Abstract
Aims
The coronavirus disease 2019 (COVID‐19) has spread rapidly around the globe, causing significant morbidity and mortality. This study aims to describe electrocardiographic (ECG) characteristics of COVID‐19 patients and to identify ECG parameters that are associated with cardiac involvement.
Methods and results
The study included patients who were hospitalized with COVID‐19 diagnosis and had cardiac biomarker assessments and simultaneous 12‐lead surface ECGs. Sixty‐three hospitalized patients (median 53 [inter‐quartile range, 43–65] years, 76.2% male) were enrolled, including patients with (n = 23) and without (n = 40) cardiac injury. Patients with cardiac injury were older, had more pre‐existing co‐morbidities, and had higher mortality than those without cardiac injury. They also had prolonged QTc intervals and more T wave changes. Logistic regression model identified that the number of abnormal T waves (odds ratio (OR), 2.36 [95% confidence interval (CI), 1.38–4.04], P = 0.002) and QTc interval (OR, 1.31 [95% CI, 1.03–1.66], P = 0.027) were independent indicators for cardiac injury. The combination model of these two parameters along with age could well discriminate cardiac injury (area the under curve 0.881, P < 0.001) by receiver operating characteristic analysis. Cox regression model identified that the presence of T wave changes was an independent predictor of mortality (hazard ratio, 3.57 [1.40, 9.11], P = 0.008) after adjustment for age.
Conclusions
In COVID‐19 patients, presence of cardiac injury at admission is associated with poor clinical outcomes. Repolarization abnormalities on surface ECG such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in further risk stratification.
Keywords: COVID‐19, Heart injury, ECG, Repolarization, Clinical outcome
Introduction
The coronavirus disease 2019 (COVID‐19) pandemic has caused millions of infectious cases and considerable mortality. 1 Many studies have described the clinical characteristics of patients with COVID‐19. 2 , 3 , 4 , 5 , 6 , 7 Cardiac injury has been observed as a common complication, ranging from 10% to 30% among hospitalized cases, 3 , 4 , 8 , 9 exacerbating severity and mortality of the disease. Monitoring of cardiac involvement in patients with COVID‐19 is of utmost importance to evaluate the progression of disease and its prognosis.
Surface electrocardiographic (ECG) findings have not been systematically reported in COVID‐19, except for in some case studies describing representative ECGs of special cases. 10 , 11 , 12 The evaluation of cardiac involvement by measuring serum cardiac biomarkers has often been reserved for high‐risk individuals or suspected cases. An association between surface ECG characteristics and cardiac injury, if confirmed, can be of clinical relevance, because ECG can be used as a non‐invasive monitoring strategy to reflect a possible cardiac involvement in patients with COVID‐19. In the present study, we aimed to describe ECG characteristics of COVID‐19 patients and to identify ECG parameters that are associated with cardiac involvement.
Methods
Study participants
This single‐centre, observational study included laboratory confirmed COVID‐19 patients who were treated at the Jinyintan Hopital in Wuhan, China, between 1 January 2020 and 27 February 2020. The cases who had no cardiac biomarker testing or surface ECG were excluded. This study complied with the Declaration of Helsinki and was approved by the Institutional Review Board of the Jinyintan Hospital. Written informed consent was provided from all patients.
Clinical data collection
The clinical data and 12‐lead surface ECG were simultaneously collected during the hospitalization (by J. T., W. H., and P. Z.). Serum cardiac biomarkers were collected including high‐sensitivity troponin I (hs‐TnI), myohaemoglobin, and creatinine kinase‐myocardial band at the time of ECG recordings for these patients. Cardiac injury was defined as at least one elevated cardiac biomarker (above the 99th percentile upper reference limit). The ECGs were evaluated by two experts (F. D. and M. C.) who were blinded to the patient groups. Abnormal T wave was defined as T wave inversion (TWI), isoelectric, or biphasic T wave. The degree of T wave abnormality was determined as prominent TWI, mild TWI, or isoelectric T wave. Treatment and outcome data were recorded during hospitalization (by N. H.). The clinical outcomes were defined as hospital discharge or death.
