Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment

Abstract

BackgroundDisruption of the autonomic nervous system (ANS) can lead to acute coronary syndrome (ACS). We developed a nomogram model using heart rate variability (HRV) and other data to predict major adverse cardiovascular events (MACEs) following emergency coronary angiography in patients with ACS.MethodsACS patients admitted from January 2018 to June 2020 were examined. Holter monitors were used to collect HRV data for 24 h. Coronary angiograms, clinical data, and MACEs were recorded. A nomogram was developed using the results of Cox regression analysis.ResultsThere were 439 patients in a development cohort and 241 in a validation cohort, and the mean follow-up time was 22.80 months. The nomogram considered low-frequency/high-frequency ratio, age, diabetes, previous myocardial infarction, and current smoking. The area-under-the-curve (AUC) values for 1-year MACE-free survival were 0.790 (95% CI: 0.702–0.877) in the development cohort and 0.894 (95% CI: 0.820–0.967) in the external validation cohort. The AUCs for 2-year MACE-free survival were 0.802 (95% CI: 0.739–0.866) in the development cohort and 0.798 (95% CI: 0.693–0.902) in the external validation cohort. Development and validation were adequately calibrated and their predictions correlated with the observed outcome. Decision curve analysis (DCA) showed the model had good discriminative ability in predicting MACEs.ConclusionOur validated nomogram was based on non-invasive ANS assessment and traditional risk factors, and indicated reliable prediction of MACEs in patients with ACS. This approach has potential for use as a method for non-invasive monitoring of health that enables provision of individualized treatment strategies.

Predicting long-term prognosis after percutaneous coronary intervention in patients with new onset ST-elevation myocardial infarction: development and external validation of a nomogram model

“…As noted elsewhere, Zheng et al [25] constructed a simple-to-use nomogram, integrating three clinical features that are both straightforward to obtain and routinely collected in clinical practice, evaluating cardiac mortality in non-ST-segment elevation ACS patients after PCI. Consistently, emerging evidence demonstrates that a prediction nomogram considering five conventional clinical features that are easy to obtain in daily clinical work in cardiovascular risk assessment can provide clinicians with a non-invasive and simple method for assessing MACE risk in patients with ACS who are scheduled for emergency coronary angiography [26]. Undeniably, single-modality data alone cannot systematically and comprehensively depict the degree of coronary atherosclerosis in patients, especially for new-onset STEMI patients who have undergone emergency complete and successful revascularization.…”

“…Therefore, approaches to understanding and managing ACS have evolved from focusing on "vulnerable plaques" to "vulnerable patients" who are suffering from an atheromatous-plaque burden, metabolic disturbance, as well as inflammatory and oxidative stress responses leading to plaque disruption to build a platform for better risk stratification for ACS patients [22]. Systematic ACS risk assessment is a process for identifying individuals who are at an increased risk of an adverse outcome, and such assessment could inform clinical decision-making by identifying high-risk patients who may require extra care and resources [22,[26][27][28]. Notably, residual cardiovascular risk is a persisting clinical problem that limits the benefit some patients can receive from the long-term success of PCI [26-27, 29−30].…”