Clinical Decision Rules for Heart Failure Management in Acute Emergency Department Settings

Background: Heart failure (HF) is the leading cause of ED visits, thus patients must immediately assess their risk to make informed treatment choices. Emergency heart failure treatment is increasingly using clinical decision rules (CDRs) like the HEART score. More study is needed on their ED efficacy. Aim: This research examined how effectively CDRs predicted ED outcomes for HF patients. In the emergency room of King Hussein Medical Centre, this research examined how effectively and how frequently established CDRs treated acute HF. Planning for release and short-term problems was the emphasis. Methods: This research reviewed all adult HF patients (age 18 or older) presented to the ED between January 2024 and June 2025 using electronic health records. HF patients with acute decompensation were included. No patients with missing data, non-cardiac dyspnoea, or terminal conditions were included. We collected patient data, vital signs, lab findings, CDR usage (HEART-like route, MEESSI score components), and events (hospital admission, 30-day return, and death). ROC curves and multivariable logistic regression let us identify separate adverse event factors and assess the CDR's prediction ability. Results: The MEESSI-based rule accurately identified hospitalised patients (AUC=0.87, 95% CI: 0.85–0.89) and non-hospitalized patients (AUC=0.76, 95% CI: 0.73–0.79) in 1,420 patient interactions. The modified HEART-like circuit differentiated well at entrance (AUC=0.79) but not after 30 days (AUC=0.7). Multiple regression study indicated that high-acuity screening, qSOFA score, higher BNP, and ED crowds independently predicted admission. However, MEESSI high-risk categorisation, higher creatinine, and older age independently predicted the 30-day composite outcome. Conclusion: Well-known CDRs, notably those based on the MEESSI score, accurately predicted clinical outcomes in Jordanian tertiary care emergency departments for severe HF patients. These techniques may assist standardise risk ranking, make decisions, and optimise resources.