Abstract
BACKGROUND:
Preeclampsia remains a leading cause of maternal mortality, particularly in low-resource settings. We aimed to develop a dynamic prediction model accommodating repeated measurements for predicting adverse maternal outcomes in preeclampsia.
METHODS:
We utilized data from 7283 women with preeclampsia from the PIERS studies (Preeclampsia Integrated Estimate of Risk). Data from 6548 women were randomly split into a training set (80%) and an internal validation set (20%), while an independent cohort of 735 women was used for external validation. The outcome was a composite of maternal mortality and severe maternal morbidity. The mini PIERSdynamic model was fitted with the binary mixed model random forest algorithm. Threshold probabilities were calculated for 5 risk strata, ranging from very low to very high risk. Model performance was externally validated.
RESULTS:
In the development cohort, 785 (12.0%) women experienced an adverse maternal outcome, compared with 91 (12.4%) women in the external validation cohort. The final model retained all candidate predictors. Model performance was similar in internal and external validation. The daily area under the receiver-operating-characteristics curve remained around 0.75 on days 0 to 9 in internal validation and 0.70 in external validation, with peak values of 0.92 and 0.94, respectively. The area under the precision-recall curve declined over time as outcome incidence decreased. Observed daily event rates corresponded closely with the assigned risk strata.
CONCLUSIONS:
We developed the mini PIERSdynamic model and performed external validation, demonstrating its potential for dynamic prediction of adverse maternal outcomes in preeclampsia for up to 10 days after initial presentation. Further evaluation is required to verify its generalizability and clinical utility.