Statistical analysis
Continuous variables were presented as median (inter‐quartile range [IQR]) values. Comparisons between two groups were performed by Mann–Whitney U test or Fisher's exact test, as appropriate. Paired Student's t‐test was used to compare paired samples between the two groups. Spearman test was used for correlation analysis between hs‐cTnI and number of abnormal T waves. Logistic regression analysis with odds ratio (OR) and 95% confidence intervals (CIs) was used to determine the independent factors reflecting cardiac injury. Receiver operating characteristic (ROC) with area the under curve (AUC) analyses were used to assess the prediction value with optimal sensitivity and specificity. Multivariable Cox regression with hazard ratio (HR) and 95% CI was used to evaluate the predictors for mortality.
Results
Patient characteristics and laboratory findings
The study included 63 patients (53 [IQR, 43–65] years, 15 [23.8%] female) including 23 patients (36.5%) with cardiac injury and 40 patients (63.5%) without cardiac injury. The patient characteristics were presented in Table 1 . Compared with patients without cardiac injury, patients with cardiac injury were older and more often had baseline co‐morbidities such as hypertension, coronary heart disease, arrhythmias, and cancer. In addition to significantly elevated cardiac biomarkers, patients with cardiac injury also showed higher leukocytes count, aspartate transaminase, high‐sensitivity C‐reaction protein, D‐dimer, serum ferritin, and lactic dehydrogenase. Renal function, lipid, and interleukin 6 (IL6) were comparable between two groups (Table 1 ).
Table 1.
Baseline characteristics and laboratory findings of 63 patients with coronavirus disease 2019
| Characteristics | Total (n = 63) | Without cardiac injury (n = 40) | With cardiac injury (n = 23) | P value |
|---|---|---|---|---|
| Female | 15 (23.8) | 9 (22.5) | 6 (26.1) | 0.750 |
| Age, years | 53 (43, 65) | 48 (39, 62) | 61 (49, 75) | 0.007 |
| Systolic pressure, mmHg | 124 (115, 140) | 125.5 (119, 141.5) | 120 (110, 136) | 0.123 |
| Diastolic pressure, mmHg | 77 (70, 85) | 78.5 (70, 84) | 72 (66, 87) | 0.597 |
| Signs and symptoms at admission, n (%) | ||||
| Fever | 56 (88.9) | 34 (85) | 22 (95.7) | 0.199 |
| Cough | 47 (74.6) | 28 (70) | 19 (82.6) | 0.272 |
| Chest tightness | 29 (46) | 19 (47.5) | 10 (43.5) | 0.760 |
| Palpitation | 1 (1.6) | 1 (2.5) | 0 (0) | 1.000 |
| Dyspnoea | 25 (39.7) | 14 (35) | 11 (47.8) | 0.320 |
| Fatigue | 18 (28.6) | 9 (22.5) | 9 (39.1) | 0.163 |
| Sputum | 18 (28.6) | 11 (27.5) | 7 (30.4) | 0.805 |
| Muscle ache | 5 (7.9) | 3 (7.5) | 2 (8.7) | 0.867 |
| Diarrhoea | 8 (12.7) | 5 (12.5) | 3 (13) | 1.000 |
| Chest pain | 2 (3.2) | 2 (5) | 0 (0) | 0.529 |
| Headache | 3 (4.8) | 3 (7.5) | 0 (0) | 0.293 |
| Sore throat | 4 (6.3) | 4 (10) | 0 (0) | 0.287 |
| Hypertension | 17 (27) | 7 (17.5) | 10 (43.5) | 0.027 |
| Diabetes | 11 (17.5) | 6 (15) | 5 (21.7) | 0.732 |
| Coronary heart disease | 4 (6.3) | 0 (0) | 4 (17.4) | 0.015 |
| Arrhythmias | 5 (7.9) | 1 (2.5) | 4 (17.4) | 0.055 |
| COPD | 4 (6.3) | 2 (5) | 2 (8.7) | 0.566 |
| Cancer | 3 (4.8) | 0 (0) | 3 (13) | 0.045 |
| Laboratory findings at admission, median (IQR) | ||||
