A machine learning approach to investigate potential risk factors for gastroschisis in California

Genitourinary infection and gastroschisis: A systematic review and meta-analysis

BACKGROUND

Gastroschisis is a congenital anomaly of the umbilical ring with increasing prevalence, especially amongst younger mothers. There is increasing evidence that exposure to genitourinary infections (GUTI) may play an important role in the etiology of gastroschisis. This systematic review and meta-analysis aimed to identify, appraise, and summarize the literature on exposure to GUTI and gastroschisis.

METHODS

Six electronic databases (MEDLINE, EMBASE, Web of Science, Scopus, Cochrane Library electronic databases, and Prospero) were searched using a comprehensive search strategy. Citations and cited articles for all included studies were searched. Peer-reviewed, quantitative studies reporting an association of urinary tract infections (UTI) and/or sexually transmitted infections (STI) with gastroschisis were included. Prospero registration CRD42022377420.

RESULTS

A total of 2392 papers were identified via the searches of which 15 met our inclusion criteria and were included after title and abstract and full text screening. The study period for included studies ranged from 1995 to 2016, most were from the USA. Four studies considering exposure to STIs and five to UTIs were eligible to progress to meta-analysis. Meta-analysis identified a significantly increased risk of gastroschisis in association with periconceptional exposure to UTI [OR 1.54 (95% CI 1.29, 1.8)], STI [OR 1.4 (95% CI 1.01, 1.79)].

CONCLUSIONS

Periconceptional exposure to GUTI is associated with an increased risk of gastroschisis. The prevention and timely treatment of GUTI amongst women of childbearing age may help to reduce the occurrence of gastroschisis.

Gastroschisis: embriology, pathogenesis, risk factors, prognosis, and ultrasonographic markers for adverse neonatal outcomes

Gastroschisis is the most common congenital defect of the abdominal wall, typically located to the right of the umbilical cord, through which the intestinal loops and viscera exit without being covered by the amniotic membrane. Despite the known risk factors for gastroschisis, there is no consensus on the cause of this malformation. Prenatal ultrasound is useful for diagnosis, prognostic prediction (ultrasonographic markers) and appropriate monitoring of fetal vitality. Survival rate of children with gastroschisis is more than 95% in developed countries; however, complex gastroschisis requires multiple neonatal interventions and is associated with adverse perinatal outcomes. In this article, we conducted a narrative review including embryology, pathogenesis, risk factors, and ultrasonographic markers for adverse neonatal outcomes in fetuses with gastroschisis. Prenatal risk stratification of gastroschisis helps to better counsel parents, predict complications, and prepare the multidisciplinary team to intervene appropriately and improve postnatal outcomes.

Maternal periconceptional alcohol consumption and gastroschisis in the National Birth Defects Prevention Study, 1997-2011

BACKGROUND

Gastroschisis is particularly prevalent among offspring of young women and has increased over recent decades. Although previous studies suggest that maternal alcohol consumption is associated with increased gastroschisis risk, none have explored whether maternal age modifies that association.

OBJECTIVE

The objective of the study was to evaluate associations between self-reported maternal periconceptional alcohol consumption (1 month prior through the third month after conception) and risk of gastroschisis among offspring, by maternal age.

METHODS

We used data from the National Birth Defects Prevention Study (NBDPS), a multi-site population-based case-control study. The analysis included 1450 gastroschisis cases and 11,829 unaffected liveborn controls delivered during 1997-2011 in ten US states. We estimated adjusted odds ratios (aOR) and 95% confidence intervals (CI) for the individual and joint effects of alcohol consumption and young maternal age at delivery (<25 years vs ≥25 years) on gastroschisis risk. We estimated the relative excess risk due to interaction (RERI) to quantify additive interaction.

RESULTS

Periconceptional alcohol consumption was common regardless of maternal age (women <25 years: cases 38.8%, controls 29.3%; women ≥25: cases 43.5%, controls 39.5%). Compared with women ≥25 years who did not consume alcohol, we observed increased risk of gastroschisis among women <25 years, with higher estimates among those who consumed alcohol (women <25 years who did not consume alcohol. aOR 5.90, 95% CI 4.89, 7.11; women <25 years who did consume alcohol: aOR 8.21, 95% CI 6.69, 10.07). Alcohol consumption among women ≥25 years was not associated with gastroschisis (aOR 1.12, 95% CI 0.88, 1.42). This suggests super-additive interaction between alcohol consumption and maternal age (RERI -2.19, 95% CI 1.02, 3.36).

CONCLUSIONS

Periconceptional alcohol consumption may disproportionately increase risk of gastroschisis among young mothers. Our findings support public health recommendations to abstain from alcohol consumption during pregnancy.

Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review

Background

The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others.

Objective

The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals.

Methods

The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process.

Results

The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals.

Conclusions

In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.

Genetic Susceptibility to Drug Teratogenicity: A Systematic Literature Review

Since the 1960s, drugs have been known to cause teratogenic effects in humans. Such teratogenicity has been postulated to be influenced by genetics. The aim of this review was to provide an overview of the current knowledge on genetic susceptibility to drug teratogenicity in humans and reflect on future directions within the field of genetic teratology. We focused on 12 drugs and drug classes with evidence of teratogenic action, as well as 29 drugs and drug classes with conflicting evidence of fetal safety in humans. An extensive literature search was performed in the PubMed and EMBASE databases using terms related to the drugs of interest, congenital anomalies and fetal development abnormalities, and genetic variation and susceptibility. A total of 29 studies were included in the final data extraction. The eligible studies were published between 1999 and 2020 in 10 different countries, and comprised 28 candidate gene and 1 whole-exome sequencing studies. The sample sizes ranged from 20 to 9,774 individuals. Several drugs were investigated, including antidepressants (nine studies), thalidomide (seven studies), antiepileptic drugs (five studies), glucocorticoids (four studies), acetaminophen (two studies), and sex hormones (estrogens, one study; 17-alpha hydroxyprogesterone caproate, one study). The main neonatal phenotypic outcomes included perinatal complications, cardiovascular congenital anomalies, and neurodevelopmental outcomes. The review demonstrated that studies on genetic teratology are generally small, heterogeneous, and exhibit inconsistent results. The most convincing findings were genetic variants in SLC6A4, MTHFR, and NR3C1, which were associated with drug teratogenicity by antidepressants, antiepileptics, and glucocorticoids, respectively. Notably, this review demonstrated the large knowledge gap regarding genetic susceptibility to drug teratogenicity, emphasizing the need for further efforts in the field. Future studies may be improved by increasing the sample size and applying genome-wide approaches to promote the interpretation of results. Such studies could support the clinical implementation of genetic screening to provide safer drug use in pregnant women in need of drugs.

Three machine learning algorithms and their utility in exploring risk factors associated with primary cesarean section in low-risk women: A methods paper

Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables' contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. To provide an example of the different algorithms' function, they were used on a completed cross-sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.