A hospital wide predictive model for unplanned readmission using hierarchical ICD data

Abstract

Background and objective: Hospitals already acquire a large amount of data, mainly for administrative, billing and registration purposes. Tapping on these already available data for additional purposes, aiming at improving care, without significant incremental effort and cost. This potential of secondary patient data is explored through modeling administrative and billing data, as well as the hierarchical structure of pathology codes of the International Classification of Diseases (ICD) in the prediction of unplanned readmissions, as a clinically relevant outcome parameter that can be impacted on in a quality improvement program.

Methods: In this single-center, hospital-wide observational cohort study, we included all adult patients discharged in 2016 after applying an exclusion protocol (n = 29,702). In addition to administrative variables, such as age and length of stay, structured pathology data were taken into account in predictive models. As a first research question, we compared logistic regression against penalized logistic regression, gradient boosting and Random Forests to predict unplanned readmission. As a second research goal, we investigated the level of hierarchy within the pathology data needed to achieve the best accuracy. Finally, we investigated which prediction variables play a prominent role in predicting hospital readmission. The performance of all models was evaluated using the Area Under the ROC Curve (AUC) measure.

Results: All models have the best predictive results using Random Forests. An added value of 7% is observed compared to a baseline method such as logistic regression. The best model, based on Random Forests, achieved an AUC of 0.77, using the diagnosis category and procedure code as lowest level of the hierarchical pathology data.

Conclusions: The most accurate model to predict hospital wide unplanned readmission is based on Random Forests and includes the ICD hierarchy, especially diagnosis category. Such an approach lowers the number of predictor variables and yields a higher interpretability than a model based on a detailed diagnosis. The performance of the model proved high enough to be used as a decision support tool.

Keywords: Boosting; Decision support; ICD-10 diagnosis; Machine learning; Random Forests; Readmission.