Department of Cardiovascular Medicine III, Baoji Traditional Chinese Medicine Hospital, Baoji, Shaanxi Province, China.
Medical Department, Baoji Vocational & Technical College, Baoji, Shaanxi Province, China.
Medicine (Baltimore). 2024 Nov 1;103(44):e40266. doi: 10.1097/MD.0000000000040266.
This study aims to compare the efficacy of single-indicator models versus comprehensive models in predicting cardiac deterioration events in patients with acute heart failure (AHF), providing a more precise predictive tool for clinical practice. This retrospective cohort study included 484 patients with AHF treated at our hospital between June 2018 and January 2023. Patients were categorized into a deterioration group and a non-deterioration group based on the occurrence of cardiac deterioration events within 1 year, defined as cardiogenic shock, cardiac arrest, or the need for mechanical circulatory support. We collected clinical data, laboratory markers, and imaging indicators for analysis. Both single-indicator models and comprehensive models (clinical data + indicators) were constructed and evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) to assess their predictive performance. Among the 484 AHF patients, 121 were in the deterioration group and 363 were in the non-deterioration group. Among the single indicators, WBC had the highest AUC of 0.683. The indicator model (WBC, NOMO, Cr, BUN, Troponin, NT-proBNP, D-Dimer, LVEF, and RVFAC) achieved an AUC of 0.886 in the training set and 0.876 in the validation set. The comprehensive model (age, time from onset to admission, heart failure type, WBC, NOMO, Cr, BUN, troponin, NT-proBNP, LA, D-dimer, fibrinogen, and RVFAC) had an AUC of 0.940 in the training set and 0.925 in the validation set. In the training set, the comprehensive model had a significantly higher AUC than the indicator model (P < .05), while no significant difference was observed between the 2 in the validation set (P > .05). Furthermore, decision curve analysis (DCA) and calibration curve analysis indicated that the comprehensive model provided greater clinical benefits and better predictive accuracy in clinical applications. The comprehensive model demonstrates superior predictive capability for cardiac deterioration events in AHF patients, significantly outperforming both single-indicator and indicator models. This suggests that a comprehensive assessment can more accurately identify high-risk patients, offering a more reliable basis for clinical decision-making.
这项研究旨在比较单项指标模型与综合模型在预测急性心力衰竭（AHF）患者心脏恶化事件中的疗效，为临床实践提供更精确的预测工具。这项回顾性队列研究纳入了 2018 年 6 月至 2023 年 1 月在我院治疗的 484 例 AHF 患者。根据 1 年内是否发生心脏恶化事件（定义为心源性休克、心脏骤停或需要机械循环支持）将患者分为恶化组和非恶化组。我们收集了临床数据、实验室标志物和影像学指标进行分析。构建并评估了单项指标模型和综合模型（临床数据+指标），使用接受者操作特征（ROC）曲线下面积（AUC）评估其预测性能。在 484 例 AHF 患者中，121 例为恶化组，363 例为非恶化组。在单项指标中，WBC 的 AUC 最高，为 0.683。指标模型（WBC、NOMO、Cr、BUN、肌钙蛋白、NT-proBNP、D-二聚体、LVEF 和 RVFAC）在训练集中的 AUC 为 0.886，在验证集中的 AUC 为 0.876。综合模型（年龄、发病至入院时间、心力衰竭类型、WBC、NOMO、Cr、BUN、肌钙蛋白、NT-proBNP、LA、D-二聚体、纤维蛋白原和 RVFAC）在训练集中的 AUC 为 0.940，在验证集中的 AUC 为 0.925。在训练集中，综合模型的 AUC 显著高于指标模型（P<.05），而在验证集中，两者无显著差异（P>.05）。此外，决策曲线分析（DCA）和校准曲线分析表明，综合模型在临床应用中提供了更大的临床获益和更好的预测准确性。综合模型对 AHF 患者心脏恶化事件具有优越的预测能力，显著优于单项指标和指标模型。这表明综合评估可以更准确地识别高危患者，为临床决策提供更可靠的依据。
