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Ewington L, Black N, Leeson C, Al Wattar BH, Quenby S. Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review. BJOG 2024. [PMID: 38465451 DOI: 10.1111/1471-0528.17802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/08/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies. OBJECTIVES To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice. SEARCH STRATEGY MEDLINE, EMBASE and Cochrane Library were searched until June 2022. SELECTION CRITERIA We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance. DATA COLLECTION AND ANALYSIS Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool). MAIN RESULTS A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias. CONCLUSIONS There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.
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Affiliation(s)
- Lauren Ewington
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Naomi Black
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Charlotte Leeson
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Bassel H Al Wattar
- Beginnings Assisted Conception Unit, Epsom and St Helier University Hospitals, London, UK
- Comprehensive Clinical Trials Unit, Institute for Clinical Trials and Methodology, University College London, London, UK
| | - Siobhan Quenby
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
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Mennickent D, Rodríguez A, Opazo MC, Riedel CA, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano AE, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front Endocrinol (Lausanne) 2023; 14:1130139. [PMID: 37274341 PMCID: PMC10235786 DOI: 10.3389/fendo.2023.1130139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
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Affiliation(s)
- Daniela Mennickent
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Andrés Rodríguez
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío, Chillán, Chile
| | - Ma. Cecilia Opazo
- Instituto de Ciencias Naturales, Facultad de Medicina Veterinaria y Agronomía, Universidad de Las Américas, Santiago, Chile
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
| | - Claudia A. Riedel
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
- Departamento de Ciencias Biológicas, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
| | - Erica Castro
- Departamento de Obstetricia y Puericultura, Facultad de Ciencias de la Salud, Universidad de Atacama, Copiapó, Chile
| | - Alma Eriz-Salinas
- Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Javiera Appel-Rubio
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Claudio Aguayo
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Alicia E. Damiano
- Cátedra de Biología Celular y Molecular, Departamento de Ciencias Biológicas, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
- Laboratorio de Biología de la Reproducción, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO-Houssay)- CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Enrique Guzmán-Gutiérrez
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Juan Araya
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
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Analytical Comparison of Risk Prediction Models for the Onset of Macrosomia Based on Three Statistical Methods. DISEASE MARKERS 2022; 2022:9073043. [PMID: 36124028 PMCID: PMC9482546 DOI: 10.1155/2022/9073043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/29/2022] [Accepted: 09/01/2022] [Indexed: 11/18/2022]
Abstract
Background and Purpose. Fetal overgrowth can pose a serious threat to the safety of a mother and child. Early identification of high-risk pregnant women and timely pregnancy intervention and guidance are of great value in preventing the development of giant babies and improving adverse maternal and infant outcomes. The current clinical methods for predicting macrosomia mainly rely on obstetric examination and imaging, but their accuracy is controversial. And there is no accepted method for accurately predicting macrosomia. We investigated the risk factors influencing the occurrence of macrosomia and established a prediction model for the occurrence of macrosomia to provide a reference basis for interventions to prevent macrosomia. Method. A retrospective selection of 93 women who were hospitalized in our hospital from March 2019 to May 2022 with a singleton pregnancy and delivered at term with macrosomia were the study group. And 356 women who delivered a normal size baby during the same period were the control group. The variables that were associated with the onset of macrosomia were screened from maternal medical records. Logistic regression models, random forest, and CART decision tree models were developed using the screened variables as input variables and whether they were macrosomia as outcome variables, respectively. The performance of the three models was evaluated by accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC). Result. The risk prediction models for the onset of macrosomia, logistic regression model, random forest model, and decision tree, were successfully developed, with accuracies of 0.904, 1.000, and 0.901 in the training set and 0.926, 0.582, and 0.852 in the validation set, respectively. The AUC in the training set were 0.898, 1.000, and 0.789, and in the validation set were 0.906, 0.913, and 0.731, respectively. In general, the logistic regression model has the highest diagnostic efficiency, followed by the random forest model. Conclusion. Logistic regression models have high application value in the assessment of predicting the risk of macrosomia, and it is suggested that the advantages of logistic regression models and random forest models should be combined in future studies and applications to make them work better in the prediction of the risk of macrosomia.
