Analysis of risk factors and development of predictive model for acute myocardial injury in patients with acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND

Acute myocardial injury (AMI) is a common complication in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), often leading to a worse prognosis. Identifying modifiable risk factors for AMI in AECOPD is essential for improving outcomes, but these factors remain unclear. This study aims to explore the risk factors of AMI in AECOPD patients and identify those at high risk.

METHODS

In this study, 437 inpatients with AECOPD were enrolled from the Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, between January 2018 and October 2023. The AECOPD patients were divided into non-myocardial injury (non-MI) and AMI groups. Demographic information, clinical characteristics, electrocardiogram (ECG), and laboratory tests were collected. Univariate logistic regression analysis and the least absolute shrinkage and selection operator (LASSO) were used to identify essential variables for multivariable logistic regression, which was then used to develop a nomogram. The nomogram was internally validated using the bootstrap method. The receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA) were employed to evaluate this nomogram's discrimination, calibration, and clinical usefulness.

RESULTS

Compared with the non-MI group, the AMI group had an older age, a faster heart rate, a more severe Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade, and a higher prevalence of hypercapnic respiratory failure (HRF), hypertension, arrhythmia, ischemic ECG changes, cardiac medications usage, serum electrolyte imbalances, and even acute kidney injury. Additionally, this group demonstrated significantly higher admission rates to the respiratory intensive care unit (RICU) and in-hospital mortality. High-sensitivity cardiac troponin I (hs-TnI) levels were positively correlated with the risks of in-hospital mortality, RICU admission, and HRF (P<0.001). Furthermore, the AMI group showed elevated levels of inflammatory markers, such as the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and neutrophil-to-lymphocyte ratio (NLR) (P<0.001). Conversely, hemoglobin, serum albumin, calcium ions, and chloride ions were significantly lower in this group (P<0.001). Multivariate logistic regression analysis identified seven independent risk factors for AMI in AECOPD patients: cardiac medications usage, ischemic ECG changes, age, HRF, heart rate, blood urea nitrogen (BUN), and serum calcium. The nomogram model achieved an area under the ROC curve (AUC) of 0.8882 [95% confidence interval (CI): 0.8525-0.9238] and demonstrated good internal validation (bootstrapped AUC =0.8885). The Hosmer-Lemeshow test had a nonsignificant P value of 0.16. Moreover, the DCA curves show that the model provided valuable clinical utility.

CONCLUSIONS

Age, use of cardiac medications, ischemic ECG changes, HRF, heart rate, BUN, and serum calcium ions were independently associated with AMI in AECOPD patients. A clinical risk assessment model of AMI was developed based on independent risk factors.