Assessing Hospital Readmission Risk Factors in Heart Failure Patients Enrolled in a Telemonitoring Program

The purpose of this study was to validate a previously developed heart failure readmission predictive algorithm based on psychosocial factors, develop a new model based on patient-reported symptoms from a telemonitoring program, and assess the impact of weight fluctuations and other factors on hospital readmission. Clinical, demographic, and telemonitoring data was collected from 100 patients enrolled in the Partners Connected Cardiac Care Program between July 2008 and November 2011. 38% of study participants were readmitted to the hospital within 30 days. Ten different heart-failure-related symptoms were reported 17,389 times, with the top three contributing approximately 50% of the volume. The psychosocial readmission model yielded an AUC of 0.67, along with sensitivity 0.87, specificity 0.32, positive predictive value 0.44, and negative predictive value 0.8 at a cutoff value of 0.30. In summary, hospital readmission models based on psychosocial characteristics, standardized changes in weight, or patient-reported symptoms can be developed and validated in heart failure patients participating in an institutional telemonitoring program. However, more robust models will need to be developed that use a comprehensive set of factors in order to have a significant impact on population health. 1. Introduction Several predictive models can identify the risk status of patients with heart failure [1]. However, predictors used in those models are often not actionable, as they are typically based on demographic (e.g., age, race/ethnicity) or clinical data (e.g., medical history, billing or laboratory data). In our previous work, we aimed to identify a subset of high-risk patients with reversible risk factors, as our goal was to prevent their readmission by connecting those patients to appropriate interventions. Since psychosocial factors might be a root cause for cardiac decompensation, we set ourselves to develop a multivariable logistic regression model based on psychosocial predictors [2]. In that work, we identified 5 psychosocial predictors “dementia,” “depression,” “adherence,” “declining/refusal of services,” and “missed clinical appointments” as significant predictors of readmission [2]. Similarly, patient-reported symptoms and other factors collected by a telemonitoring system could potentially serve as reversible predictors to eventually strengthen our original model. In fact, body weight gain among heart failure patients is already a known factor linked to early readmissions [3]. Telemonitoring is a promising innovation that allows clinicians to monitor