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Jing G, Huwei S, Chao C, Lei C, Ping W, Zhongzhou X, Sen Y, Jiayuan C, Ruiyao C, Lu L, Shuqing L, Kaixiang Y, Jie X, Weiwei C. A predictive model of macrosomic birth based upon real-world clinical data from pregnant women. BMC Pregnancy Childbirth 2022; 22:651. [PMID: 35982421 PMCID: PMC9386989 DOI: 10.1186/s12884-022-04981-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction. METHODS In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software. RESULTS We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model. CONCLUSIONS Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.
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Affiliation(s)
- Gao Jing
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, 200040, China.,Shanghai Municipal Key Clinical Specialty, Shanghai, 200030, China
| | - Shi Huwei
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Chen Chao
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, 200040, China.,Shanghai Municipal Key Clinical Specialty, Shanghai, 200030, China
| | - Chen Lei
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China
| | - Wang Ping
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China
| | - Xiao Zhongzhou
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Yang Sen
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Chen Jiayuan
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Chen Ruiyao
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Lu Lu
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Luo Shuqing
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Yang Kaixiang
- The Second Affiliated Hospital of Nanjing Medical University, Nanjing, 210003, Jiangsu, China
| | - Xu Jie
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China.
| | - Cheng Weiwei
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China. .,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, 200040, China. .,Shanghai Municipal Key Clinical Specialty, Shanghai, 200030, China.
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Du J, Zhang X, Chai S, Zhao X, Sun J, Yuan N, Yu X, Zhang Q. Nomogram-based risk prediction of macrosomia: a case-control study. BMC Pregnancy Childbirth 2022; 22:392. [PMID: 35513792 PMCID: PMC9074352 DOI: 10.1186/s12884-022-04706-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 04/22/2022] [Indexed: 12/20/2022] Open
Abstract
Background Macrosomia is closely associated with poor maternal and fetal outcome. But there is short of studies on the risk of macrosomia in early pregnancy. The purpose of this study is to establish a nomogram for predicting macrosomia in the first trimester. Methods A case-control study involving 1549 pregnant women was performed. According to the birth weight of newborn, the subjects were divided into macrosomia group and non-macrosomia group. The risk factors for macrosomia in early pregnancy were analyzed by multivariate logistic regression. A nomogram was used to predict the risk of macrosomia. Results The prevalence of macrosomia was 6.13% (95/1549) in our hospital. Multivariate logistic regression analysis showed that prepregnancy overweight (OR: 2.13 95% CI: 1.18–3.83)/obesity (OR: 3.54, 95% CI: 1.56–8.04), multiparity (OR:1.88, 95% CI: 1.16–3.04), the history of macrosomia (OR: 36.97, 95% CI: 19.90–68.67), the history of GDM/DM (OR: 2.29, 95% CI: 1.31–3.98), the high levels of HbA1c (OR: 1.76, 95% CI: 1.00–3.10) and TC (OR: 1.36, 95% CI: 1.00–1.84) in the first trimester were the risk factors of macrosomia. The area under ROC (the receiver operating characteristic) curve of the nomogram model was 0.807 (95% CI: 0.755–0.859). The sensitivity and specificity of the model were 0.716 and 0.777, respectively. Conclusion The nomogram model provides an effective mothed for clinicians to predict macrosomia in the first trimester.
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Affiliation(s)
- Jing Du
- Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China
| | - Xiaomei Zhang
- Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China.
| | - Sanbao Chai
- Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China
| | - Xin Zhao
- Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China
| | - Jianbin Sun
- Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China
| | - Ning Yuan
- Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China
| | - Xiaofeng Yu
- Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China
| | - Qiaoling Zhang
- Department of Endocrinology and metabolism, Peking University International Hospital, No. 1 Life Garden Road Zhongguancun Life Science Garden Changping District, Beijing, 102206, China
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Effective Macrosomia Prediction Using Random Forest Algorithm. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063245. [PMID: 35328934 PMCID: PMC8951305 DOI: 10.3390/ijerph19063245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/06/2022] [Accepted: 03/07/2022] [Indexed: 02/01/2023]
Abstract
(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.
