Predicting risk of maternal critical care admission in Scotland: Development of a risk prediction model.

Abstract

Background: Identifying women at highest or lowest risk of perinatal intensive care unit (ICU) admission may enable clinicians to risk stratify women antenatally so that enhanced care or elective admission to ICU may be considered or excluded in birthing plans. We aimed to develop a statistical model to predict the risk of maternal ICU admission.

Methods: We studied 762,918 pregnancies between 2005 and 2018. Predictive models were constructed using multivariable logistic regression. The primary outcome was ICU admission. Additional analyses were performed to allow inclusion of delivery-related factors. Predictors were selected following expert consultation and reviewing literature, resulting in 13 variables being included in the primary analysis: demographics, prior health status, obstetric history and pregnancy-related factors. A complete case analysis was performed. K-fold cross validation was used to mitigate against overfitting.

Results: Complete data were available for 578,310 pregnancies, of whom 1087 were admitted to ICU (0.19%). Model performance was fair (area under the ROC curve = 0.66). A comparatively high cut-point of ⩾0.6% for ICU admission risk resulted in a negative predictive value (NPV) of 99.8% (specificity 97.8%) but positive predictive value (PPV) of 0.8% (sensitivity 9.1%). Models including delivery-related factors demonstrated superior discriminative performance.

Conclusions: Our model for maternal ICU admission has an acceptable discriminative performance. The low frequency of ICU admission and resulting low PPV indicates that the model would be unlikely to be useful as a 'rule-in' test for pre-emptive consideration of ICU admission. Its potential for improving efficiency in screening as a 'rule-out' test remains uncertain.