Abstract
BACKGROUND: Major adverse cardiac events (MACE) are more likely to occur when abnormal heart rate recovery (HRR). This study aimed to assess the incremental predictive significance of HRR over exercise stress myocardial perfusion single-photon emission computed tomography (MPS) results for MACE in individuals with suspected coronary artery disease (CAD). METHODS: Between January 2014 and December 2017, we continually gathered data on 595 patients with suspected CAD who received cycling exercise stress MPS. HRR at 1, 2, 3, and 4 min were used as study variables to obtain the optimal cut-off values of HRR for MACE. The difference between the peak heart rate achieved during exercise and the heart rate at 1, 2, 3, and 4 min was used to calculate the HRR, as shown in HRR3. Heart rate variations between two locations in time, such as HRR(2 min-1 min), were used to establish the slope of HRR. All patients were followed for a minimum of 4 years, with MACE as the follow-up goal. The associations between HRR and MACE were assessed using Cox proportional hazards analyses. RESULTS: Patients with MACE were older (P = 0.001), and they also had higher rates of hypertension, dyslipidemia, diabetes, abnormal MPS findings (SSS ≥ 5%), medication history (all P < 0.001), and lower HRR values (all P < 0.01). Patients with and without MACE did not significantly vary in their HRR(4 min-3 min). The optimal cut-off of HRR1, 2, and 3 combined with SSS can stratify the risk of MACE in people with suspected CAD (all P < 0.001). HRR 1, 2, and 3 and its slope were linked to MACE in multivariate analysis, where HRR3 was the most significant risk predictor. With a global X(2) increase from 101 to 126 (P < 0.0001), HRR3 demonstrated the greatest improvement in the model's predictive capacity, incorporating clinical data and MPS outcomes. CONCLUSIONS: HRR at 3 min has a more excellent incremental prognostic value for predicting MACE in patients with suspected CAD following cycling exercise stress MPS. Therefore, incorporating HRR at 3 min into known predictive models may further improve the risk stratification of the patients.