Evidence map›Paper›PMID 38711005›Full record

Observational studyBMC pregnancy and childbirth2024

A first trimester prediction model and nomogram for gestational diabetes mellitus based on maternal clinical risk factors in a resource-poor setting.

Bruno Basil, Izuchukwu Nnachi Mba, Blessing Kenechi Myke-Mbata, Simeon Adelani Adebisi, Efosa Kenneth Oghagbon

Abstract readObservational StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
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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

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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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Bruno BasilDepartment of Chemical Pathology, Benue State University, Makurdi, Nigeria.
Izuchukwu Nnachi MbaDepartment of Chemical Pathology, Nile University of Nigeria, Abuja, Nigeria. mbaizuchukwu@gmail.com.
Blessing Kenechi Myke-MbataDepartment of Chemical Pathology, Benue State University, Makurdi, Nigeria.
Simeon Adelani AdebisiDepartment of Chemical Pathology, Benue State University, Makurdi, Nigeria.
Efosa Kenneth OghagbonDepartment of Chemical Pathology, Benue State University, Makurdi, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe implementation of universal screening for Gestational Diabetes Mellitus (GDM) is challenged by several factors key amongst which is limited resources, hence the continued reliance on risk factor-based screening. Effective identification of high-risk women early in pregnancy may enable preventive intervention. This study aimed at developing a GDM prediction model based on maternal clinical risk factors that are easily assessable in the first trimester of pregnancy in a population of Nigerian women.

methodsThis was a multi-hospital prospective observational cohort study of 253 consecutively selected pregnant women from which maternal clinical data was collected at 8-12 weeks gestational age. Diagnosis of GDM was made via a one-step 75-gram Oral Glucose Tolerance Test (OGTT) at 24-28 weeks of gestation. A GDM prediction model and nomogram based on selected maternal clinical risk factors was developed using multiple logistic regression analysis, and its performance was assessed by Receiver Operator Curve (ROC) analysis. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) version 25 and Python programming language (version 3.0).

resultsIncreasing maternal age, higher body mass index (BMI), a family history of diabetes mellitus in first-degree relative and previous history of foetal macrosomia were the major predictors of GDM. The model equation was: LogitP = 6.358 - 0.066 × Age - 0.075 × First trimester BMI - 1.879 × First-degree relative with diabetes mellitus - 0.522 × History of foetal macrosomia. It had an area under the receiver operator characteristic (ROC) curve (AUC) of 0.814 (95% CI: 0.751-0.877; p-value < 0.001), and at a predicted probability threshold of 0.745, it had a sensitivity of 79.2% and specificity of 74.5%.

conclusionThis first trimester prediction model reliably identifies women at high risk for GDM development in the first trimester, and the nomogram enhances its practical applicability, contributing to improved clinical outcomes in the study population.

Indexed as

Diabetes, GestationalGlucose Tolerance TestNomogramsPregnancy Trimester, FirstAdultBody Mass IndexFemaleFetal MacrosomiaHumansMaternal AgeNigeriaPregnancyProspective StudiesRisk AssessmentRisk FactorsROC CurveBody mass indexFirst trimesterFoetal macrosomiaGestational diabetes mellitusMaternal clinical risk factorsNomogramPrediction model

Identifiers

PMID38711005
PMCPMC11071227

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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.