| SpO2 (%) | 95 (89, 98) | 97 (94, 98) | 92 (84, 95) | 0.001 |
| Leukocytes, /μL | 6.36 (4.16, 9.83) | 5.63 (3.44, 8.12) | 8.69 (5.14, 11.26) | 0.008 |
| Erythrocytes, /μL | 4.41 (4.02, 4.74) | 4.44 (4.04, 4.92) | 4.26 (3.92, 4.61) | 0.558 |
| Haemoglobin, g/dL | 132 (122, 141) | 131.5 (123, 141) | 132 (118, 142) | 0.869 |
| Platelets, /μL | 176 (133, 272) | 187 (134.5, 278.5) | 166 (132, 229) | 0.617 |
| Lymphocytes, /μL | 0.84 (0.59, 1.25) | 0.88 (0.56, 1.33) | 0.83 (0.63, 1.08) | 0.842 |
| Creatine, μmol/L | 71.9 (64, 84.6) | 77.7 (66.3, 87) | 68.7 (52.5, 84.2) | 0.109 |
| eGFR, ×mL/(min × 1.73 m2) | 103.52 (86.37, 123.33) | 101.79 (84.42, 121.87) | 113.15 (86.51, 148.21) | 0.226 |
| Albumin, g/L | 33.3 (29.2, 37.4) | 35.75 (31.65, 38.9) | 31.4 (28.6, 35.3) | 0.036 |
| Total bilirubin, μmol/L | 13.05 (9.9, 17.3) | 13.2 (10, 17) | 12.9 (9.5, 18) | 0.965 |
| AST, U/L | 40 (26, 49) | 34.5 (25.5, 43.5) | 45 (30, 55) | 0.012 |
| ALT, U/L | 32.5 (23, 43) | 32 (23, 43) | 33 (20, 53) | 0.748 |
| Total cholesterol, mmol/L | 3.88 (3.15, 4.63) | 3.98 (3.18, 4.55) | 3.73 (3.01, 4.68) | 0.743 |
| Triglyceride, mmol/L | 1.17 (0.96, 1.76) | 1.14 (0.96, 1.76) | 1.21 (0.94, 1.86) | 0.867 |
| LDL‐C, mmol/L | 2.29 (1.75, 2.65) | 2.36 (1.91, 2.68) | 2.21 (1.51, 2.61) | 0.173 |
| HDL‐C, mmol/L | 0.98 (0.81, 1.17) | 0.96 (0.82, 1.13) | 1.13 (0.78, 1.26) | 0.526 |
| hs‐CRP, mg/L | 26.7 (7.35, 69.7) | 17.1 (4, 71.5) | 47.1 (21.9, 67.9) | 0.027 |
| PaO2, kPa | 9.36 (7.81, 14.08) | 9.12 (7.75, 13.57) | 9.36 (8.24, 14.16) | 0.605 |
| D‐dimer, μg/mL | 0.79 (0.36, 2.97) | 0.48 (0.26, 1.66) | 2.11 (0.55, 22.47) | 0.002 |
| Serum ferritin, ng/mL | 757.85 (420.33, 1280.95) | 523.65 (300.83, 808.35) | 1035.7 (787.29, 2000) | 0.001 |
| ESR, mm/h | 41 (28, 53.7) | 39.25 (27.65, 48.05) | 48.2 (31, 66) | 0.075 |
| Interleukin 6, pg/mL | 8.68 (6.65, 11.2) | 8.68 (6.73, 11.92) | 8.73 (6.32, 10.93) | 0.706 |
| hs‐TnI, pg/mL | 6.1 (2.6, 14.4) | 3.55 (1.1, 8.75) | 13.1 (5.7, 79) | <0.001 |
| Myohaemoglobin, ng/mL | 71 (34.7, 139.7) | 52.1 (30.8, 76.6) | 147.65 (77.7, 230.1) | <0.001 |
| BNP, pg/mL | 36.3 (12.6, 85) | 27.4 (3, 50.9) | 73.6 (32.1, 324.25) | 0.007 |
| Creatinine kinase, U/L | 96 (67, 175) | 80.5 (65.5, 133.5) | 139 (67, 349) | 0.074 |
| CK‐MB, U/L | 15 (12, 19) | 15 (11, 17) | 18 (14, 26) | 0.005 |
| Lactic dehydrogenase, U/L | 352 (236, 473) | 276.5 (217, 417) | 416 (327, 631) | 0.001 |
ALT, alanine aminotransferase; AST, aspartate transaminase; BNP, B‐type natriuretic peptide; CK‐MB, creatinine kinase‐myocardial band; COPD, chronic obstructive pulmonary disease; eGFR, estimated glomerular filtration rate; ESR, erythrocyte sedimentation rate; HDL‐C, high density lipoprotein cholesterol; hs‐CRP, high‐sensitivity C‐reaction protein; LDL‐C, low density lipoprotein cholesterol
Clinical course and outcomes
Table 2 shows the treatment, complications, and clinical outcomes. Compared with patients without cardiac injury, patients with cardiac injury required more glucocorticoids and non‐invasive ventilation and showed more frequent hypoproteinaemia and acute respiratory distress syndrome, and more often progressed to severe condition. As a consequence, the mortality rate was higher among patients with vs. without cardiac injury (12 [52.2%] vs. 5 [12.5%], P = 0.001).
