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Predicting complications in pre-eclampsia: external validation of the fullPIERS model using the PETRA trial dataset.

Eur J Obstet Gynecol Reprod Biol. 2014 Aug;179:58-62. doi: 10.1016/j.ejogrb.2014.05.021. Epub 2014 Jun 2.

Akkermans J1, Payne B2, Dadelszen Pv2, Groen H3, Vries Jd4, Magee LA5, Mol BW6, Ganzevoort W7.

Eur J Obstet Gynecol Reprod Biol
AbstractOBJECTIVE: The internally validated fullPIERS model predicts adverse maternal outcomes in women with pre-eclampsia within 48h after eligibility. Our objective was to assess generalizability of this prediction model.STUDY DESIGN: External validation study using prospectively collected data from two tertiary care obstetric centers.METHODS: The existing PETRA dataset, a cohort of women (n=216) with severe early-onset pre-eclampsia, eclampsia, HELLP syndrome or hypertension-associated fetal growth restriction was used. The fullPIERS model equation was applied to all women in the dataset using values collected within 48h after inclusion. The performance (ROC area and R-squared) of the model, risk stratification and calibration were assessed from 48h up to a week after inclusion.RESULTS: Of 216 women in the PETRA trial, 73 (34%) experienced an adverse maternal outcome(s) at any time after inclusion. Adverse maternal outcome was observed in 32 (15%) cases within 48h and 62 (29%) within 7 days after inclusion. The fullPIERS model predicted adverse maternal outcomes within 48h (AUC ROC 0.97, 95% CI: 0.87-0.99) and up to 7 days after inclusion (AUC ROC 0.80, 95% CI: 0.70-0.87).CONCLUSIONS: The fullPIERS model performed well when applied to the PETRA dataset. These results confirm the usability of the fullPIERS prediction model as a ‘rule-in’ test for women admitted with severe pre-eclampsia, eclampsia, HELLP syndrome or hypertension-associated fetal growth restriction. Future research should focus on intervention studies that assess the clinical impact of strategies using the fullPIERS model.Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Full Text Source: Elsevier Science
PMID:24965981 |

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