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Abstract
BackgroundReduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients.MethodsThe California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results.ResultsArea under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation.DiscussionThe results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.
Highlights
Hospital readmission prediction models have been widely studied and deployed worldwide (Zhu et al, 2015; Hao et al, 2015; Stiglic et al, 2015)
We focus on the following research question: Does the inclusion of additional information from historical patient hospitalization records improve the predictive performance of 30-day readmission models? Hospitalization claims data from morbidly obese patients were used to build a readmission prediction model
Note that the purpose of this study is to evaluate the contribution of additional information from historical records on the prediction of the 30-days readmission of morbid obesity patients
Summary
Hospital readmission prediction models have been widely studied and deployed worldwide (Zhu et al, 2015; Hao et al, 2015; Stiglic et al, 2015). He et al (2014) demonstrated the importance of a logistic regression predictor representing the number of prior hospitalizations in the past five years This simple variable was selected as significant in both a general and a specific chronic pancreatitis subgroup based predictive model. The target of a study by Shahn et al does not include readmission classification, but focuses on predicting strokes in patients with prior diagnoses of Atrial Fibrillation, it demonstrates the importance of temporal information to achieve meaningful improvements in predictive performance. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients. The results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations
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