Dataset Information

Risk factors and comorbidities associated with magnesium deficiency in pregnant women and women with hormone-related conditions: analysis of a large real-world dataset.

ABSTRACT:

Background

An accumulating body of literature indicates that magnesium deficiency is associated with a number of hormone-related conditions (HRC) in women, and epidemiological studies are needed to assess its prevalence and risk factors. Here, we present a secondary analysis of data pooled from four large observational studies that assessed magnesium deficiency among pregnant women and women with HRC across the Russian Federation.

Methods

The main objective of this analysis was to estimate the prevalence of magnesium deficiency in this population and to describe risk factors and comorbidities associated with low serum magnesium. Univariate logistic regression analysis was performed to identify the risk factors and comorbid conditions associated with an increased risk of low serum magnesium level.

Results

A total of 983 pregnant women and 9444 women with HRC were eligible for analysis. Prevalence of hypomagnesemia (magnesium serum level cut-off ConclusionsThese results confirm the high prevalence of hypomagnesemia in pregnant women and women with HRC and underline the importance of routine screening, since risk factors are mostly non-specific.

SUBMITTER: Orlova S

PROVIDER: S-EPMC7821493 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Risk factors and comorbidities associated with magnesium deficiency in pregnant women and women with hormone-related conditions: analysis of a large real-world dataset.

Orlova Svetlana S Dikke Galina G Pickering Gisele G Yaltseva Natalya N Konchits Sofya S Starostin Kirill K Bevz Alina A

BMC pregnancy and childbirth 20210122 1

<h4>Background</h4>An accumulating body of literature indicates that magnesium deficiency is associated with a number of hormone-related conditions (HRC) in women, and epidemiological studies are needed to assess its prevalence and risk factors. Here, we present a secondary analysis of data pooled from four large observational studies that assessed magnesium deficiency among pregnant women and women with HRC across the Russian Federation.<h4>Methods</h4>The main objective of this analysis was to ...[more]

PMID: 33482760

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Project description:BackgroundFetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction.MethodsIn the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software.ResultsWe compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model.ConclusionsOur model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.

Dataset Information

Risk factors and comorbidities associated with magnesium deficiency in pregnant women and women with hormone-related conditions: analysis of a large real-world dataset.

Background

Methods

Results

Publications

Risk factors and comorbidities associated with magnesium deficiency in pregnant women and women with hormone-related conditions: analysis of a large real-world dataset.

Similar Datasets

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