Draper AJ, Ye Y, Ruiz VM, Patterson C, Urbach A, Palmer F, Wang S, Somboonna M, Tsui F.-C., Using laboratory data for prediction of 30-day hospital readmission of pediatric seizure patients, Conference Proceedings, American Medical Informatics Association, Washington DC, Nov. 2014 (Distinguished award)
Prior work in readmission risk prediction has under-utilized laboratory data, which may provide valuable information about a patient’s condition. We aim to assess the contribution of laboratory data in predicting readmission risk. Preliminary work has focused on pediatric seizure, which has the highest volume of pediatric readmissions but no identified readmission risk factors.
We used ICD-9 codes to identify seizure-specific visits to Children’s Hospital of Pittsburgh of UPMC during 2007-2012. Patients were considered readmitted if they returned to the hospital within 30-days post-discharge. We extracted features to summarize laboratory data for each patient. We used a training dataset (2007-2011) to rank features with information gain ratio and added features to a baseline model in order of rank. We kept features that improved prediction accuracy under 10-fold cross validation. A testing dataset (2012) was used to compare the AUROCs of the baseline model and the model with the added laboratory features. The addition of laboratory features significantly improved the prediction ability of the model, which suggests that laboratory data may be useful in identifying patients at risk of readmission. Ongoing work includes examining the contribution of laboratory data to readmission risk in adult heart failure patients.