Association of preterm birth with medications: machine learning analysis using national health insurance data

Gastroesophageal Reflux Disease and Preterm Birth: Univariate and Multivariate Mendelian Randomization

Background

Observational studies have established a connection between Gastroesophageal reflux disease (GERD) and preterm birth (PTB). Nevertheless, these correlations can be affected by residual confounding or reverse causality, resulting in ambiguity regarding the connection. The objective of this study was to assess the relationship between genetically predicted GERD and PTB.

Methods

Initially, we performed bidirectional univariate Mendelian randomization (UVMR) analysis utilizing publicly accessible genome-wide association studies (GWAS) data. The primary analytical approach employed to determine the causal impact between GERD and PTB is the inverse variance weighted technique (IVW). Subsequently, we utilized multivariate Mendelian randomization (MVMR) to adjust for potential factors that could influence the results, such as body mass index (BMI), maternal smoking around birth, educational attainment, household income, and Townsend deprivation index (TDI). Furthermore, we performed a sequence of comprehensive sensitivity analyses to assess the reliability of our MR findings.

Results

The UVMR analysis results showed a significant correlation between GERD and PTB (odds ratio [OR]: 1.810; 95% confidence interval [CI]: 1.344-2.439; P=9.60E-05) in the IVW model, and the Weighted median method (OR=1.591, 95% CI=1.094-2.315, P=0.015) revealed consistent results. The inverse MR findings suggest no causal link between PTB and the incidence of GERD. In addition, the sensitivity analysis did not detect heterogeneity or horizontal pleiotropy, and the "leave-one-out" examination confirmed that the causal estimation is unlikely to be influenced by the single nucleotide polymorphisms (SNPs) effect. The MVMR analysis demonstrated that the causal association between GERD and PTB still existed after considering BMI, maternal smoking around birth, educational attainment, household income, and TDI (OR=1.921, 95% CI=1.401-2.634, P=5.08E-05).

Conclusion

This study presents evidence indicating that genetically predicted GERD can heighten the risk of PTB. Therefore, it is advisable to perform focused screening for pregnant women with GERD in order to find the initial signs of PTB and promptly apply intervention strategies to extend the duration of pregnancy.

Enhanced Runge-Kutta-driven feature selection model for early detection of gastroesophageal reflux disease

Gastroesophageal reflux disease (GERD) profoundly compromises the quality of life, with prolonged untreated cases posing a heightened risk of severe complications such as esophageal injury and esophageal carcinoma. The imperative for early diagnosis is paramount in averting progressive pathological developments. This study introduces a wrapper-based feature selection model based on the enhanced Runge Kutta algorithm (SCCRUN) and fuzzy k-nearest neighbors (FKNN) for GERD prediction, named bSCCRUN-FKNN-FS. Runge Kutta algorithm (RUN) is a metaheuristic algorithm designed based on the Runge-Kutta method. However, RUN's effectiveness in local search capabilities is insufficient, and it exhibits insufficient convergence accuracy. To enhance the convergence accuracy of RUN, spiraling communication and collaboration (SCC) is introduced. By facilitating information exchange among population individuals, SCC expands the solution search space, thereby improving convergence accuracy. The optimization capabilities of SCCRUN are experimentally validated through comparisons with classical and state-of-the-art algorithms on the IEEE CEC 2017 benchmark. Subsequently, based on SCCRUN, the bSCCRUN-FKNN-FS model is proposed. During the period from 2019 to 2023, a dataset comprising 179 cases of GERD, including 110 GERD patients and 69 healthy individuals, was collected from Zhejiang Provincial People's Hospital. This dataset was utilized to compare our proposed model against similar algorithms in order to evaluate its performance. Concurrently, it was determined that features such as the internal diameter of the esophageal hiatus during distention, esophagogastric junction diameter during distention, and external diameter of the esophageal hiatus during non-distention play crucial roles in influencing GERD prediction. Experimental findings demonstrate the outstanding performance of the proposed model, with a predictive accuracy reaching as high as 93.824 %. These results underscore the significant advantage of the proposed model in both identifying and predicting GERD patients.