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Bertini A, Salas R, Chabert S, Sobrevia L, Pardo F. Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review. Front Bioeng Biotechnol 2022; 9:780389. [PMID: 35127665 PMCID: PMC8807522 DOI: 10.3389/fbioe.2021.780389] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/10/2021] [Indexed: 12/11/2022] Open
Abstract
Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications.Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications.Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method.Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy.Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.
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Affiliation(s)
- Ayleen Bertini
- Metabolic Diseases Research Laboratory (MDRL), Interdisciplinary Center for Research in Territorial Health of the Aconcagua Valley (CIISTe Aconcagua), Center for Biomedical Research (CIB), Universidad de Valparaíso, Valparaiso, Chile
- PhD Program Doctorado en Ciencias e Ingeniería para La Salud, Faculty of Medicine, Universidad de Valparaíso, Valparaiso, Chile
| | - Rodrigo Salas
- School of Biomedical Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaiso, Chile
- Centro de Investigación y Desarrollo en INGeniería en Salud – CINGS, Universidad de Valparaíso, Valparaiso, Chile
- Instituto Milenio Intelligent Healthcare Engineering, Valparaíso, Chile
| | - Steren Chabert
- School of Biomedical Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaiso, Chile
- Centro de Investigación y Desarrollo en INGeniería en Salud – CINGS, Universidad de Valparaíso, Valparaiso, Chile
- Instituto Milenio Intelligent Healthcare Engineering, Valparaíso, Chile
| | - Luis Sobrevia
- Cellular and Molecular Physiology Laboratory (CMPL), Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Physiology, Faculty of Pharmacy, Universidad de Sevilla, Seville, Spain
- University of Queensland Centre for Clinical Research (UQCCR), Faculty of Medicine and Biomedical Sciences, University of Queensland, Herston, QLD, Australia
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Medical School (Faculty of Medicine), São Paulo State University (UNESP), São Paulo, Brazil
- Tecnologico de Monterrey, Eutra, The Institute for Obesity Research, School of Medicine and Health Sciences, Monterrey, Mexico
| | - Fabián Pardo
- Metabolic Diseases Research Laboratory (MDRL), Interdisciplinary Center for Research in Territorial Health of the Aconcagua Valley (CIISTe Aconcagua), Center for Biomedical Research (CIB), Universidad de Valparaíso, Valparaiso, Chile
- Cellular and Molecular Physiology Laboratory (CMPL), Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- School of Medicine, Campus San Felipe, Faculty of Medicine, Universidad de Valparaíso, San Felipe, Chile
- *Correspondence: Fabián Pardo,
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Wang Y, Liu H, Wang J, Hu X, Wang A, Nie Z, Xu H, Li J, Xin H, Zhang J, Zhang H, Wang Y, Lyu Z. Development and validation of a new predictive model for macrosomia at late-term pregnancy: A prospective study. Front Endocrinol (Lausanne) 2022; 13:1019234. [PMID: 36465629 PMCID: PMC9713232 DOI: 10.3389/fendo.2022.1019234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/01/2022] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE Fetal macrosomia is defined as a birth weight more than 4,000 g and is associated with maternal and fetal complications. This early metabolic disease may influence the entire life of the infant. Currently, macrosomia is predicted by using the estimated fetal weight (EFW). However, the EFW is inaccurate when the gestational week is gradually increasing. To assess precisely the risk of macrosomia, we developed a new predictive model to estimate the risk of macrosomia. METHODS We continuously collected data on 655 subjects who attended regular antenatal visits and delivered at the Second Hospital of Hebei Medical University (Shijiazhuang, China) from November 2020 to September 2021. A total of 17 maternal features and 2 fetal ultrasonographic features were included at late-term pregnancy. The 655 subjects were divided into a model training set and an internal validation set. Then, 450 pregnant women were recruited from Handan Central Hospital (Handan, China) from November 2021 to March 2022 as the external validation set. The least absolute shrinkage and selection operator method was used to select the most appropriate predictive features and optimize them via 10-fold cross-validation. The multivariate logistical regressions were used to build the predictive model. Receiver operating characteristic (ROC) curves, C-indices, and calibration plots were obtained to assess model