Prediction models of small for gestational age based on machine learning: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

YANG Xiao ¹ , FU JianLin ² ,  ZHOU HuiLan ¹

1. Department of Critical Care Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong 637500, P. R. China;
2. School of Public Health, Chongqing Medical University, Chongqing 401331, P. R. China;

Corresponding author：

ZHOU HuiLan, Email: 472260181@qq.com

Keywords：

Small for gestational age; Prediction model; Machine learning; Systematic review; Predictive performance

DOI：

10.7507/1672-2531.202210001

Video：

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Objective To systematically review prediction models of small for gestational age (SGA) based on machine learning and provide references for the construction and optimization of such a prediction model. Methods The PubMed, EMbase, Web of Science, CBM, WanFang Data, VIP and CNKI databases were electronically searched to collect studies on SGA prediction models from database inception to August 10, 2022. Two researchers independently screened the literature, extracted data, evaluated the risk of bias of the included studies, and conducted a systematic review. Results A total of 14 studies, comprising 40 prediction models constructed using 19 methods, such as logical regression and random forest, were included. The results of the risk of bias assessment from 13 studies were high; the area under the curve of the prediction models ranged from 0.561 to 0.953. Conclusion The overall risk of bias in the prediction models for SGA was high, and the predictive performance was average. Models built using extreme gradient boosting (XGBoost) demonstrated the best predictive performance across different studies. The stacking method can improve predictive performance by integrating different models. Finally, maternal blood pressure, fetal abdominal circumference, head circumference, and estimated fetal weight were important predictors of SGA.

Citation： YANG Xiao, FU JianLin, ZHOU HuiLan. Prediction models of small for gestational age based on machine learning: a systematic review. Chinese Journal of Evidence-Based Medicine, 2023, 23(3): 334-340. doi: 10.7507/1672-2531.202210001 Copy