Identifying pregnancy episodes and estimating the last menstrual period using an administrative database in Korea: an application to patients with systemic lupus erythematosus

OBJECTIVES

This study developed an algorithm for identifying pregnancy episodes and estimating the last menstrual period (LMP) in an administrative claims database and applied it to investigate the use of pregnancy-incompatible immunosuppressants among pregnant women with systemic lupus erythematosus (SLE).

METHODS

An algorithm was developed and applied to a nationwide claims database in Korea. Pregnancy episodes were identified using a hierarchy of pregnancy outcomes and clinically plausible periods for subsequent episodes. The LMP was estimated using preterm delivery, sonography, and abortion procedure codes. Otherwise, outcome-specific estimates were applied, assigning a fixed gestational age to the corresponding pregnancy outcome. The algorithm was used to examine the prevalence of pregnancies and utilization of pregnancy-incompatible immunosuppressants (cyclophosphamide [CYC]/mycophenolate mofetil [MMF]/methotrexate [MTX]) and non-steroidal anti-inflammatory drugs (NSAIDs) during pregnancy in SLE patients.

RESULTS

The pregnancy outcomes identified in SLE patients included live births (67%), stillbirths (2%), and abortions (31%). The LMP was mostly estimated with outcome-specific estimates for full-term births (92.3%) and using sonography procedure codes (54.7%) and preterm delivery diagnosis codes (37.9%) for preterm births. The use of CYC/MMF/MTX decreased from 7.6% during preconception to 0.2% at the end of pregnancy. CYC/MMF/MTX use was observed in 3.6% of women within 3 months preconception and 2.5% during 0-7 weeks of pregnancy.

CONCLUSIONS

This study presents the first pregnancy algorithm using a Korean administrative claims database. Although further validation is necessary, this study provides a foundation for evaluating the safety of medications during pregnancy using secondary databases in Korea, especially for rare diseases.

Exploring the use of ChatGPT in OBGYN: a bibliometric analysis of the first ChatGPT-related publications

PURPOSE

Little is known about the scientific literature regarding the new revolutionary tool, ChatGPT. We aim to perform a bibliometric analysis to identify ChatGPT-related publications in obstetrics and gynecology (OBGYN).

STUDY DESIGN

A bibliometric study through PubMed database. We mined all ChatGPT-related publications using the search term "ChatGPT". Bibliometric data were obtained from the iCite database. We performed a descriptive analysis. We further compared IF among publications describing a study vs. other publications.

RESULTS

Overall, 42 ChatGPT-related publications were published across 26 different journals during 69 days. Most publications were editorials (52%) and news/briefing (22%), with only one (2%) research article identified. Five (12%) publications described a study performed. No ChatGPT-related publications in OBGYN were found. The leading journal by the number of publications was Nature (24%), followed by Lancet Digital Health and Radiology (7%, for both). The main subjects of publications were ChatGPT's scientific writing quality (26%) and a description of ChatGPT (26%) followed by tested performance of ChatGPT (14%), authorship and ethical issues (10% for both topics).In a comparison of publications describing a study performed (n = 5) vs. other publications (n = 37), mean IF was lower in the study-publications (mean 6.25 ± 0 vs. 25.4 ± 21.6, p < .001).

CONCLUSIONS

The study highlights main trends in ChatGPT-related publications. OBGYN is yet to be represented in this literature.

Association between early preterm birth and maternal exposure to fine particular matter (PM10): A nation-wide population-based cohort study using machine learning