discrimination and accuracy. The model's clinical utility was evaluated via decision curve analysis (DCA). RESULTS Four predictors were finally included to develop this new model: prepregnancy obesity (prepregnancy body mass index ≥ 30 kg/m2), hypertriglyceridemia, gestational diabetes mellitus, and fetal abdominal circumference. This model afforded moderate predictive power [area under the ROC curve 0.788 (95% confidence interval [CI] 0.736, 0.840) for the training set, 0.819 (95% CI 0.744,0.894) for the internal validation set, and 0.773 (95% CI 0.713,0.833) for the external validation set]. On DCA, the model evidenced a good fit with, and positive net benefits for, both the internal and external validation sets. CONCLUSIONS We developed a predictive model for macrosomia and performed external validation in other regions to further prove the discrimination and accuracy of this predictive model. This novel model will aid clinicians in easily identifying those at high risk of macrosomia and assist obstetricians to plan accordingly.
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Affiliation(s)
- Yuhan Wang
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Hongzhou Liu
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Department of Endocrinology, First Hospital of Handan City, Handan, Hebei, China
| | - Jincheng Wang
- Department of Epidemiology, The George Washington University, Washington, DC, United States
| | - Xiaodong Hu
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Anning Wang
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Zhimei Nie
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Huaijin Xu
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Jiefei Li
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Hong Xin
- Department of Obstetrics, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jiamei Zhang
- Department of Ultrasound Diagnosis, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Han Zhang
- Department of Ultrasound Diagnosis, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yueheng Wang
- Department of Ultrasound Diagnosis, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zhaohui Lyu
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Zhaohui Lyu,
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Harvey L, van Elburg R, van der Beek EM. Macrosomia and large for gestational age in Asia: One size does not fit all. J Obstet Gynaecol Res 2021; 47:1929-1945. [PMID: 34111907 DOI: 10.1111/jog.14787] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 02/05/2021] [Accepted: 03/22/2021] [Indexed: 12/17/2022]
Abstract
Macrosomia, usually defined as infant birth weight of ≥4000 g, does not consider gestational age, sex, or country/region-specific differences in mean birth weight and maternal body weight. This issue is particularly relevant for Asia, where 60% of the world's population lives, due to variations in maternal size and birth weights across populations. Large for gestational age (LGA), defined as birth weight > 90th centile, is a more sensitive measure as it considers gestational age and sex, though it is dependent on the choice of growth charts. We aimed to review reporting of macrosomia and LGA in Asia. We reviewed the literature on prevalence and risk of macrosomia and LGA in Asia over the last 29 years. Prevalence of macrosomia ranged from 0.5% (India) to 13.9% (China) while prevalence of LGA ranged from 4.3% (Korea) to 22.1% (China), indicating substantial variation in prevalence within and between Asian countries. High pre-pregnancy body mass index, excessive gestational weight gain, and impaired glucose tolerance conferred risk of macrosomia/LGA. Incidence of macrosomia and LGA varies substantially within and between Asian countries, as do the growth charts and definitions. The latter makes it impossible to make comparisons but suggests differences in intrauterine growth between populations. Reporting LGA, using standardized country/regional growth charts, would better capture the incidence of high birth weight and allow for comparison and identification of contributing factors. Better understanding of local drivers of excessive intrauterine growth could enable development of improved strategies for prevention and management of LGA.
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Affiliation(s)
- Louise Harvey
- Nutricia Research, Danone Nutricia Research, Utrecht, The Netherlands
| | - Ruurd van Elburg
- Department of Pediatrics, Emma Children's Hospital, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Eline M van der Beek
- Department of Pediatrics, University Medical Centre Groningen, Groningen, The Netherlands
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