Prediction of low birth weight from fetal ultrasound and clinical characteristics: a comparative study between a low- and middle-income and a high-income country.

Autor:	Sanchez-Martinez S; Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain sergio.sanchezm@upf.edu.; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain., Marti-Castellote PM; Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain., Hoodbhoy Z; Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan., Bernardino G; Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain., Prats-Valero J; Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain., Aguado AM; Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain., Testa L; BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain., Piella G; Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain., Crovetto F; BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain.; Centre for Biomedical Research on Rare Diseases (CIBER-ER), IDIBAPS, Barcelona, Spain., Snyder C; Cardiology Care for Children, Lancaster, Pennsylvania, USA., Mohsin S; Sindh Institute of Urology and Transplantation, Karachi, Pakistan., Nizar A; Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan., Ahmed R; Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan., Jehan F; Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan., Jenkins K; Children's Hospital Boston, Boston, Massachusetts, USA., Gratacós E; BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain.; Centre for Biomedical Research on Rare Diseases (CIBER-ER), IDIBAPS, Barcelona, Spain.; Institut de Recerca Sant Joan de Deu, Esplugues de Llobregat, Spain., Crispi F; BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain.; Centre for Biomedical Research on Rare Diseases (CIBER-ER), IDIBAPS, Barcelona, Spain., Chowdhury D; Cardiology Care for Children, Lancaster, Pennsylvania, USA., Hasan BS; Sindh Institute of Urology and Transplantation, Karachi, Pakistan., Bijnens B; Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain.; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.; ICREA, Barcelona, Spain.
Jazyk:	angličtina
Zdroj:	BMJ global health [BMJ Glob Health] 2024 Dec 05; Vol. 9 (12). Date of Electronic Publication: 2024 Dec 05.
DOI:	10.1136/bmjgh-2024-016088
Abstrakt:	Introduction: Adverse perinatal outcomes (APO) pose a significant global challenge, particularly in low- and middle-income countries (LMICs). This study aims to analyse two cohorts of high-risk pregnant women for APO to comprehend risk factors and improve prediction accuracy. Methods: We considered an LMIC and a high-income country (HIC) population to derive XGBoost classifiers to predict low birth weight (LBW) from a comprehensive set of maternal and fetal characteristics including socio-demographic, past and current pregnancy information, fetal biometry and fetoplacental Doppler measurements. Data were sourced from the FeDoC (Fetal Doppler Collaborative) study (Pakistan, LMIC) and theIMPACT (Improving Mothers for a Better PrenAtal Care Trial) study (Spain, HIC), and included 520 and 746 pregnancies assessed from 28 weeks gestation, respectively. The models were trained on varying subsets of the mentioned characteristics to evaluate their contribution in predicting LBW cases. For external validation, and to highlight potential differential risk factors for LBW, we investigated the generalisation of these models across cohorts. Models' performance was evaluated through the area under the curve (AUC), and their interpretability was assessed using SHapley Additive exPlanations. Results: In FeDoC, Doppler variables demonstrated the highest value at predicting LBW compared with biometry and maternal clinical data (AUC Doppler , 0.67; AUC Clinical , 0.65; AUC Biometry , 0.63), and its combination with maternal clinical data yielded the best prediction (AUC Clinical+Doppler , 0.71). In IMPACT, fetal biometry emerged as the most predictive set (AUC Biometry , 0.75; AUC Doppler , 0.70; AUC Clinical , 0.69) and its combination with Doppler and maternal clinical data achieved the highest accuracy (AUC Clinical+Biometry+Doppler , 0.81). External validation consistently indicated that biometry combined with Doppler data yielded the best prediction. Conclusions: Our findings provide new insights into the predictive role of different clinical and ultrasound descriptors in two populations at high risk for APO, highlighting that different approaches are required for different populations. However, Doppler data improves prediction capabilities in both settings, underscoring the value of standardising ultrasound data acquisition, as practiced in HIC, to enhance LBW prediction in LMIC. This alignment contributes to bridging the health equity gap. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)
Databáze:	MEDLINE
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