An Easy-to-Use Risk Stratification System for NSTE-ACS Patients Combining Autonomic Nervous System and Coronary Physiology

Abstract

Background: The evaluation of autonomic nervous system (ANS) function and coronary physiology through quantitative flow ratio (QFR) analysis provides a precise method for assessing the severity and prognosis of acute coronary syndrome (ACS). Aims: This study aimed to develop and validate a risk score model for predicting the long-term prognosis of non-ST-elevation ACS (NSTE-ACS) patients who underwent complete and successful percutaneous coronary intervention (PCI). Methods: NSTE-ACS patients who underwent complete and successful PCI with preoperative and postoperative QFR measurements between January 2018 and December 2020 in our medical center were included. 24-hour Holter monitoring was performed to assess deceleration capacity (DC) and heart rate variability (HRV) parameters. The primary endpoint was the occurrence of major adverse cardiac events (MACEs). Results: The training cohort consisted of 271 patients, while the testing cohort consisted of 119 patients. The nomogram considered diabetes, normalized low-frequency (nLF) power/normalized high-frequency (nHF) power, DC, cardiac troponin I (cTnI), post-PCI QFR of the target vessel. The model demonstrated excellent discriminative ability, with area under the curve (AUC) values of 0.874 (95% CI: 0.809-0.939) for 1-year MACE prediction in the training cohort and 0.893 (95% CI: 0.808-0.978) in the testing cohort. For 2-year MACE prediction, the AUC values were 0.882 (95% CI: 0.822-0.942) and 0.842 (95% CI: 0.724-0.960) in the training and testing cohorts. Conclusions: We successfully developed and validated a risk stratification system that integrates baseline clinical characteristics (diabetes, cTnI levels), ANS parameters (nLF/nHF ratio, DC), and coronary physiological assessment (post-PCI QFR). This model effectively predicts MACEs in NSTE-ACS patients following PCI, providing valuable prognostic information for clinical decision-making.

Keywords: autonomic nervous system; major adverse cardiac events; non-st-elevation ACS; quantitative flow ratio; risk stratification system.

Figure 1

Hazard ratios for major adverse cardiovascular events (MACEs) in patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) who underwent percutaneous coronary intervention (PCI). The blue line represents the 95% confidence intervals for hazard ratio, the blue dots represent the hazard ratio values of each variable, and there is a vertical line at x=1. The blue line does not intersect the dashed line representing that the 95% confidence intervals for the hazard ratio of the variables do not contain 1, the p-value was less than 0.05 and the variables were statistically significant for patient outcomes. nLF/nHF, ratio of normalized low-frequency to normalized high-frequency; DC, deceleration capacity; PCI, percutaneous coronary intervention; QFR, quantitative flow ratio; cTnI, cardiac troponin I.

Figure 2

Nomogram for predicting the probability of MACEs in NSTE-ACS patients after PCI. (A) Each of the five clinical characteristics (diabetes, nLF/nHF, DC, cTnI, post-PCI QFR of target vessel) was assigned points by drawing a vertical line from its value to the top row. Number of points for each clinical characteristic is in the first line. The presence of characteristics is associated with a number of points generated employing the nomogram. The points for each characteristic are summed together to generate a total-points score. The total points correspond to the 1-year and 2-year probabilities of MACEs-free survival by drawing a vertical line to the bottom two rows. (B) A simple-to-use online dynamic nomogram for real-time calculation of Balance Score (https://nste-acs.shinyapps.io/BalanceS/). nLF/nHF, ratio of normalized low-frequency to normalized high-frequency; DC, deceleration capacity; QFR, quantitative flow ratio; cTnI, cardiac troponin I; MACEs, Major adverse cardiovascular events.

Figure 3

ROC analysis was performed to assess the accuracy of the nomogram for predicting 1- and 2- year MACEs-free survival in the training cohort and testing cohort. (A) 1-year ROC analysis of the accuracy of the nomogram in predicting MACEs-free survival in the training cohort; (B) 1-year ROC analysis of the accuracy of the nomogram in predicting MACEs-free survival in the testing cohort; (C) 2-year ROC analysis of the accuracy of the nomogram in predicting MACEs-free survival in the training cohort; (D) 2-year ROC analysis of the accuracy of the nomogram in predicting MACEs-free survival in the testing cohort.

Figure 4

Calibration curves for the prediction of the risk for 1- and 2- year MACEs-free survival in the training cohort and testing cohort. (A)Calibration plot of the 1-year MACEs-free survival in the training cohort; (B)Calibration plot of the 1-year MACEs-free survival in the testing cohort; (C)Calibration plot of the 2-year MACEs-free survival in the training cohort; (D)Calibration plot of the 2-year MACEs-free survival in the testing cohort.

Figure 5

DCA for predicting 1- and 2- year MACEs-free survival in the training cohort and testing cohort. (A) DCA for predicting 1-year MACEs-free survival in the training cohort; (B) DCA for predicting 1-year MACEs-free survival in the testing cohort; (C) DCA for predicting 2-year MACEs-free survival in the training cohort; (D) DCA for predicting 2-year MACEs-free survival in the testing cohort.

Figure 6

Central illustration. The integrated approach that incorporates multiple-modality data from baseline characteristics (Clinical text data, laboratory analyses and imaging data) and autonomic nervous system assessment were linked to risk stratification and non-invasively predict cardiovascular events. This model is easy-to-use and straightforward provides clinicians with a non-invasive and simple method for assessing the risk of MACEs in patients with NSTE-ACS.