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

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). 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). 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). 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. 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.