Development of a risk prediction score for acute postpartum care utilization

Acute postpartum care utilization and readmissions are increasing in the United States and contribute significantly to maternal morbidity, mortality, and healthcare costs. Currently, there are limited data on the prediction of patients who will require acute postpartum care utilization.

To develop and validate a risk prediction model for acute postpartum care utilization.

A retrospective cohort study of delivery hospitalizations with a linked birth certificate and discharge records in California from 2011 to 2015 was divided into a training and testing set for analysis and validation. Predictive models for acute postpartum care utilization using demographic, comorbidity, obstetrical complication, and other factors were developed using a backward stepwise logistic regression on training data. A risk score for acute postpartum care utilization was developed using beta coefficients from the factors remaining in the final multivariable model. Risk scores were validated using the testing dataset.

The final sample included 2,045,988 delivery hospitalizations with an acute postpartum care utilization rate of 7.6% in both training and testing cohorts. Twenty-two risk factors were identified for the final multivariable model, including several that were associated with two or more increased odds of acute care utilization (public insurance, postpartum hemorrhage, extremes of maternal age). The mean risk score was 2.45, conferring a 15 times higher risk of acute postpartum care utilization compared to those with a risk score <1 (RR 15.4, 95% CI: 11.0, 21.7). Demographics and test performance characteristics were comparably similar in predictive capability in both models (0.67 in both the training and testing cohorts).

Risk factors that are identifiable before discharge can be used to create a cumulative risk score to stratify patients at the lowest and highest risk of acute postpartum care utilization with satisfactory accuracy. External validation and the addition of other granular clinical variables are necessary to validate the feasibility of use.