1.	The American College of Obstetricians and Gynecologists. ACOG Practice bulletin no. 134: fetal growth restriction. Obstet Gynecol, 2013, 121(5): 1122-1133.
2.	Ding G, Tian Y, Zhang Y, et al. Application of a global reference for fetal-weight and birthweight percentiles in predicting infant mortality. BJOG, 2013, 120(13): 1613-1621.
3.	龚海红, 张丽娜, 凌岚. 重组人生长激素治疗小于胎龄儿持续矮小的疗效观察. 南京医科大学学报(自然科学版), 2013, 33(2): 263-264.
4.	Cianfarani S, Ladaki C, Geremia C. Hormonal regulation of postnatal growth in children born small for gestational age. Horm Res, 2006, 65(Suppl 3): 70-74.
5.	Chen HY, Chauhan SP, Ward TC, et al. Aberrant fetal growth and early, late, and postneonatal mortality: an analysis of Milwaukee births, 1996-2007. Am J Obstet Gynecol, 2011, 204(3): 261.e1-261.e10.
6.	刘璐. 基于机器学习的小于胎龄儿预测模型的研究. 北京: 北京工业大学, 2017.
7.	Levin S, Toerper M, Hamrock E, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Ann Emerg Med, 2018, 71(5): 565-574.
8.	刘慧婷. 小儿胎龄儿危险因素及其预测模型的研究. 北京: 北京协和医学院, 2015.
9.	崔建伟, 赵哲, 杜小勇. 支撑机器学习的数据管理技术综述. 软件学报, 2021, 32(3): 604-621.
10.	陈茹, 王胜锋, 周家琛, 等. 预测模型研究的偏倚风险和适用性评估工具解读. 中华流行病学杂志, 2020, 41(5): 776-781.
11.	梁思远. 基于深度学习的小于胎龄儿疾病预测方法研究. 北京: 北京工业大学, 2018.
12.	McCowan LM, Thompson JM, Taylor RS, et al. Clinical prediction in early pregnancy of infants small for gestational age by customised birthweight centiles: findings from a healthy nulliparous cohort. PLoS One, 2013, 8(8): e70917.
13.	McCowan LM, Thompson JM, Taylor RS, et al. Prediction of small for gestational age infants in healthy nulliparous women using clinical and ultrasound risk factors combined with early pregnancy biomarkers. PLoS One, 2017, 12(1): e0169311.
14.	Saw SN, Biswas A, Mattar CNZ, et al. Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor. Prenat Diagn, 2021, 41(4): 505-516.
15.	Bai X, Zhou Z, Luo Y, et al. Development and evaluation of a machine learning prediction model for small-for-gestational-age births in women exposed to radiation before pregnancy. J Pers Med, 2022, 12(4): 550.
16.	Vicoveanu P, Vasilache IA, Scripcariu IS, et al. Use of a feed-forward back propagation network for the prediction of small for gestational age newborns in a cohort of pregnant patients with thrombophilia. Diagnostics (Basel), 2022, 12(4): 1009.
17.	Kuhle S, Maguire B, Zhang H, et al. Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study. BMC Pregnancy Childbirth, 2018, 18(1): 333.
18.	Tao J, Yuan Z, Sun L, et al. Fetal birthweight prediction with measured data by a temporal machine learning method. BMC Med Inform Decis Mak, 2021, 21(1): 26.
19.	Crovetto F, Triunfo S, Crispi F, et al. Differential performance of first-trimester screening in predicting small-for-gestational-age neonate or fetal growth restriction. Ultrasound Obstet Gynecol, 2017, 49(3): 349-356.
20.	Wahab RJ, Jaddoe VWV, van Klaveren D, et al. Preconception and early-pregnancy risk prediction for birth complications: development of prediction models within a population-based prospective cohort. BMC Pregnancy Childbirth, 2022, 22(1): 165.
21.	Mula R, Meler E, García S, et al. Screening for small-for-gestational age neonates at early third trimester in a high-risk population for preeclampsia. BMC Pregnancy Childbirth, 2020, 20(1): 563.
22.	Schwartz N, Wang E, Parry S. Two-dimensional sonographic placental measurements in the prediction of small-for-gestational-age infants. Ultrasound Obstet Gynecol, 2012, 40(6): 674-679.
23.	Souka AP, Papastefanou I, Pilalis A, et al. Performance of third-trimester ultrasound for prediction of small-for-gestational-age neonates and evaluation of contingency screening policies. Ultrasound Obstet Gynecol, 2012, 39(5): 535-542.
24.	Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart, 2012, 98(9): 683-690.
25.	Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol, 2016, 69: 245-247.
26.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
27.	贺婷, 袁丽, 杨小玲, 等. 亚洲2型糖尿病发病风险预测模型的系统评价. 中国全科医学, 2022, 25(34): 4267-4277.
28.	郭慧敏. 基于Stacking模型融合的Ⅱ型糖尿病分类预测研究. 合肥: 安徽大学, 2021.
29.	Kim MA, Han GH, Kim YH. Prediction of small-for-gestational age by fetal growth rate according to gestational age. PLoS One, 2019, 14(4): e0215737.
30.	Skovron ML, Berkowitz GS, Lapinski RH, et al. Evaluation of early third-trimester ultrasound screening for intrauterine growth retardation. J Ultrasound Med, 1991, 10(3): 153-159.

