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The Association and Predictive Ability of ECG Abnormalities with Cardiovascular Diseases: A Prospective Analysis Cover

The Association and Predictive Ability of ECG Abnormalities with Cardiovascular Diseases: A Prospective Analysis

Open Access
|Sep 2020

Figures & Tables

Table 1

The Minnesota codes of the electrocardiographic abnormalities.

Electrocardiographic abnormalities Possible myocardial infarctionMinnesota Codes
Moderate Q/QS waves without ST-depression or T-wave inversion1-2-1 to 1-2-7 without 4-1, 4-2, 5-1, and 5-2
Minor Q/QS waves with ST-depression or T-wave inversion1-2-8 or 1-3-1 to 1-3-6 with 4-1, 4-2, 5-1, or 5-2
Probable myocardial infarction
Major Q/QS waves1-1-1 to 1-1-7
Moderate Q/QS waves with ST-depression or T-wave inversion1-2-1 to 1-2-7 with 4-1, 4-2, 5-1, or 5-2
Q-QS wave abnormalities1-1-1 to 1-2-8
Left ventricular hypertrophy3-1
Wolff Parkinson White syndrome6-4
Bundle branch block or intraventricular block7-1, 7-2, 7-4 or 7-8
Atrial fibrillation or atrial flutter8-3
ST-T changes
ST-depression4-1 or 4-2
T-wave inversion5-1 or 5-2
Minor ST- codes4-3 or 4-4
Minor T-wave codes5-3 or 5-4
Table 2

Baseline characteristics of participants with or without ECG abnormalities.

CharacteristicsTotal (N = 7872)ECG abnormalities
No (N = 5402)Yes (N = 2470)P value
Age, years57.8 ± 9.456.9 ± 9.159.7 ± 9.8<0.001
Women, n (%)4899 (62.2)3477 (64.4)1422 (57.6)<0.001
Education, n (%)<0.001
      Illiteracy962 (12.3)557 (10.4)405 (16.5)
      Primary1864 (23.8)1231 (22.9)633 (25.7)
      Secondary4726 (60.3)3370 (62.7)1356 (55.1)
      Post-secondary285 (3.6)220 (4.1)65 (2.6)
Currently smoking, n (%)1612 (21.1)1066 (20.4)546 (22.7)0.018
Currently drinking, n (%)816 (10.7)518 (9.9)298 (12.4)0.001
Physical activity, MET-mins/week0.378
      <600, n (%)3142 (40.5)2178 (40.9)964 (39.5)
      600–1499, n (%)2262 (29.1)1553 (29.2)709 (29.1)
      ≥1500, n (%)2361 (30.4)1595 (29.9)766 (31.4)
Body mass index, kg/m224.9 ± 3.324.9 ± 3.325.0 ± 3.30.545
Systolic blood pressure, mmHg141.0 ± 20.0138.7 ± 19.3145.8 ± 20.8<0.001
Diastolic blood pressure, mmHg82.6 ± 10.482.1 ± 10.283.8 ± 10.7<0.001
HbA1c, %5.8 ± 0.95.8 ± 0.95.9 ± 1.00.002
Total cholesterol, mg/dL208.8 ± 38.7204.9 ± 38.7208.8 ± 38.70.057
LDL cholesterol, mg/dL123.7 ± 34.8123.7 ± 34.8123.7 ± 30.90.707
HDL cholesterol, mg/dL50.3 ± 11.650.3 ± 11.650.3 ± 11.60.004
ACEI use, n (%)438 (5.6)283 (5.2)155 (6.3)0.063
Hypertension, n (%)4651 (59.1)2950 (54.6)1701 (68.9)<0.001
Diabetes, n (%)651 (8.3)453 (8.4)198 (8.0)0.574
10-year ASCVD risk ≥10%, n (%)1609 (21.1)945 (18.1)664 (27.7)<0.001

[i] Abbreviations: ECG, Electrocardiographic; MET, metabolic equivalent; HbA1c, glycated hemoglobin A1c; LDL, low-density lipoprotein; HDL, high-density lipoprotein; ACEI, angiotensin-converting enzyme inhibitors; ASCVD, atherosclerotic cardiovascular diseases.

Data are means ± SD for continuous variables and numbers (percentages) for categorical variables.

Table 3

Associations of ECG abnormalities at baseline with the development of cardiovascular events during follow-up.

