Heart failure mortality prediction using PRISM score and development of a classification and regression tree model to refer patients for palliative care consultation

Abstract

Introduction: We sought to assess one-year mortality in heart failure (HF) patients by using (Placement Resource Indicator for Systems Management) PRISM, a disease nonspecific risk stratification score, and use it along with modified Seattle Heart Failure Model (SHFM) to guide patient selection for palliative care consultation. Methods: A retrospective study design was used to examine 1-year mortality in 689 HF patients admitted from 2012 to 2014. One-year mortality was calculated using Pmort30/PRISM and modified SHFM scores, and the predicted scores were validated using the area under the ROC curve. CART was used to develop an algorithm to classify patients based on their mortality risk. Results: The discriminatory ability of PRISM categorical score (AUC = 0.701) was not significantly different than the discriminatory ability of modified SHFM (AUC = 0.686) (DeLong's test p = 0.56) but improved significantly with the combination of PRISM (categorical) score + modified SHFM (AUC = 0.740) (p = 0.002). The predictive capability of the CART tree model after cross-validation was 72.2% (AUC 0.631). Conclusion: Our study suggests PRISM score performed as well as modified SHFM for one-year mortality prediction. Moreover, the addition of modified SHFM to PRISM score increases discriminatory ability in predicting 1-year mortality in heart failure patients compared to either of the two models alone. Together, when combined in a CART model, they can be used to identify the population subset with the highest mortality risk and hence guide goals of care discussion.