A Nomogram Based on Lactate to Predict Major Adverse Cardiovascular Events for Acute Coronary Syndrome after Percutaneous Coronary Intervention

Abstract

Background Accurate prediction of major adverse cardiovascular events (MACE) is very important for the management of acute coronary syndrome (ACS) patients. We aim to develop and validate effective prognostic nomogram for individualized risk estimate of MACE in patients with ACS after percutaneous coronary intervention (PCI). Methods We conducted a prospective assessment of patients with ACS after PCI from January 2013 to July 2019 (n = 1986). Based on the training set, single-factor and multi-factor Cox proportional hazard analysis method was used to determine the results of single-factor and multi-factor Cox proportional hazard analysis. The receiver operating characteristic (ROC) and calibration curve were used to evaluate the prediction accuracy and discriminability, we have compared nomogram with the classical cardiovascular risk scores. In the validation set, X-tile analysis and Kaplan-Meier curve were used to evaluate the value of clinical application. Results Independent prognostic factors included lactate, age, left anterior descending branch (LAD) stenosis ≥ 50%, right coronary artery (RCA) stenosis ≥ 50%, brain natriuretic peptide (BNP), and left ventricular ejection fraction (LVEF). The area under the ROC curve (AUC) of the training group were about 0.712 to 0.762. In the validation set, the nomogram still shows good differentiation (AUC were about 0.724 to 0.818). On the calibration plot, the predicted values of the statistical chart agree well with the actual observed values. In addition, participants can be divided into two different risk groups (low and high) according to the nomogram.

Development of a novel tool: a nomogram for predicting in-hospital mortality of patients in intensive care unit after percutaneous coronary intervention

“…Second, it could help clinicians make better decisions. Recently, machine learning analysis was used to create a nomogram for predicting the prognosis of different patient cohorts [25,26]. The clinical value of these predictive models lies in the identification of high-risk patients on one hand and the improvement in patient management through personalized medicine on the other.…”