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ABSTRACT To make early predictions of preeclampsia before diagnosis, we developed and validated a new nomogram for the early prediction of preeclampsia in pregnant Chinese women. A stepwise

regression model was used for feature selection. Multivariable logistic regression analysis was used to develop the prediction model. We incorporated BMI, blood pressure, uterine artery

ultrasound parameters, and serological indicator risk factors, and this was presented with a nomogram. The performance of the nomogram was assessed with respect to its calibration,

discrimination, and clinical usefulness. Internal validation was assessed. The signature, which consisted of 11 selected features, was associated with preeclampsia status (_P_ < 0.1) for

the development dataset. Predictors contained in the individualized prediction nomogram included BMI, blood pressure, uterine artery ultrasound parameters, and serological indicator levels.

The model showed good discrimination, with an area under the ROC curve of 0.8563 (95% CI: 0.8364–0.8761) and good calibration. The nomogram still had good discrimination and good calibration

when applied to the validation dataset (area under ROC curve of 0.8324, 95% CI: 0.7873–0.8775). Decision curve analysis demonstrated that the nomogram was clinically useful. The nomogram

presented in this study incorporates BMI, blood pressure, uterine artery ultrasound parameters, and serological indicators and can be conveniently used to facilitate the individualized

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our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS MACHINE-LEARNING PREDICTIVE MODEL OF PREGNANCY-INDUCED HYPERTENSION IN THE FIRST TRIMESTER Article 09 May 2023

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ZERO-COST MATERNAL PREDICTORS EXCLUSIVELY Article Open access 07 February 2024 REFERENCES * De Kat AC, Hirst J, Woodward M, Kennedy S, Peters SA. Prediction models for preeclampsia: a

systematic review. Pregnancy Hypertens. 2019;16:48–66. Article  Google Scholar  * Khan KS, Wojdyla D, Say L, Gülmezoglu AM, Van Look PF. WHO analysis of causes of maternal death: a

systematic review. Lancet. 2006;367:1066–74. Article  Google Scholar  * Bian X, Biswas A, Huang X, Lee KJ, Li TK, Masuyama H, et al. Short-term prediction of adverse outcomes using the

sFlt-1 (soluble fms-like tyrosine kinase 1)/PlGF (placental growth factor) ratio in Asian women with suspected preeclampsia. Hypertension. 2019;74:164–72. Article  CAS  Google Scholar  *

Agrawal S, Shinar S, Cerdeira AS, Redman C, Vatish M. Predictive performance of plgf (placental growth factor) for screening preeclampsia in asymptomatic women: a systematic review and

meta-analysis. Hypertension. 2019;74:1124–35. Article  CAS  Google Scholar  * Yue CY, Ying CM. Epidemiological analysis of maternal lipid levels during the second trimester in pregnancy and

the risk of adverse pregnancy outcome adjusted by pregnancy BMI. J Clin Lab Anal. 2018;32:e22568. Article  Google Scholar  * Yu CK, Smith GC, Papageorghiou AT, Cacho AM, Nicolaides KH, Fetal

Medicine Foundation Second Trimester Screening Group. An integrated model for the prediction of preeclampsia using maternal factors and uterine artery Doppler velocimetry in unselected

low-risk women. Am J Obstet Gynecol. 2005;193:429–36. Article  Google Scholar  * Goetzinger KR, Tuuli MG, Cahill AG, Macones GA, Odibo AO. Development and validation of a risk factor scoring

system for first-trimester prediction of preeclampsia. Am J Perinatol. 2014;31:1049–56. Article  Google Scholar  * Kenny LC, Black MA, Poston L, Taylor R, Myers JE, Baker PN, et al. Early

pregnancy prediction of preeclampsia in nulliparous women, combining clinical risk and biomarkers: the Screening for Pregnancy Endpoints (SCOPE) international cohort study. Hypertension.

2014;643:644–52. Article  Google Scholar  * American College of Obstetricians and Gynecologists: Hypertension in pregnancy. ACOG Tech Bull. 1996;219:1–8. * Holick MF, Binkley NC,

Bischoff-Ferrari HA, et al. Evaluation, treatment, and prevention of vitamin D deficiency: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2011;96:1911–30. Article

  CAS  Google Scholar  * WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63.

