Evidence map›Paper›PMID 36088319›Full record

ArticleBMC pediatrics2022

Prognostic risk score development to predict birth asphyxia using maternal and fetal characteristics in South Gondar zone hospitals, north West Ethiopia.

Desalegn Tesfa, Sofonyas Abebaw Tiruneh, Melkalem Mamuye Azanaw, Alemayehu Digssie Gebremariam, Melaku Tadege Engidaw, Mulu Tiruneh, Tsion Dessalegn, Melkamu Aderajew Zemene, Ermias Sisay

Open access · goldAbstract read
In one paragraph

Article in BMC pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 20% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. 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

9 authors at 1 institution in 1 country.

Desalegn TesfaDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia. desalegntesfa50@gmail.com.
Sofonyas Abebaw TirunehDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Melkalem Mamuye AzanawDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Alemayehu Digssie GebremariamDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Melaku Tadege EngidawDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Mulu TirunehDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Tsion DessalegnDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Melkamu Aderajew ZemeneDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Ermias SisayDepartment of Pediatrics & Child Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Debre Tabor University · ET

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBirth asphyxia leads to profound systemic and neurological sequela to decrease blood flow or oxygen to the fetus followed by lethal progressive or irreversible life-long pathologies. In low resource setting countries, birth asphyxia remains a critical condition. This study aimed to develop and validate prognostic risk scores to forecast birth asphyxia using maternal and neonatal characteristics in south Gondar zone hospitals.

methodsProspective cohorts of 404 pregnant women were included in the model in south Gondar Zone Hospitals, Northwest Ethiopia. To recognize potential prognostic determinants for birth asphyxia, multivariable logistic regression was applied. The model discrimination probability was checked using the receiver operating characteristic curve (AUROC) and the model calibration plot was assessed using the 'givitiR' R-package. To check the clinical importance of the model, a cost-benefit analysis was done through a decision curve and the model was internally validated using bootstrapping. Lastly, a risk score prediction measurement was established for simple application.

resultsOf 404, 108 (26.73%) (95% CI: 22.6-31.3) newborns were exposed to birth asphyxia during the follow-up time. Premature rupture of membrane, meconium aspiration syndrome, malpresentation, prolonged labor, Preterm, and tight nuchal was the significant prognostic predictors of birth asphyxia. The AUROC curve for birth asphyxia was 88.6% (95% CI: 84.6-92.2%), which indicated that the tool identified the newborns at risk for birth asphyxia very well. The AUROC of the simplified risk score algorithm, was 87.9 (95% CI, 84.0- 91.7%) and the risk score value of 2 was selected as the optimal cut-off value, with a sensitivity of 78.87%, a specificity of 83.26%, a positive predictive value of 63.23%, and a negative predictive value of 91.52%.

conclusionsWe established birth asphyxia prediction tools by applying non-sophisticated maternal and neonatal characteristics for resource scares countries. The driven score has very good discriminative ability and prediction performance. This risk score tool would allow reducing neonatal morbidity and mortality related to birth asphyxia. Consequently, it will improve the overall neonatal health / under-five child health in low-income countries.

Indexed as

Asphyxia NeonatorumMeconium Aspiration SyndromeAsphyxiaChildEthiopiaFemaleFetusHospitalsHumansInfant, NewbornPregnancyPrognosisProspective StudiesRisk FactorsBirth asphyxiaDecision curvePredictionPrognostic risk score

Identifiers

PMID36088319
PMCPMC9463805
OpenAlexW4295503341

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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.