External Validation of the fullPIERS Risk Prediction Model in a U.S. Cohort of Individuals with Preeclampsia

Abstract

Objective

This study aimed to externally validate the Preeclampsia Integrated Estimate of Risk (fullPIERS) risk prediction model in a cohort of pregnant individuals with preeclampsia in the United States.

Study Design

This was a retrospective study of individuals with preeclampsia who delivered at 22 weeks or greater from January 1, 2010, to December 31, 2020. The primary outcome was a composite of maternal mortality or other serious complications of preeclampsia occurring within 48 hours of admission. We calculated the probability of the composite outcome using the fullPIERS prediction model based on data available within 12 hours of admission, including gestational age, chest pain or dyspnea, serum creatinine levels, platelet count, aspartate transaminase levels, and oxygen saturation. We assessed the model performance using the area under the curve (AUC) of the receiver operating characteristic curve. The optimal cutoff point was determined using Liu's method. A calibration plot was used to evaluate the model's goodness-of-fit.

Results

Among 1,510 individuals with preeclampsia, 82 (5.4%) experienced the composite outcome within 48 hours. The fullPIERS model achieved an AUC of 0.80 (95% confidence interval [CI]: 0.75–0.86). The predicted probability for individuals with the composite outcome (median: 18.8%; interquartile range: 2.9–59.1) was significantly higher than those without the outcome (median: 0.9%; interquartile range: 0.4–2.7). The optimal cutoff point of 5.5% yielded a sensitivity of 70.7% (95% CI: 59.6–80.3), a specificity of 85% (95% CI: 82.7–86.5), a positive likelihood ratio of 4.6 (95% CI: 3.8–5.5), and an odds ratio of 13.3 (95% CI: 8.1–21.8). The calibration plot indicated that the model underestimated risk when the predicted probability was below 1% and overestimated risk when the predicted probability exceeded 5%.

Conclusion

The fullPIERS model demonstrated good discrimination in this U.S. cohort of individuals with preeclampsia, suggesting it may be a useful tool for health care providers to identify individuals at risk for severe complications.

Key Points

• The fullPIERS risk prediction model has not been validated in a U.S. cohort.

• The model showed good predictive accuracy (AUC: 0.80) for severe maternal complications but had calibration issues at extreme-risk levels.

• This study confirms the fullPIERS model's applicability in the United States.

Keywords

preeclampsia - severe maternal complication - prediction model - severe complications

• References

• 1 Abalos E, Cuesta C, Grosso AL, Chou D, Say L. Global and regional estimates of preeclampsia and eclampsia: a systematic review. Eur J Obstet Gynecol Reprod Biol 2013; 170 (01) 1-7
• 2 Ananth CV, Keyes KM, Wapner RJ. Pre-eclampsia rates in the United States, 1980-2010: age-period-cohort analysis. BMJ 2013; 347 (15) f6564
• 3 Lisonkova S, Sabr Y, Mayer C, Young C, Skoll A, Joseph KS. Maternal morbidity associated with early-onset and late-onset preeclampsia. Obstet Gynecol 2014; 124 (04) 771-781
• 4 Ghosh G, Grewal J, Männistö T. et al. Racial/ethnic differences in pregnancy-related hypertensive disease in nulliparous women. Ethn Dis 2014; 24 (03) 283-289
• 5 MacKay AP, Berg CJ, Liu X, Duran C, Hoyert DL. Changes in pregnancy mortality ascertainment: United States, 1999-2005. Obstet Gynecol 2011; 118 (01) 104-110
• 6 Chang J, Elam-Evans LD, Berg CJ. et al. Pregnancy-related mortality surveillance–United States, 1991–1999. MMWR Surveill Summ 2003; 52 (02) 1-8
• 7 Odegård RA, Vatten LJ, Nilsen ST, Salvesen KA, Austgulen R. Preeclampsia and fetal growth. Obstet Gynecol 2000; 96 (06) 950-955
• 8 Simpson LL. Maternal medical disease: risk of antepartum fetal death. Semin Perinatol 2002; 26 (01) 42-50
• 9 Laughon SK, Zhang J, Grewal J, Sundaram R, Beaver J, Reddy UM. Induction of labor in a contemporary obstetric cohort. Am J Obstet Gynecol 2012; 206 (06) 486.e1-486.e9
• 10 American College of Obstetricians and Gynecologists. Hypertension in pregnancy: executive summary. Obstet Gynecol 2013; 122 (05) 1122-1131
• 11 von Dadelszen P, Payne B, Li J. et al; PIERS Study Group. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model. Lancet 2011; 377 (9761) 219-227
• 12 Ukah UV, Payne B, Karjalainen H. et al; fullPIERS Group. Temporal and external validation of the fullPIERS model for the prediction of adverse maternal outcomes in women with pre-eclampsia. Pregnancy Hypertens 2019; 15: 42-50
• 13 Boutot M, Margueritte F, Boukeffa N, Coste Mazeau P, Aubard Y, Gauthier T. Validation externe du modele FullPIERS dans la pré-éclampsie à partir d'une série française de 4 ans [External validation of Full PIERS model for prediction of adverse outcomes among women with pre-eclampsia in French maternity of 2014 to 2018]. Gynecol Obstet Fertil Senol 2020; 48 (02) 167-173
• 14 Ukah UV, Payne B, Lee T, Magee LA, von Dadelszen P. fullPIERS and miniPIERS Working Groups. External validation of the fullPIERS model for predicting adverse maternal outcomes in pregnancy hypertension in low- and middle-income countries. Hypertension 2017; 69 (04) 705-711
• 15 Akkermans J, Payne B, von Dadelszen P. et al. Predicting complications in pre-eclampsia: external validation of the fullPIERS model using the PETRA trial dataset. Eur J Obstet Gynecol Reprod Biol 2014; 179: 58-62
• 16 American College of Obstetricians and Gynecologists. Gestational hypertension and preeclampsia: ACOG Practice Bulletin, Number 222. Obstet Gynecol 2020; 135 (06) e237-e260
• 17 Harris PA, Taylor R, Minor BL. et al; REDCap Consortium. The REDCap consortium: building an international community of software platform partners. J Biomed Inform 2019; 95: 103208
• 18 Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol 2022; 75 (01) 25-36
• 19 Liu A, Wu C, Schisterman EF. Nonparametric sequential evaluation of diagnostic biomarkers. Stat Med 2008; 27 (10) 1667-1678
• 20 Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26 (06) 565-574
• 21 Fitzgerald M, Saville BR, Lewis RJ. Decision curve analysis. JAMA 2015; 313 (04) 409-410
• 22 Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 2016; 352: i6
• 23 Guida JP, Cralcev C, Costa Santos J, Marangoni-Junior M, Sanchez MP, Laura Costa M. Validation of the fullPIERS model for prediction of adverse outcomes in preeclampsia at a referral center. Pregnancy Hypertens 2021; 23: 112-115