Risk Score For Rehospitalization Among Home Health Care Patients with Heart Failure

Abstract

Introduction Heart failure (HF) is one of the leading causes of rehospitalization in the United States. Rehospitalizations are a costly outcome and are challenging to reduce due to multiple factors. Some interventions for reducing readmissions utilize predictive models to forecast adverse events such as mortality and rehospitalizations. Many rehospitalization risk scores were developed using hospital-based data from electronic health records (EHR). However, no rehospitalization risk score uses home healthcare EHR data. This study evaluated the production of a rehospitalization risk score using data from the Outcome and Assessment Information Set (OASIS) among home healthcare patients with HF after hospital discharge. Aim To produce a risk score for rehospitalization using OASIS among home healthcare patients with HF after hospital discharge. Methods This study was a secondary data analysis of the OASIS C version that was collected from January 1, 2011 to August 31, 2013. The dataset included N=836 patients. A multivariable logistic regression model was fitted to the rehospitalization outcome, using interactive backwards elimination variable selection. The final model included 9 predictor variables in a simple-to-use risk score that has a possible range (0 to 41). Regression coefficients were divided by the smallest regression coefficient and rounded to the nearest integer. For each patient risk factor, the integer weightings were summed to obtain the risk score. The predictive accuracy of the risk score was measured using the area under the receiver operating characteristic curve (i.e., c-statistic). The model was then internally validated using 10-fold cross validation. Results The median age was 79.5 years old, 43% were male, and 82% were white. We identified 9 significant risk factors: multiple hospitalization in the past 12 months, frequency of pain interfering, urinary incontinence, ability to grooming, ability to dress lower body, ability to feed, prior ability with everyday activities, the level of assistance, and Charlson comorbidity index score. For example, patients with grooming utensils must be placed within reach was a significantly greater risk compared to being able to groom self-unaided. The clinical prediction score which combined the patient's risk factors achieved a c-statistic of 0.64 (95%CI: 0.61 to 0.68). In the 10-fold cross-validation, the risk score had a c-statistic of 0.62 (95%CI: 0.58 to 0.65), which is the validated prediction accuracy expected in future home healthcare patients with HF. Conclusions This study developed a risk score for rehospitalization using OASIS among home healthcare patients with HF after hospital discharge. The risk scores of specific items such as the identified risk factors in this study could be an indicator of decision-making for personalized care.