Evidence map›Paper›PMID 37153839›Full record

ArticleCureus2023

Predictors of Neonatal Intensive Care Unit Admission and Adverse Outcomes Related to Gestational Diabetes.

Abdullah M Al-Shahrani

Abstract read
In one paragraph

Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Abdullah M Al-ShahraniFamily Medicine, College of Medicine, University of Bisha, Bisha, SAU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGestational diabetes mellitus (GDM) is a type of diabetes that manifests itself in pregnant women. It poses a significant risk to the mother's health as well as the health of the infant, including more babies being brought to the neonatal intensive care unit (NICU). It puts both the mother's and the child's health at serious risk, increasing the likelihood that newborns may need to be treated in a neonatal critical care unit. This study aimed to determine the factors that predict GDM-related NICU admission and other adverse newborn outcomes.

methodsThe study was a cross-sectional analysis of 175 pregnant women who presented with gestational diabetes at the Maternity and Children's Hospital in Bisha (MCH-Bisha), Saudi Arabia, between January 1 and December 31, 2022. A logistic regression model was used to analyze the data to predict adverse outcomes for newborns and NICU admissions and identify associations between maternal variables and outcomes.

resultsMaternal characteristics highly associated with adverse neonatal outcomes included advanced maternal age (greater than 30 years), a family history of DM, and a history of greater than or equal to four previous pregnancies. Logistic regression models revealed that newborns delivered to mothers older than 30 years were 7.17 times more likely to be admitted to the NICU than newborns born to mothers younger than 30 years. Saudi nationality, urban residence, and cesarean section delivery factors account for nearly all adverse neonatal outcomes (91%, 75%, and 91%, respectively). Newborns delivered by cesarean section were 3.38 times more likely to be admitted to NICU, and the association was significant.

conclusionsMaternal age greater than 30 years and a history of more than or equal to four pregnancies were the strongest indicators of infant adverse outcomes and NICU admittance among women with gestational diabetes. These findings highlight the need for approaches to GDM management that are efficient, thorough, and multidisciplinary.

Indexed as

admissionadverse outcomesgestational diabetesneonatepredictors

Identifiers

PMID37153839
PMCPMC10161799

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.