Pokhrel Bhattarai Sunita, Dzikowicz Dillon J, Xue Ying, Block Robert, Tucker Rebecca G, Carey Mary G
J Emerg Nurs. 2025 Mar 31. doi: 10.1016/j.jen.2025.03.002.
Despite advances in echocardiography and biomarkers, the pathophysiological complexities among heart failure categories remain incompletely understood. This study analyzed patients' characteristics across heart failure with reduced ejection fraction, heart failure with midrange ejection fraction, and heart failure with preserved ejection fraction presenting at the emergency department.
This is a retrospective analysis of 954 patients with acute heart failure (2016-2023) using electronic health records. Data were collected from patient triage and the emergency department and during hospitalization. Survival analysis was performed using Kaplan-Meier estimates, and an elastic net model was used to handle multicollinearity and high dimensionality in predictor variables.
Patients (median age, 71 years) were categorized as heart failure with reduced ejection fraction (n = 363), heart failure with midrange ejection fraction (n = 131), and heart failure with preserved ejection fraction (n = 460). Patients with heart failure with preserved ejection fraction were older (80 vs 77 vs 74 years; P < .001). Heart failure with reduced ejection fraction showed higher prevalence of cardiomegaly, pleural effusion, and orthopnea (34% and 51%; P < .001), elevated diastolic blood pressure (P < .001), creatinine, N-terminal pro-B-type natriuretic peptide (P < .001), and hematocrit differences (P < .05) than heart failure with preserved ejection fraction. Echocardiographic measures differed significantly across subtypes. In-hospital prediction models achieved an area under the curve of 0.84 (91% sensitivity, 50% specificity); 30-day models had an area under the curve of 0.80 (98% sensitivity, 50% specificity).
HF subtypes exhibit distinct clinical and biomarker profiles. Emergency nurses' recognition of these differences during initial assessment may enhance risk stratification and tailored interventions (eg, prioritizing diuretics in heart failure with reduced ejection fraction, managing comorbidities in heart failure with preserved ejection fraction), improving outcomes. Integrating subtype-specific data into protocols could optimize emergency department care, particularly during prolonged boarding.
尽管超声心动图和生物标志物取得了进展，但心力衰竭各类型之间的病理生理复杂性仍未完全了解。本研究分析了因射血分数降低的心力衰竭、射血分数中等范围的心力衰竭和射血分数保留的心力衰竭而到急诊科就诊的患者特征。
这是一项对954例急性心力衰竭患者（2016 - 2023年）使用电子健康记录进行的回顾性分析。数据从患者分诊、急诊科以及住院期间收集。使用Kaplan - Meier估计进行生存分析，并使用弹性网模型处理预测变量中的多重共线性和高维度问题。
患者（中位年龄71岁）被分类为射血分数降低的心力衰竭（n = 363）、射血分数中等范围的心力衰竭（n = 131）和射血分数保留的心力衰竭（n = 460）。射血分数保留的心力衰竭患者年龄更大（80岁对77岁对74岁；P <.001）。与射血分数保留的心力衰竭相比，射血分数降低的心力衰竭患者心脏扩大、胸腔积液和端坐呼吸的患病率更高（34%和51%；P <.001），舒张压升高（P <.001）、肌酐、N末端B型利钠肽原升高（P <.001）以及血细胞比容存在差异（P <.05）。超声心动图测量在各亚型之间存在显著差异。院内预测模型的曲线下面积为0.84（敏感性91%，特异性50%）；30天模型的曲线下面积为0.80（敏感性98%，特异性50%）。
心力衰竭亚型表现出不同的临床和生物标志物特征。急诊护士在初始评估期间识别这些差异可能会加强风险分层和针对性干预（例如，在射血分数降低的心力衰竭中优先使用利尿剂，在射血分数保留的心力衰竭中管理合并症），从而改善预后。将亚型特异性数据纳入方案可以优化急诊科护理，特别是在长时间候诊期间。
