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.	龔海紅, 張麗娜, 凌嵐. 重組人生長激素治療小于胎齡兒持續矮小的療效觀察. 南京醫科大學學報(自然科學版), 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.	劉璐. 基于機器學習的小于胎齡兒預測模型的研究. 北京: 北京工業大學, 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.	劉慧婷. 小兒胎齡兒危險因素及其預測模型的研究. 北京: 北京協和醫學院, 2015.
9.	崔建偉, 趙哲, 杜小勇. 支撐機器學習的數據管理技術綜述. 軟件學報, 2021, 32(3): 604-621.
10.	陳茹, 王勝鋒, 周家琛, 等. 預測模型研究的偏倚風險和適用性評估工具解讀. 中華流行病學雜志, 2020, 41(5): 776-781.
11.	梁思遠. 基于深度學習的小于胎齡兒疾病預測方法研究. 北京: 北京工業大學, 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.	賀婷, 袁麗, 楊小玲, 等. 亞洲2型糖尿病發病風險預測模型的系統評價. 中國全科醫學, 2022, 25(34): 4267-4277.
28.	郭慧敏. 基于Stacking模型融合的Ⅱ型糖尿病分類預測研究. 合肥: 安徽大學, 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. 龔海紅, 張麗娜, 凌嵐. 重組人生長激素治療小于胎齡兒持續矮小的療效觀察. 南京醫科大學學報(自然科學版), 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. 劉璐. 基于機器學習的小于胎齡兒預測模型的研究. 北京: 北京工業大學, 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. 劉慧婷. 小兒胎齡兒危險因素及其預測模型的研究. 北京: 北京協和醫學院, 2015.
9. 崔建偉, 趙哲, 杜小勇. 支撐機器學習的數據管理技術綜述. 軟件學報, 2021, 32(3): 604-621.
10. 陳茹, 王勝鋒, 周家琛, 等. 預測模型研究的偏倚風險和適用性評估工具解讀. 中華流行病學雜志, 2020, 41(5): 776-781.
11. 梁思遠. 基于深度學習的小于胎齡兒疾病預測方法研究. 北京: 北京工業大學, 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. 賀婷, 袁麗, 楊小玲, 等. 亞洲2型糖尿病發病風險預測模型的系統評價. 中國全科醫學, 2022, 25(34): 4267-4277.
28. 郭慧敏. 基于Stacking模型融合的Ⅱ型糖尿病分類預測研究. 合肥: 安徽大學, 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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