Evidence map›Paper›PMID 42439710›Full record

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Diagnostic accuracy of ultrasound-based fetal weight estimation and prediction modeling for low birth weight: A retrospective cohort study in Northwest Ethiopia.

Amsalu Worku Mekonin, Sintayehu Debas, Haile Mekonnen Fenta, Yihun Miskir Wubie, Asres Zegeye, Zelalem Alamrew Anteneh

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6 authors.

Amsalu Worku MekoninDepartment of Obstetrics and Gynecology, College of Medicine and Health Sciences Bahir Dar University, Bahir Dar, Ethiopia.
Sintayehu DebasDepartment of Obstetrics and Gynecology, College of Medicine and Health Sciences Bahir Dar University, Bahir Dar, Ethiopia.
Haile Mekonnen FentaDepartment of Statistics, College of Natural Sciences Bahir Dar University, Bahir Dar, Ethiopia.
Yihun Miskir WubieDepartments of Adult Nursing, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.
Asres ZegeyeSirinka Agricultural Research Center, Amhara Agricultural Research Institute, Bahir Dar, Ethiopia.
Zelalem Alamrew AntenehDepartments of Epidemiology and Biostatistics, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.ORCID 0000-0003-4879-675X

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6 · The paper itself

Abstract

ObjectiveTo evaluate the accuracy of ultrasound-based fetal weight estimation and to develop and internally validate a prediction model for low birth weight.MethodsA retrospective cohort study was conducted among 269 individuals.ResultsThe accuracy of ultrasound weight estimation was 72.9%, with 27.1% of estimates falling outside ±10% of actual birth weight. The Pearson correlation for estimated fetal weight and actual birth weight was 0.92. Using LASSO regression eight predictors were selected and entered in the multivariable analysis of which gestational age, estimated fetal weight, timing of estimation, and placental location were found significant. The original model demonstrated strong discrimination of AUC of 95.9% 95%CI (93.1-98.0%), and good calibration. Internal validation via 1000-bootstrap resampling yielded a bias-corrected AUC of 94.3%, indicating preserved predictive performance with minimal optimism. An integer-based risk score was derived to enhance clinical usability. The risk score model achieved acceptable discrimination (AUC = 76.8%, 95% CI: 70.0-83.0%) and good calibration (p = 0.301). Agreement between the full prediction model and the simplified risk score was fair (Cohen's Kappa = 0.353, p < 0.0001), indicating that the simplified score is best suited as a pragmatic bedside risk-stratification tool rather than a direct substitute for the full model. Women were stratified into low (0-10 points), medium (11-21), and high-risk (22-36) groups corresponding to increasing predicted probabilities of low birth weight (3.2%-96.3%).ConclusionUltrasound-based fetal weight estimation using the Hadlock-III formula indicated acceptable accuracy and strong correlation with actual birth weight. We developed and internally validated prediction model for low birth weight with excellent discrimination, good calibration, and predictive value beyond estimated fetal weight alone. A risk-score tool was created to facilitate rapid bedside risk stratification. However, given the single-center nature of the study and the absence of external validation, further multicenter validation is required before broader clinical implementation.

Indexed as

Fetal WeightInfant, Low Birth WeightUltrasonography, PrenatalAdultBirth WeightEthiopiaFemaleGestational AgeHumansInfant, NewbornPrediction AlgorithmsPregnancyRetrospective Studiesaccuracyestimated fetal weightlow birth weightrisk predictionultrasound

Identifiers

PMID42439710
PMCPMC13457155

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