Table 2.
Treatment, complications, and clinical outcome of patients with coronavirus disease 2019
| Characteristics | Total (n = 63) | Without cardiac injury (n = 40) | With cardiac injury (n = 23) | P value |
|---|---|---|---|---|
| Treatment | ||||
| Antiviral drugs | 57 (90.5) | 35 (87.5) | 22 (95.7) | 0.402 |
| Antibiotics | 60 (95.2) | 38 (95) | 22 (95.7) | 1.000 |
| Glucocorticoids | 33 (52.4) | 17 (42.5) | 16 (69.6) | 0.040 |
| Immunoglobulin therapy | 14 (22.2) | 8 (20) | 6 (26.1) | 0.579 |
| Oxygen inhalation | 37 (73.0) | 27 (67.5) | 19 (82.6) | 0.197 |
| Non‐invasive ventilation | 17 (27) | 6 (15) | 11 (47.8) | 0.005 |
| Invasive ventilation | 1 (1.6) | 0 (0) | 1 (4.3) | 0.365 |
| Complications | ||||
| ARDS | 15 (23.8) | 7 (17.5) | 8 (34.8) | 0.124 |
| Acute kidney injury | 2 (3.2) | 1 (2.5) | 1 (4.3) | 1.000 |
| Hypoproteinaemia | 18 (28.6) | 7 (17.5) | 11 (47.8) | 0.011 |
| Anaemia | 4 (6.3) | 2 (5) | 2 (8.7) | 0.619 |
| Hypoxaemia | 23 (36.5) | 12 (30) | 11 (47.8) | 0.160 |
| Outcome | ||||
| Critical condition | 32 (50.8) | 13 (32.5) | 19 (82.6) | <0.001 |
| Death | 17 (27) | 5 (12.5) | 12 (52.2) | 0.001 |
Electrocardiographic characteristics
Most patients with COVID‐19 showed abnormal ECG features, such as sinus tachycardia (17.5%), abnormal T waves (48.3%), and ST segment changes (7.9%) (Table 3 ). Compared with patients without cardiac injury, patients with cardiac injury showed prolonged QTc interval (452 [423, 479] vs. 428 [407.5, 439.5], P = 0.006), more abnormal T wave leads (2 [1, 2] vs. 0 [0, 1], P < 0.001), and more severe T wave alterations (P = 0.002). In addition, sinus tachycardia (7 [30.4%] vs. 4 [10.0%], P = 0.041) and Q wave (4 [17.4%] vs. 1 [2.5%], P = 0.037) were more often observed among patients with vs. without cardiac injury. Figure 1A shows representative ECGs from patients with and without cardiac injury.
Table 3.
Electrocardiographic characteristics of patients with coronavirus disease 2019
| Characteristics | Total (n = 63) | Without cardiac injury (n = 40) | With cardiac injury (n = 23) | P value |
|---|---|---|---|---|
| Heart rate, b.p.m. | 80 (71, 89) | 77 (68, 86.5) | 85 (77, 101) | 0.018 |
| PR interval, ms | 150 (139, 165) | 155 (139, 164) | 147 (139, 166) | 0.597 |
| QRS duration, ms | 92 (86, 99) | 92 (87, 100) | 89 (84, 95) | 0.155 |
| QT interval, ms | 372 (354, 402) | 373 (355, 403) | 369 (354, 402) | 0.911 |
| QTc, ms | 432.5 (413, 452) | 428 (407.5, 439.5) | 452 (423, 479) | 0.006 |
| Sinus tachycardia | 11 (17.5) | 4 (10.0) | 7 (30.4) | 0.041 |
| Branch bundle block | 5 (7.9) | 4 (10) | 1 (4.3) | 0.644 |
| ST segment changes | 5 (7.9) | 3 (7.5) | 2 (8.7) | 1.000 |
| Q wave | 5 (7.9) | 1 (2.5) | 4 (17.4) | 0.037 |
| Abnormal T waves (≥1 lead) | 29 (48.3) | 11 (28.9) | 18 (81.8) | <0.001 |
| Severity of abnormal T wave a | 0.002 | |||
| Prominent T wave | 9 (15) | 4 (10.5) | 5 (22.7) | |
| Mild TWI | 10 (16.7) | 4 (10.5) | 6 (27.3) | |
| Isoelectric T wave | 10 (16.7) | 3 (7.9) | 7 (31.8) | |
| Normal | 31 (51.7) | 27 (71.1) | 4 (18.2) | |
| No. of abnormal T waves | 0 (0, 2) | 0 (0, 1) | 2 (1, 2) | <0.001 |
Abnormal T wave was defined as T wave inversion (TWI), isoelectric, or biphasic T wave.
