A Statistical Predictive Model Consistent Within a 5-Year Follow-up Period for Patients with Acute Heart Failure

BACKGROUND Acute heart failure (AHF) is a major and rapidly growing health problem responsible for millions of hospitalizations annually. Due to a high proportion of in-hospital mortality and post-discharge re-hospitalization and mortality, a prompt strategy for risk stratification and subsequently tailored therapy is desirable to help improve clinical outcomes. The AHEAD and AHEAD-U are popular prognostic scoring systems. However, only a specific follow-up period is considered in these systems, and whether their predictive capability is still accurate in a significantly shorter or longer follow-up period is not known. METHODS In this research, we adapted extensive statistical approaches based on the Cox model to explore consistent risk factors in various follow-up durations. Results showed that six factors, namely, hemoglobin level, age, sodium level, blood urea nitrogen level, atrial fibrillation, and high-density lipoprotein level, could be used to establish a new prognostic model, which was referred to as HANBAH. For a simple clinical application, the HANBAH scoring system, with scores from 0 to 6, was developed using several statistical models. RESULTS Based on an evaluation using the conventional statistical approaches, such as the Akaike information criterion, concordance statistic, and Cox area under the curve, the HANBAH scoring system consistently outperformed other strategies in predicting short- and long-term mortality. Notably, an independent replication study also revealed similar results. In addition, a modern machine learning technique using the support vector machine confirmed its superior performance. CONCLUSION The use of the HANBAH scoring system, which is a clinically friendly tool, was proposed, and its efficacy in predicting the mortality rates of patients with AHF regardless of the follow-up duration was independently validated.