Development and validation of an LDH-focused nomogram for the early prediction of heart failure in patients with acute ST-elevation myocardial infarction after percutaneous coronary intervention - Panting Wei; Jingjin Hou; Ruijiao Yu; Kun Na; Jiaqi He; Xiaojie Zhao; Ziqi Liu; Yan Xu; Yi Li; Chenghui Yan; Haibo Yu; Yaling Han (Lipids in Health and Disease)
Abstract Background ST-elevation myocardial infarction (STEMI) patients remain at significant risk of heart failure (HF) despite successful percutaneous coronary intervention (PCI) reperfusion, imposing a considerable public health burden. Lactate dehydrogenase (LDH), a key enzyme in glycolysis, functions as a notable biomarker of cardiac pathology, yet it is frequently often overlooked in clinical practice. This study focused on establishing a nomogram incorporating LDH levels to assess the probability of HF occurring within one year following PCI in individuals who have previously experienced STEMI. Methods 527 patients diagnosed with STEMI were initially included in this study. After excluding 71 patients with ejection fractions < 40% and other cardiovascular diseases, the final cohort of 456 patients with STEMI was recruited and randomly assigned to the training and validation sets. To identify potential risk factors linked to HF, both univariate and multivariate logistic regression analyses were conducted, leading to the development of a predictive nomogram. Model performance in terms of discrimination, calibration, and clinical usefulness was validated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analyses (DCA). Results Patients with STEMI who developed HF within one year exhibited higher LDH levels within 24 hours post-PCI. A nomogram model was constructed that included alcohol drinking, left anterior descending artery involvement, and creatine kinase-MB, log N-terminal pro b-type natriuretic peptide, and LDH levels. The area under the ROC curve (AUC) was 0.831 (95% CI: 0.7807–0.8805), and the DCA demonstrated that the model offered a considerable net benefit when the threshold probability varied between 1% and 97% within the training dataset. Additionally, LDH demonstrated excellent predictive capability for HF, with an AUC of 0.756 (95% CI: 0.706–0.806). It showed even better performance for HF with a reduced ejection fraction, with an AUC of 0.848 (95% CI: 0.7705–0.9249). Conclusions LDH independently predicts the development of HF within one year of PCI in patients with STEMI. The LDH-based nomogram demonstrated a robust predictive capability. It enables early identification and timely intervention in STEMI patients at an elevated risk of HF, which is crucial for reducing HF incidence and alleviating the associated healthcare burden.