1. The American College of Obstetricians and Gynecologists. ACOG Practice bulletin no. 134: fetal growth restriction. Obstet Gynecol, 2013, 121(5): 1122-1133.
2. Ding G, Tian Y, Zhang Y, et al. Application of a global reference for fetal-weight and birthweight percentiles in predicting infant mortality. BJOG, 2013, 120(13): 1613-1621.
3. 龚海红, 张丽娜, 凌岚. 重组人生长激素治疗小于胎龄儿持续矮小的疗效观察. 南京医科大学学报(自然科学版), 2013, 33(2): 263-264.
4. Cianfarani S, Ladaki C, Geremia C. Hormonal regulation of postnatal growth in children born small for gestational age. Horm Res, 2006, 65(Suppl 3): 70-74.
5. Chen HY, Chauhan SP, Ward TC, et al. Aberrant fetal growth and early, late, and postneonatal mortality: an analysis of Milwaukee births, 1996-2007. Am J Obstet Gynecol, 2011, 204(3): 261.e1-261.e10.
6. 刘璐. 基于机器学习的小于胎龄儿预测模型的研究. 北京: 北京工业大学, 2017.
7. Levin S, Toerper M, Hamrock E, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Ann Emerg Med, 2018, 71(5): 565-574.
8. 刘慧婷. 小儿胎龄儿危险因素及其预测模型的研究. 北京: 北京协和医学院, 2015.
9. 崔建伟, 赵哲, 杜小勇. 支撑机器学习的数据管理技术综述. 软件学报, 2021, 32(3): 604-621.
10. 陈茹, 王胜锋, 周家琛, 等. 预测模型研究的偏倚风险和适用性评估工具解读. 中华流行病学杂志, 2020, 41(5): 776-781.
11. 梁思远. 基于深度学习的小于胎龄儿疾病预测方法研究. 北京: 北京工业大学, 2018.
12. McCowan LM, Thompson JM, Taylor RS, et al. Clinical prediction in early pregnancy of infants small for gestational age by customised birthweight centiles: findings from a healthy nulliparous cohort. PLoS One, 2013, 8(8): e70917.
13. McCowan LM, Thompson JM, Taylor RS, et al. Prediction of small for gestational age infants in healthy nulliparous women using clinical and ultrasound risk factors combined with early pregnancy biomarkers. PLoS One, 2017, 12(1): e0169311.
14. Saw SN, Biswas A, Mattar CNZ, et al. Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor. Prenat Diagn, 2021, 41(4): 505-516.
15. Bai X, Zhou Z, Luo Y, et al. Development and evaluation of a machine learning prediction model for small-for-gestational-age births in women exposed to radiation before pregnancy. J Pers Med, 2022, 12(4): 550.
16. Vicoveanu P, Vasilache IA, Scripcariu IS, et al. Use of a feed-forward back propagation network for the prediction of small for gestational age newborns in a cohort of pregnant patients with thrombophilia. Diagnostics (Basel), 2022, 12(4): 1009.
17. Kuhle S, Maguire B, Zhang H, et al. Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study. BMC Pregnancy Childbirth, 2018, 18(1): 333.
18. Tao J, Yuan Z, Sun L, et al. Fetal birthweight prediction with measured data by a temporal machine learning method. BMC Med Inform Decis Mak, 2021, 21(1): 26.
19. Crovetto F, Triunfo S, Crispi F, et al. Differential performance of first-trimester screening in predicting small-for-gestational-age neonate or fetal growth restriction. Ultrasound Obstet Gynecol, 2017, 49(3): 349-356.
20. Wahab RJ, Jaddoe VWV, van Klaveren D, et al. Preconception and early-pregnancy risk prediction for birth complications: development of prediction models within a population-based prospective cohort. BMC Pregnancy Childbirth, 2022, 22(1): 165.
21. Mula R, Meler E, García S, et al. Screening for small-for-gestational age neonates at early third trimester in a high-risk population for preeclampsia. BMC Pregnancy Childbirth, 2020, 20(1): 563.
22. Schwartz N, Wang E, Parry S. Two-dimensional sonographic placental measurements in the prediction of small-for-gestational-age infants. Ultrasound Obstet Gynecol, 2012, 40(6): 674-679.
23. Souka AP, Papastefanou I, Pilalis A, et al. Performance of third-trimester ultrasound for prediction of small-for-gestational-age neonates and evaluation of contingency screening policies. Ultrasound Obstet Gynecol, 2012, 39(5): 535-542.
24. Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart, 2012, 98(9): 683-690.
25. Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol, 2016, 69: 245-247.
26. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
27. 贺婷, 袁丽, 杨小玲, 等. 亚洲2型糖尿病发病风险预测模型的系统评价. 中国全科医学, 2022, 25(34): 4267-4277.
28. 郭慧敏. 基于Stacking模型融合的Ⅱ型糖尿病分类预测研究. 合肥: 安徽大学, 2021.
29. Kim MA, Han GH, Kim YH. Prediction of small-for-gestational age by fetal growth rate according to gestational age. PLoS One, 2019, 14(4): e0215737.
30. Skovron ML, Berkowitz GS, Lapinski RH, et al. Evaluation of early third-trimester ultrasound screening for intrauterine growth retardation. J Ultrasound Med, 1991, 10(3): 153-159.

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