Abstract

BackgroundThe prognostic ability of the temporal changes in resting heart rate (ΔHR) in patients with acute myocardial infarction (AMI) for cardiovascular (CV) mortality and clinical outcomes is rarely examined. This study investigated the predictive value of ΔHR using models with SYNTAX score II (SxS-II) for the long-term prognosis of patients with AMI.MethodsSix hundred five AMI patients with vital signs recorded at the first outpatient visit (2–4 weeks after discharge) were retrospectively recruited into this study. The changes between discharge and outpatient resting heart rate (D-O ΔHR) were calculated by subtracting the HR at the first post-discharge visit from the value recorded at discharge. The major adverse cardiovascular and cerebrovascular events (MACCE) include cardiovascular death, recurrent myocardial infarction, revascularization, and nonfatal stroke. The predictive values and reclassification ability of the different models were assessed using a likelihood ratio test, Akaike’s information criteria (AIC), receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsDuring the follow-up period, a drop-in resting heart rate (RHR) from discharge to first outpatient visit was independently associated with less risk of CV mortality [D-O ΔHR: hazards ratio (HR) = 0.97, 95% CI = 0.96–0.99, P < 0.001] and MACCE (HR = 0.98, 95% CI = 0.97–0.99, p = 0.001). The likelihood test indicated that the combined model of SxS-II and D-O ΔHR yielded the lowest AIC for CV mortality and MACCE (P < 0.001). Moreover, D-O ΔHR alone significantly improved the net reclassification and integrated discrimination of the models containing SxS-II for CV mortality and MACCE (CV mortality: NRI = 0.5600, P = 0.001 and IDI = 0.0759, P = 0.03; MACCE: NRI = 0.2231, P < 0.05 and IDI = 0.0107, P < 0.05).ConclusionsThe change in D-O ΔHR was an independent predictor of long-term CV mortality and MACCE. The D-O ΔHR combined with SxS-II could significantly improve its predictive probability.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.