Frequency and Significance of High-Degree Atrioventricular Block and Sinoatrial Node Dysfunction in Patients With

Naoki Misumida1, Gbolahan O Ogunbayo1, Sun Moon Kim1

  • 1Division of Cardiovascular Medicine, Gill Heart Institute, University of Kentucky, Lexington, KY and VA Medical Center, Lexington, Kentucky.

Insights

Sinoatrial node dysfunction (SND) is more common after non-ST-elevation myocardial infarction (NSTEMI) than high-degree AV block (HDAVB). NSTEMI patients with HDAVB had higher mortality and pacemaker implantation rates compared to STEMI patients.

Area of Science:

  • Cardiology
  • Internal Medicine
  • Clinical Research

Background:

  • Conduction disorders post-STEMI are well-documented.
  • Limited data exist on conduction disorders following NSTEMI, including their incidence, treatment, and prognosis.

Purpose of the Study:

  • To compare the characteristics and outcomes of conduction disorders in STEMI versus NSTEMI patients.
  • To investigate treatment trends and prognostic impact of conduction disorders after NSTEMI.

Main Methods:

  • Utilized the National Inpatient Sample database (2010-2014).
  • Compared STEMI (135,468 patients) and NSTEMI (281,928 patients) cohorts.
  • Classified conduction disorders into high-degree AV block (HDAVB) and sinoatrial node dysfunction (SND).

Main Results:

  • SND was more frequent than HDAVB in NSTEMI (0.9% vs 0.6%), while HDAVB predominated in STEMI (2.4% vs 0.5%).
  • NSTEMI patients with HDAVB had higher permanent pacemaker implantation rates (30.0% vs 11.6%) and in-hospital mortality (16.6% vs 3.8%) than STEMI patients.
  • Pacemaker implantation rates for SND were similar between STEMI and NSTEMI cohorts (33.9% vs 30.5%).

Conclusions:

  • SND is more prevalent than HDAVB in NSTEMI, unlike STEMI where HDAVB is more common.
  • HDAVB after NSTEMI suggests lower spontaneous resolution rates and necessitates pacemaker implantation in about one-third of cases.
  • Conduction disorders, particularly HDAVB, portend a worse prognosis in NSTEMI patients.

Related Concept Videos

Freezing Point Depression and Boiling Point Elevation03:12

Freezing Point Depression and Boiling Point Elevation

Boiling Point Elevation
The boiling point of a liquid is the temperature at which its vapor pressure is equal to ambient atmospheric pressure. Since the vapor pressure of a solution is lowered due to the presence of nonvolatile solutes, it stands to reason that the solution’s boiling point will subsequently be increased. Vapor pressure increases with temperature, and so a solution will require a higher temperature than will pure solvent to achieve any given vapor pressure, including one...
40.2K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
853
Degrees of Freedom01:02

Degrees of Freedom

The degree of freedom for a particular statistical calculation is the number of values that are free to vary. Thus, the minimum number of independent numbers can specify a particular statistic. The degrees of freedom differ greatly depending on known and uncalculated statistical components.
For example, suppose there are three unknown numbers whose mean is 10; although we can freely assign values to the first and second numbers, the value of the last number can not be arbitrarily assigned.
7.2K
Degrees of Freedom01:02

Degrees of Freedom

The degree of freedom for a particular statistical calculation is the number of values that are free to vary. As a result, the minimum number of independent numbers can specify a particular statistic. The degrees of freedom differ greatly depending on known and uncalculated statistical components.
For example, suppose there are three unknown numbers whose mean is 10; although we can freely assign values to the first and second numbers, the value of the last number can not be arbitrarily...
10.3K