Without ECG abnormalitiesWith ECG abnormalitiesHazard Ratio (95% CI)
Events (n, %)Incidence rate per 1000 person-years (95% CI)Events (n, %)Incidence rate per 1000 person-years (95% CI)Adjusted for TCVRFs*Adjusted for multivariables
Overall participants (n = 7872)
CVD270 (5.0)10.9 (9.7, 12.3)194 (7.9)17.1 (14.8, 19.7)1.25 (1.03, 1.51)1.25 (1.02, 1.53)
      MI or CHD death23 (0.4)0.9 (0.6, 1.4)29 (1.2)2.5 (1.7, 3.6)1.83 (1.04, 3.21)2.06 (1.15, 3.70)
      Stroke248 (4.6)10.0 (8.8, 11.3)167 (6.8)14.7 (12.6, 17.1)1.19 (0.97, 1.46)1.16 (0.95, 1.44)
Participants with ASCVD risk ≥10% (n = 1609)
CVD103 (10.9)24.5 (20.2, 29.7)113 (17.0)38.1 (31.7, 45.8)1.45 (1.11, 1.91)1.45 (1.08, 1.95)
      MI or CHD death13 (1.4)3.0 (1.7, 5.1)21 (3.2)6.7 (4.4, 10.3)2.01 (1.01, 4.06)2.29 (1.09, 4.81)
      Stroke91 (9.6)21.6 (17.6, 26.5)94 (14.2)31.5 (25.7, 38.5)1.39 (1.03, 1.86)1.41 (1.04, 1.90)
Participants with ASCVD risk <10% (n = 6007)
CVD157 (3.7)7.9 (6.8, 9.3)79 (4.6)9.8 (7.9, 12.2)1.09 (0.83, 1.44)1.10 (0.82, 1.48)
      MI or CHD death10 (0.2)0.5 (0.3, 0.9)8 (0.5)1.0 (0.5, 2.0)1.60 (0.62, 4.13)1.84 (0.69, 4.88)
      Stroke147 (3.4)7.4 (6.3, 8.7)71 (4.1)8.8 (7.0, 11.1)1.06 (0.79, 1.41)1.00 (0.75, 1.34)

[i] Abbreviations: ECG, Electrocardiographic; CI, confidence interval; TCVRFs, traditional cardiovascular risk factors; MI, Myocardial infarction; CHD, coronary heart disease; ASCVD, atherosclerotic cardiovascular diseases.

* Adjusted for traditional cardiovascular risk factors used to calculate the 10-year ASCVD risk score including age, sex, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, smoking, and diabetes.

Adjusted for TCVRFs and education, drinking, physical activity, low-density lipoprotein cholesterol, BMI, HbA1c and ACEI use.

Of the total participants, 256 participants with missing data on any of the traditional risk factors were not included in the separate analysis in different ASCVD risk subgroups.

Table 4

The predictive abilities of ECG abnormalities for the development of cardiovascular events.

ModelsC statistic (95% CI)C statistic change (95% CI)IDI (95% CI)NRI (95% CI)
OverallNon-eventsEvents
Overall participants (n = 7616)*
Model A0.699 (0.674, 0.723)0.002 (–0.003, 0.006)0.002 (0.001, 0.006)–0.031 (–0.044, 0.046)0.007 (–0.004, 0.026)–0.038 (–0.054, 0.036)
Model B0.701 (0.677, 0.725)
Participants with ASCVD risk ≥10% (n = 1609)
Model A0.587 (0.548, 0.626)0.014 (–0.001, 0.035)0.007 (0.001, 0.019)0.060 (–0.022, 0.132)0.080 (0.002, 0.195)–0.020 (–0.096, 0.036)
Model B0.601 (0.563, 0.639)
Participants with ASCVD risk <10% (n = 6007)
Model A0.630 (0.595, 0.665)0.004 (–0.007, 0.013)0.000 (0.000, 0.001)–0.012 (–0.054, 0.068)0.002 (–0.008, 0.012)–0.014 (–0.049, 0.065)
Model B0.634 (0.599, 0.669)

[i] Abbreviations: ECG, Electrocardiographic; CI, confidence interval; IDI, integrated discrimination improvement; NRI, net reclassification index; ASCVD, atherosclerotic cardiovascular diseases.

Model A used traditional CVD risk factors used to calculate the 10-year ASCVD risk score including age, sex, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, smoking, and diabetes as predictors.

Model B used traditional CVD risk factors in model A plus ECG abnormalities as predictors.

* Participants with missing data on any of the traditional risk factors were excluded for the assessment of the additional value of ECG.

Figure 1

Reclassification of individuals by adding the ECG results to the ASCVD risk predicted model with traditional risk factors*.

Abbreviations: ECG, Electrocardiography; HDL, high-density lipoprotein; ASCVD, atherosclerotic cardiovascular diseases; CVD, cardiovascular diseases.

* The traditional risk factors included age, sex, smoking, systolic blood pressure, diabetes, total cholesterol and HDL cholesterol.

Numbers are proportions of participants being reclassified.

Figure 2

Calibration plots of the models with and without ECG abnormalities.

Abbreviations: ASCVD, atherosclerotic cardiovascular diseases; ECG, Electrocardiographic.

Data points indicate expected vs observed risk by deciles of predicted risk. The bars showed the 95% confidential interval of the observed risks. The dotted lines correspond to the lines of perfect calibration on which predicted risks coincide with the observed risks.

Model A used traditional CVD risk factors used to calculate the Framingham Risk Score and the 10-year ASCVD risk score including age, sex, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, smoking, and diabetes as predictors.

Model B used traditional CVD risk factors in model A plus ECG abnormalities as predictors.

DOI: https://doi.org/10.5334/gh.790 | Journal eISSN: 2211-8179
Language: English
Submitted on: Mar 20, 2020
Accepted on: Jul 31, 2020
Published on: Sep 1, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Jingya Niu, Chanjuan Deng, Ruizhi Zheng, Min Xu, Jieli Lu, Tiange Wang, Zhiyun Zhao, Yuhong Chen, Shuangyuan Wang, Meng Dai, Yu Xu, Weiqing Wang, Guang Ning, Yufang Bi, Mian Li, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.