Although preterm birth (PTB), a birth before 34 weeks of gestation accounts for only less than 3% of total births, it is a critical cause of various perinatal morbidity and mortality. Several studies have been conducted on the association between maternal exposure to PM and PTB, but the results were inconsistent. Moreover, no study has analyzed the risk of PM on PTB among women with cardiovascular diseases, even though those were thought to be highly susceptible to PM considering the cardiovascular effect of PM. Therefore, we aimed to evaluate the effect of PM10 on early PTB according to the period of exposure, using machine learning with data from Korea National Health Insurance Service (KNHI) claims. Furthermore, we conducted subgroup analysis to compare the risk of PM on early PTB among pregnant women with cardiovascular diseases and those without. A total of 149,643 primiparous singleton women aged 25 to 40 years who delivered babies in 2017 were included. Random forest feature importance and SHAP (Shapley additive explanations) value were used to identify the effect of PM10 on early PTB in comparison with other well-known contributing factors of PTB. AUC and accuracy of PTB prediction model using random forest were 0.9988 and 0.9984, respectively. Maternal exposure to PM10 was one of the major predictors of early PTB. PM10 concentration of 5 to 7 months before delivery, the first and early second trimester of pregnancy, ranked high in feature importance. SHAP value showed that higher PM10 concentrations before 5 to 7 months before delivery were associated with an increased risk of early PTB. The probability of early PTB was increased by 7.73%, 10.58%, or 11.11% if a variable PM10 concentration of 5, 6, or 7 months before delivery was included to the prediction model. Furthermore, women with cardiovascular diseases were more susceptible to PM10 concentration in terms of risk for early PTB than those without cardiovascular diseases. Maternal exposure to PM10 has a strong association with early PTB. In addition, in the context of PTB, pregnant women with cardiovascular diseases are a high-risk group of PM10 and the first and early second trimester is a high-risk period of PM10.

Associations of Preterm Birth with Dental and Gastrointestinal Diseases: Machine Learning Analysis Using National Health Insurance Data

BACKGROUND

This study uses machine learning with large-scale population data to assess the associations of preterm birth (PTB) with dental and gastrointestinal diseases.

METHODS

Population-based retrospective cohort data came from Korea National Health Insurance claims for 124,606 primiparous women aged 25-40 and delivered in 2017. The 186 independent variables included demographic/socioeconomic determinants, disease information, and medication history. Machine learning analysis was used to establish the prediction model of PTB. Random forest variable importance was used for identifying major determinants of PTB and testing its associations with dental and gastrointestinal diseases, medication history, and socioeconomic status.

RESULTS

The random forest with oversampling data registered an accuracy of 84.03, and the areas under the receiver-operating-characteristic curves with the range of 84.03-84.04. Based on random forest variable importance with oversampling data, PTB has strong associations with socioeconomic status (0.284), age (0.214), year 2014 gastroesophageal reflux disease (GERD) (0.026), year 2015 GERD (0.026), year 2013 GERD (0.024), progesterone (0.024), year 2012 GERD (0.023), year 2011 GERD (0.021), tricyclic antidepressant (0.020) and year 2016 infertility (0.019). For example, the accuracy of the model will decrease by 28.4%, 2.6%, or 1.9% if the values of socioeconomic status, year 2014 GERD, or year 2016 infertility are randomly permutated (or shuffled).

CONCLUSION

By using machine learning, we established a valid prediction model for PTB. PTB has strong associations with GERD and infertility. Pregnant women need close surveillance for gastrointestinal and obstetric risks at the same time.

Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database

BACKGROUND

Maternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB.

METHODS

A population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25-40 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy.

RESULTS

Among the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value.

CONCLUSIONS

Careful evaluation and management of maternal heart disease during pregnancy would help reduce PTB. Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD.

Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were "gastrointestinal" (title) together with "random forest" or "explainable artificial intelligence" (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70-0.98 for accuracy, 0.04-0.25 for sensitivity, and 0.54-0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID.

A Case of Recent Liver Injury Induced by Benzbromarone

A 39-year-old male had a stomachache for 10 days before abnormal liver function tests were detected by a local doctor. Then, he was referred to us and admitted to our hospital for examination and treatment of elevated transaminases. He had taken benzbromarone to treat his hyperuricemia for seven months, and we diagnosed him with benzbromarone-induced liver injury. After the termination of benzbromarone, he finally recovered from his illness. There are several reports about benzbromarone-induced liver injury. In conclusion, as periodic liver function tests seem not to be completely performed, clinicians should regularly monitor liver function tests in patients taking benzbromarone.