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

## TL;DR

This study creates a risk model for predicting heart issues in patients with a specific type of heart disease after a common treatment.

## Contribution

A new risk stratification system combining autonomic nervous system and coronary physiology for NSTE-ACS patients.

## Key findings

- The model predicted major cardiac events with high accuracy in both training and testing groups.
- It integrated diabetes, heart rate variability, and coronary flow measurements for improved prognosis.
- The system showed strong performance for 1- and 2-year event prediction after treatment.

## 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.