Figure 1.
T wave changes associated with cardiac injury among patients with coronavirus disease 2019 (COVID‐19). (A) Representative electrocardiogram (ECG) from patients with (upper panel, female, 70 years old, QTc 459 ms) and without (lower panel, 30 years, QTc 423 ms) cardiac injury. (B) Spearman correlation analysis between number of abnormal T waves and serum high‐sensitivity troponin I (hs‐TnI) concentrations. Receiver operating characteristic (ROC) curve in discriminating cardiac injury by number of abnormal T waves (C), QTc interval (D), and combined model of age, number of abnormal T waves and QTc interval with a sensitivity of 77.3% and specificity of 83.3% (E).
Electrocardiographic and cardiac injury
We enrolled a logistic regression model to identify indicators of cardiac injury among ECG features as well as routine laboratory blood tests (Table 4 ). After adjustment for age, baseline co‐morbidities, leukocyte counts, albumin, and aspartate transaminase, both number of abnormal T waves (OR, 2.36 [95% CI, 1.38–4.04], P = 0.002) and QTc intervals (OR, 1.31 [95% CI, 1.03–1.66], P = 0.027) were independent predictors for cardiac injury. Spearman test validated positive correlation between number of leads with abnormal T waves and hs‐cTnI levels among patients with COVID‐19 (r = 0.66, P < 0.0001), which suggests the extent of abnormal T waves leads reflecting the severity of cardiac injury (Figure 1B ). ROC curve analysis confirmed that both QTc and number of leads with abnormal T waves could discriminate cardiac injury with AUC of 0.716 (P = 0.006) and 0.798 (P < 0.001), respectively (Figure 1C, D ). The discrimination reached 0.881 (AUC) as we used a combined model of age, number of leads with abnormal T waves, and QTc interval (P < 0.001), with a sensitivity of 77.3% and specificity of 83.3% (Figure 1E ).
Table 4.
Logistic regression to identify cardiac injury using electrocardiographic features and routine laboratory parameters
| Variables | OR (95% CI) | P value |
|---|---|---|
| Age (per 10 years) | 1.64 (1.09–2.47) | 0.017 |
| No. of abnormal T waves | 2.36 (1.38–4.04) | 0.002 |
| QTc (per 10 ms) | 1.31 (1.03–1.66) | 0.027 |
| Q wave | — | 0.059 |
| Sinus tachycardia | — | 0.740 |
| Hypertension | — | 0.275 |
| Coronary heart disease | — | 0.199 |
| Leukocytes | — | 0.349 |
| Albumin | — | 0.859 |
| Aspartate transaminase | — | 0.204 |
Multivariate Cox regression analysis indicated that age and cardiac injury were independent predictors of mortality in patients with COVID‐19. When we included age and ECG parameters into the Cox regression model, presence of T wave changes was still an independent predictor (HR = 3.57 [1.40, 9.11], P = 0.008) for mortality when adjusted by age (Table 5 ).
Table 5.