Article  Google Scholar  * Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD

statement. BMJ. 2015,350:g7594. Article  Google Scholar  * Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests,

prediction models and molecular markers. BMC Med Inf Decis Mak. 2008;8:53. Article  Google Scholar  * Phipps E, Prasanna D, Brima W, Jim B. Preeclampsia: updates in pathogenesis,

definitions, and guidelines. Clin J Am Soc Nephrol. 2016;11:1102–13. Article  CAS  Google Scholar  * Avagliano L, Bulfamante GP, Morabito A, Marconi AM. Abnormal spiral artery remodelling in

the decidual segment during pregnancy: from histology to clinical correlation. J Clin Pathol. 2011;64:1064–8. Article  Google Scholar  * Campbell S, Black RS, Lees CC, Armstrong V, Peacock

JL. Doppler ultrasound of the maternal uterine arteries: disappearance of abnormal waveforms and relation to birthweight and pregnancy outcome. Acta Obstet Gynecol Scand. 2000;79:631–4. CAS

  PubMed  Google Scholar  * Roberts JM, Cooper DW. Pathogenesis and genetics of pre-eclampsia. Lancet. 2001;357:53–6. Article  CAS  Google Scholar  * Frusca T, Soregaroli M, Platto C,

Enterri L, Lojacono A, Valcamonico A. Uterine artery velocimetry in patients with gestational hypertension. Obstet Gynecol. 2003;102:136–40. CAS  PubMed  Google Scholar  * Reister F, Frank

HG, Kingdom JC, Heyl W, Kaufmann P, Rath W, et al. Macrophage-induced apoptosis limits endovascular trophoblast invasion in the uterine wall of preeclamptic women. Lab Invest.

2001;81:1143–52. Article  CAS  Google Scholar  * Konje JC, Kaufmann P, Bell SC, Taylor DJ. A longitudinal study of quantitative uterine blood flow with the use of color power angiography in

appropriate for gestational age pregnancies. Am J Obstet Gynecol. 2001;185:608–13. Article  CAS  Google Scholar  * Singnoi W, Wanapirak C, Sekararithi R, Tongsong T. A cohort study of the

association between maternal serum Inhibin-A and adverse pregnancy outcomes: a population-based study. BMC Pregnancy Childbirth. 2019;19:124. Article  Google Scholar  * Shen Z, Cai LY,

Suprapto IS, Shenoy P, Zhou X. Placental and maternal serum inhibin A in patients with preeclampsia and small-for-gestational-age. J Obstet Gynaecol Res. 2011;37:1290–6. Article  Google

Scholar  * Zeeman GG, Alexander JM, McIntire DD, Byrd W, Leveno KJ. Inhibin-A levels and severity of hypertensive disorders due to pregnancy. Obstet Gynecol. 2002;100:140–4. CAS  PubMed 

Google Scholar  * Xu YT, Shen MH, Jin AY, Li H, Zhu R. Maternal circulating levels of transforming growth factor-β superfamily and its soluble receptors in hypertensive disorders of

pregnancy. Int J Gynaecol Obstet. 2017;137:246–52. Article  CAS  Google Scholar  * Eche S, Mackraj I, Moodley J. Circulating fetal and total cell-free DNA, and sHLA-G in black South African

women with gestational hypertension and pre-eclampsia. Hypertens Pregnancy. 2017;36:295–301. Article  CAS  Google Scholar  * Nerenberg KA, Park AL, Vigod SN. Long-term risk of a seizure

disorder after eclampsia. Obstet Gynecol. 2017;130:1327–33. Article  Google Scholar  * El Khouly NI, Sanad ZF, Saleh SA, Shabana AA, Elhalaby AF, Badr EE. Value of first-trimester serum

lipid profile in early prediction of preeclampsia and its severity: a prospective cohort study. Hypertens Pregnancy. 2016;35:73–81. Article  CAS  Google Scholar  * Yalamati P, Bhongir AV,

