Prediction model of hypertensive disorders of pregnancy based on home blood pressure monitoring

Hypertensive disorders of pregnancy (HDP) cause adverse maternal and fetal outcomes, and establishing an early prediction method for HDP is needed. Existing methods based on serum markers require blood sampling. Therefore, we aimed to develop and validate a noninvasive HDP prediction model based on home blood pressure monitoring (HBPM).

Methods

In a development cohort, HBPM data from 443 pregnant women including 65 HDPs were divided into training data (n=365) and test data (n=78) to develop a logistic regression-based prediction model. Normal blood pressure variations depending on season and gestational age were subtracted. At each time point, four features were calculated for the last four weeks of data: average systolic blood pressure (SBP), average diastolic blood pressure (DBP), correlation coefficient between SBP and DBP, and upward trend of SBP against day. In a validation cohort, HBPM data from 264 pregnant women including 33 HDPs were collected prospectively and used to validate the model.

Results

The area under the receiver operating characteristic curve was 0.949, 0.884, and 0.845 for the training, test, and validation data, respectively. Sensitivity, specificity, positive predictive value, and negative predictive value were 0.923, 0.888, 0.387, and 0.993 for the training data; 0.667, 0.897, 0.867, and 0.729 for the test data; 0.758, 0.766, 0.316, and 0.957 for the validation data.

Conclusions

Our HDP prediction model based on HBPM showed high negative predictive values and may contribute to reducing medical consultations.

Clinical Perspective