Evidence map›Paper›PMID 36167381›Full record

ArticleBMJ open2022

Developing and validating a risk prediction model for preterm birth at Felege Hiwot Comprehensive Specialized Hospital, North-West Ethiopia: a retrospective follow-up study.

Sefineh Fenta Feleke, Zelalem Alamrew Anteneh, Gizachew Tadesse Wassie, Anteneh Kassa Yalew, Anteneh Mengist Dessie

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

4 citing papers in PubMed, 10 citations in OpenAlex.

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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 at 4 institutions in 1 country.

Sefineh Fenta FelekeDepartment of Public health, Woldia University, Woldia, Ethiopia fentasefineh21@gmail.com.ORCID 0000-0002-3051-1296
Zelalem Alamrew AntenehDepartment of Epidemiology and Biostatistics, Bahir Dar University College of Medical and Health Sciences, Bahir Dar, Ethiopia.ORCID 0000-0003-4879-675X
Gizachew Tadesse WassieDepartment of Epidemiology and Biostatistics, Bahir Dar University College of Medical and Health Sciences, Bahir Dar, Ethiopia.ORCID 0000-0001-6213-0191
Anteneh Kassa YalewDepartment of Public Health, Wolkite University, Welkite, Ethiopia.
Anteneh Mengist DessieDepartment of Public Health, Debre Tabor University, Debre Tabor, Ethiopia.ORCID 0000-0002-5919-9670
Bahir Dar University · ETDebre Tabor University · ETWoldia University · ETWolkite University · ET

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop and validate a risk prediction model for the prediction of preterm birth using maternal characteristics.

designThis was a retrospective follow-up study. Data were coded and entered into EpiData, V.3.02, and were analysed using R statistical programming language V.4.0.4 for further processing and analysis. Bivariable logistic regression was used to identify the relationship between each predictor and preterm birth. Variables with p≤0.25 from the bivariable analysis were entered into a backward stepwise multivariable logistic regression model, and significant variables (p<0.05) were retained in the multivariable model. Model accuracy and goodness of fit were assessed by computing the area under the receiver operating characteristic curve (discrimination) and calibration plot (calibration), respectively. SETTING AND

participantsThis retrospective study was conducted among 1260 pregnant women who did prenatal care and finally delivered at Felege Hiwot Comprehensive Specialised Hospital, Bahir Dar city, north-west Ethiopia, from 30 January 2019 to 30 January 2021.

resultsResidence, gravidity, haemoglobin <11 mg/dL, early rupture of membranes, antepartum haemorrhage and pregnancy-induced hypertension remained in the final multivariable prediction model. The area under the curve of the model was 0.816 (95% CI 0.779 to 0.856).

conclusionThis study showed the possibility of predicting preterm birth using maternal characteristics during pregnancy. Thus, use of this model could help identify pregnant women at a higher risk of having a preterm birth to be linked to a centre.

Indexed as

Premature BirthEthiopiaFemaleFollow-Up StudiesHospitals, SpecialHumansInfant, NewbornPregnancyRetrospective Studiesobstetricsperinatologypublic health

Identifiers

PMID36167381
PMCPMC9516143
OpenAlexW4297369334

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Registered trials

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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.