Abstract
Most current methods for modeling rehospitalization events in heart failure patients make use of only clinical and medications data that is available in the electronic health records. However, information about patient-reported functional limitations, behavioral variables and socio-economic background of patients may also play an important role in predicting the risk of readmission in heart failure patients. We developed methods for predicting the risk of rehospitalization in heart failure patients using models that integrate clinical characteristics with patient-reported functional limitations, behavioral and socio-economic characteristics. Our goal was to estimate the predictive accuracy of the joint model and compare it with models that make use of clinical data alone or behavioral and socio-economic characteristics alone, using real patient data. We collected data about the occurrence of hospital readmissions from a cohort of 789 heart failure patients for whom a range of clinical and behavioral characteristics data is also available. We applied the Cox model, four different variants of the Cox proportional hazards framework as well as an alternative non-parametric approach and determined the predictive accuracy for different categories of variables. The concordance index obtained from the joint prediction model including all types of variables was significantly higher than the accuracy obtained from using only clinical factors or using only behavioral, socioeconomic background and functional limitations in patients as predictors. Collecting information on behavior, patient-reported estimates of physical limitations and frailty and socio-economic data has significant value in the predicting the risk of readmissions with regards to heart failure events and can lead to substantially more accurate events prediction models.
Highlights
Rehospitalizations account for more than 30% of the 2 trillion annual cost of healthcare in the United States
We will describe all the data sources and factors that are being considered for our study. The data for this project will be comprehensively collected from the following sources of information that are collected at the Henry Ford Health System (HFHS) in south eastern Michigan
We identified all patients with a primary hospital discharge diagnosis of heart failure (9th Edition/ Revision International Classification of Diseases [ICD-9] codes used)
Summary
Rehospitalizations account for more than 30% of the 2 trillion annual cost of healthcare in the United States. Providing special care for a targeted group of patients who are at a high risk of rehospitalization can significantly improve the chances of avoiding rehospitalizations. An important step towards preventing or better managing hospital readmissions is the identification of important prognostic factors to assess the risk of such events for individual patients through the construction of predictive models. This can enable us to identify important physiological targets or characteristic patient profiles that can allow for more focused medical or social interventions, reduce costs and improve the quality of healthcare provided by institutions. The objective of this work is to identify the patients with high risk of rehospitalization at the time of discharge using advanced regression methodology
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
