1. Insights into the Importance of the Electrocardiogram in
Patients with Acute Heart Failure
Pishoy Gouda1, Brian H. Rowe1, Finlay A. McAlister 1, Paul Armstrong1, Mohua Podder1, Justin A. Ezekowitz 1
University of Alberta, Edmonton, Canada
Introduction
Table 1. ECG Diagnostic Criteria
Table 3. Prevalence of ECG characteristics
Table 5. Relationship between Unadjusted 1-year mortality and ECG characteristics
ECG Criteria
PR Interval
Prolonged if >200ms
QRS
Prolonged if >120ms
QTc
Prolonged if >450ms
Atrial Fibrillation
Absolutely irregular RR interval with no distinct P waves
T wave inversion
Drop >0.1mV below the baseline*
Left ventricular hypertrophy
Either Sokolow Lyons or Cornell criteria**
Left bundle branch block
QRS >120ms and a R2 peak in leads V5, V6, I, II or aVL
Right bundle branch block
QRS >120ms and a R2 peak in leads V1 or V2
Q waves
Pathological if >40ms and deeper than ¼ the following R wave
ST segment
+/- 0.1mv or greater in amplitude 40ms following the J point.*
Prevalence - N (%)
Normal ECG
71 (8)
Sinus Rhythm
495 (54) AF
325 (35)
RBBB
70 (8)
LBBB
124 (13)
LVH
219 (24)
ST Elevation/Depression
170 (18)
T-wave inversion
329 (46)
PR >200ms (385 missing)
132 (25)
QRS >120ms
334 (36)
QTc >450ms
632 (69) *
Heart Failure (HF) is a life threatening condition with an estimated prevalence of 2% in the adult population.
In Canada, HF is responsible for over acute presentations annually with an in hospital mortality rate of up to 10%.
The electrocardiogram (ECG) is a component of the standardized diagnostic workup of patients with suspected HF.
ECG abnormalities are common among patients with acute HF, however their prognostic relationship remains unclear.
* P value <0.05 * *
Objective
To describe the ECG characteristics of the acute HF population and their associated risks of short term (90 days) and long term (1-year) mortality.
*In the absence of LBBB, LVH and paced rhythms
**In the absence of LBBB and paced rhythms
Table 4. Relationship between Unadjusted 90-day mortality and ECG characteristics
Results
Methods
Table 2. Baseline Characteristics
Total ECG Cohort
N = 922
Abnormal ECG
N = 851
Normal ECG
N = 71
P-value
Age: med (q1,q3)
77 ( )
78 ( )
71 ( )
0.005
Male: n (%)
503 (55)
475 (56)
28 (39)
0.008
EMS: n (%)
460 (51)
423 (51)
37 (54)
0.650
Prior HF history: n (%)
Valvular
182 (20)
171 (20)
11 (16)
0.349
Ischemic
360 (39)
331 (39)
29 (41)
0.746
Hypertensive
436 (47)
395 (46)
41 (58)
0.066
Medical History:
CAD
482 (52)
450 (53)
32 (45)
0.206
Device (ICD, CRT, Pacemaker)
158 (17)
154 (18)
4 (6)
0.007
Prior EF (%)
Median (q1, q3) 45
( )
( )
0.631
First available laboratory readings in ED: median (q1, q3)
Troponin
(ng/ml)
0.10
( )
( )
0.565
BNP
(pg/ml)
1023
( )
1047.5
( )
798.5
( )
0.025
Design: Prospective cohort
Setting: 4 hospital emergency departments (ED) in Edmonton, Canada. Participants were recruited from June 2009 to November 2012.
Participants: 952 patients seen in ED with suspected acute HF were recruited in the Acute Heart Failure – Emergency Management cohort (AHF-EM)
Exclusion criteria: severe dementia, hemodialysis dependent, ACS or previous enrollment
Of the 952 participants, 922 (97%) had an ECG available.
AHF Diagnosis: Based on Boston Criteria, modified to include BNP.
ECG Interpretation: The first available ECG for each participant was read by a single investigator at a core lab, with a 1% quality assessment by a second investigator. ECGs were interpreted using modified Minnesota coding guidelines.
Statistics: Chi square was used to analyze association of ECG characteristics with all cause mortality.
Conclusion
* P value <0.05
ECG abnormalities are commonly seen in patients with acute HF.
While a completely normal ECG is linked to lower mortality, the presence of RBBB, Q waves and widened QRS are associated with increased risks of mortality.
Limitation of this study is the lack of adjustments for key clinical variables (age, sex, creatinine, EF).
Further work is required to determine if the ECG, using these cut points or as continuous variables, can be integrated into clinically useful risk models. *
Acknowledgments
This work was supported by grants from the Canadian Institutes of Health Research and Alberta Innovates Health Solutions.