant women, suggesting that a potential involvement of DcR3 in normal pregnancy and decreased levels of DcR3 may be related to preeclampsia.