How to enhance the applicability of a risk prediction model for term small-for-gestational-age neonates in clinical settings?

Autor: Kong SM; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.; Haizhu District Center for Disease Control and Prevention, Guangzhou, China., Gao C; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China., Yu A; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China., Lin SS; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China., Wei DM; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.; Department of Women's Health, Guangdong Provincial Key Clinical Specialty of Women and Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China., Wang CR; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.; State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China., Lu JH; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.; Department of Women's Health, Guangdong Provincial Key Clinical Specialty of Women and Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China., Zeng DY; Liuzhou Maternity and Child Healthcare Hospital, Affiliated Women and Children's Hospital of Guangxi University of Science and Technology, Liuzhou, China., Zhang J; Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Affiliated with School of Medicine, Shanghai Jiao Tong University, Shanghai, China., He JR; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.; Department of Women's Health, Guangdong Provincial Key Clinical Specialty of Women and Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.; Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China., Qiu X; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.; Department of Women's Health, Guangdong Provincial Key Clinical Specialty of Women and Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.; Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Jazyk: angličtina
Zdroj: International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics [Int J Gynaecol Obstet] 2024 Jun; Vol. 165 (3), pp. 1104-1113. Date of Electronic Publication: 2023 Dec 20.
DOI: 10.1002/ijgo.15268
Abstrakt: Objective: To construct a simple term small-for-gestational-age (SGA) neonate prediction model that is clinically practical.
Methods: This analysis was based on the Born in Guangzhou Cohort Study (BIGCS). Mothers who had a singleton pregnancy, delivered a term neonate, and had an ultrasonography within 30 + 0 to 32 + 6 weeks of gestation were included. Term SGA was defined with customized population percentiles. Prediction models were constructed with backward selection logistic regression in a four-step approach, where model 1 contained fetal biometrics only, models 2 and 3 included maternal features and a time factor (weeks between ultrasonography and delivery), respectively; and model 4 contained all features mentioned. The prediction performance of individual models was evaluated based on area under the curve (AUC) and a calibration test was performed.
Results: The prevalence of SGA in the study population of 21 346 women was 11.5%. With a complete-case analysis approach, data of 19 954 women were used for model construction and validation. The AUC of the four models were 0.781, 0.793, 0.823, and 0.834, respectively, and all were well-calibrated. Model 3 consisted of fetal biometrics and corrected for time to delivery was chosen as the final model to build risk prediction graphs for clinical use.
Conclusion: A prediction model derived from fetal biometrics in early third trimester is satisfactory to predict SGA.
(© 2023 International Federation of Gynecology and Obstetrics.)
Databáze: MEDLINE
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