Betha K, Verma R, Dandge S. Relationship of serum uric acid, serum creatinine and serum cystatin C with maternal and fetal outcomes in rural Indian pregnant women. Int J Reprod Contracept

Obstet Gynecol. 2015;4:1505–10. Article  Google Scholar  * Escudero C, Bertoglia P, Muñoz F, Roberts JM. Uric acid and purine plasma levels as plausible markers for placental dysfunction in

pre-eclampsia. Rev Med Chil. 2013;141:895–902. Article  CAS  Google Scholar  * Weissgerber TL, Milic NM, Turner ST, Asad RA, Mosley TH, Kardia SL, et al. Uric acid: a missing link between

hypertensive pregnancy disorders and future cardiovascular disease? Mayo Clin Proc. 2015;90:1207–16. Article  CAS  Google Scholar  * Masoura S, Makedou K, Theodoridis T, Kourtis A, Zepiridis

L, Athanasiadis A. The involvement of uric acid in the pathogenesis of preeclampsia. Curr Hypertens Rev. 2015;11:110–5. Article  CAS  Google Scholar  * Collins GS, Reitsma JB, Altman DG,

Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Br J Surg. 2015;102:148–58. Article  CAS  Google

Scholar  * Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16:e173–80. Article  Google Scholar  * De Kat AC, Hirst J,

Woodward M, Kennedy S, Peters SA. Prediction models for preeclampsia: a systematic review. Pregnancy Hypertens. 2019;16:48–66. Article  Google Scholar  * Albaiges G, Missfelder-Lobos H, Lees

C, Parra M, Nicolaides KH. One-stage screening for pregnancy complications by color Doppler assessment of the uterine arteries at 23 weeks’ gestation. Obstet Gynecol. 2000;96:559–64. CAS 

PubMed  Google Scholar  Download references FUNDING This work was supported by the program for National Natural Science Foundation of China (No. 81902131) and Shanghai Medical Academy New

Star Young Medical talents training subsidy Program (Shanghai Health personnel 2020-087). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Laboratory Medicine, Obstetrics and

Gynecology Hospital of Fudan University, Shanghai, China Chao-yan Yue, Chun-yi Zhang, Ying-hua Ni & Chun-mei Ying * College of Engineering and Computer Science, Australian National

University, Canberra, ACT, Australia Jiang-ping Gao Authors * Chao-yan Yue View author publications You can also search for this author inPubMed Google Scholar * Jiang-ping Gao View author

publications You can also search for this author inPubMed Google Scholar * Chun-yi Zhang View author publications You can also search for this author inPubMed Google Scholar * Ying-hua Ni

View author publications You can also search for this author inPubMed Google Scholar * Chun-mei Ying View author publications You can also search for this author inPubMed Google Scholar

CONTRIBUTIONS C-YY analyzed the data, drafted the manuscript and contributed to the study design. J-pG contributed to the data analysis. C-yiZ and Y-hN contributed to data collection. C-MY

revised the article. All authors reviewed and edited the manuscript and approved the final version of the manuscript. CORRESPONDING AUTHOR Correspondence to Chun-mei Ying. ETHICS

DECLARATIONS CONFLICT OF INTEREST The authors declare that they have no conflict of interest. ETHICAL APPROVAL The present study conformed to the principles of the Declaration of Helsinki.

Approval was obtained from the Research Ethics Committee of the Obstetrics & Gynecology Hospital of Fudan University (approval number: 2019-06). ADDITIONAL INFORMATION PUBLISHER’S NOTE

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THIS ARTICLE Yue, Cy., Gao, Jp., Zhang, Cy. _et al._ Development and validation of a nomogram for the early prediction of preeclampsia in pregnant Chinese women. _Hypertens Res_ 44, 417–425

(2021). https://doi.org/10.1038/s41440-020-00558-1 Download citation * Received: 07 August 2020 * Revised: 31 August 2020 * Accepted: 11 September 2020 * Published: 15 October 2020 * Issue

Date: April 2021 * DOI: https://doi.org/10.1038/s41440-020-00558-1 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a

shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * Preeclampsia * Uterine artery

Doppler * Inhibin-A * Nomogram * Prediction