Multivariable Cox regression prediction of mortality in patients with coronavirus disease 2019
| Variables | Hazard ratio (95% confidential interval) | P value |
|---|---|---|
| Age + clinical variables | ||
| Age (per 10 years) | 1.77 (1.27, 2.47) | 0.001 |
| Cardiac injury | 8.77 (2.41, 31.95) | 0.001 |
| Hypertension | — | 0.537 |
| Coronary artery disease | — | 0.469 |
| COPD | — | 0.193 |
| Cancer | — | 0.897 |
| SpO2 | — | 0.114 |
| Age + ECG parameters | ||
| Age (per 10 years) | 1.86 (1.37, 2.53) | <0.001 |
| Abnormal T waves ≥ 2 | 3.57 (1.40, 9.11) | 0.008 |
| QTc ≥ 448 ms | — | 0.090 |
| ST segment changes | — | 0.885 |
| Bundle branch block | — | 0.924 |
| Q wave | — | 0.407 |
| Heart rate | — | 0.723 |
Dynamic alterations of electrocardiogram
In order to investigate the dynamic alterations of ECG along with the progression of cardiac injury among patients with COVID‐19, we enrolled a subgroup of eight patients who developed cardiac injury during hospitalization and had serial ECG examinations from baseline to the time of cardiac injury. Our results showed that QTc intervals and the number of leads with abnormal T waves were significantly increased (P = 0.001 and P = 0.001, respectively), along with elevation of cardiac injury biomarkers hs‐TnI and myohaemoglobin, in parallel to the progression of cardiac involvement (Figure 2A ). In addition, after patients completely recovering, the abnormal T waves of some patients were able to return back to normal as shown in one patient with comparable ECGs at admission and discharged from hospital (Figure 2B ). These results validated that ECG alterations in patients with COVID‐19 were associated with the presence of cardiac injury, and there were dynamic changes during disease progression.
Figure 2.
Dynamic changes of electrocardiogram (ECG). (A) The ECG parameters and cardiac biomarker alterations from baseline to the time of cardiac injury (compared by paired Student's t‐test). (B) Representative ECG from one patient (male, 65 years old) during cardiac injury (upper panel) and recovery state (lower panel).
Discussion
Cardiac injury associated with prognosis
Cardiac injury was commonly reported in patients with COVID‐19, with a prevalence ranging from 10% to 30% at hospital admission. 8 , 9 Patients with cardiac injury had a considerably high mortality rate (more than 50%), compared with the ~2.3% among the overall disease population. 8 , 9 , 13 Our study has also revealed a similar mortality rate in patients with cardiac injury (52.2%), much higher than the mortality rate in patient without cardiac injury (12.5%). Our Cox regression analysis also demonstrated the independent role of cardiac injury in predicting mortality. Therefore, it is of utmost importance to detect cardiac involvement in patients with COVID‐19 and a 12‐lead surface ECG can help for identifying these patients. Other clinical conditions such as old age and the presence of chronic obstructive pulmonary disease were also reported to be associated with higher mortality in patients with COVID‐19. 9
Surface electrocardiogram and cardiac injury
Abnormal ECG features such as ST segment changes and presence of Q wave and bundle branch block were observed in a minority of cases in our cohort, which were also reported by previous case series. 10 , 14 In contrast, T wave changes were common findings with a prevalence of 48.3% in the whole study cohort. The number of leads with abnormal T waves was significantly higher among patients with vs. without cardiac injury, and this was also significantly correlated with serum hs‐cTnI levels. In addition, patients with cardiac injury had longer QTc intervals, and none of these patients received chloroquine or hydroxychloroquine therapy that might have induced QT prolongation. 15 We further demonstrated that T wave changes along with QTc interval and age could be used as a substantial predictor of cardiac injury with good discrimination. Therefore, considering that cardiac biomarkers are not routinely tested in clinical practice, ECG can be of clinical importance in determining cardiac involvement in COVID‐19 patients. As old age with presence of cardiovascular co‐morbidities may be a confounder for QTc and T wave changes, the dynamic alterations of ECG from baseline to progressive disease are clinically more applicable for monitoring cardiac involvement in patients with COVID‐19.
Potential mechanism underlying cardiac injury
The mechanisms underlying cardiac injury caused by SARS‐CoV‐2 remain unknown. A newly proposed theory has focused on endothelial involvement causing microvascular dysfunction across different organ systems. 16 Our recent study has revealed that cardiac pericytes (a perivascular cell type that wrap around capillaries) have high expression of angiotensin‐converting enzyme 2 (ACE2), the putative viral receptor and, therefore, may serve as the target cell of SARS‐CoV‐2, 17 which was later validated by an independent study. 18 Therefore, pericyte injury due to virus infection may result in capillary endothelial dysfunction, thus inducing microvascular dysfunction. 17 The representative signature of T wave alterations among COVID‐19 patients with cardiac injury was also previously demonstrated to be a predictor of coronary microvascular dysfunction in patients with non‐obstructive disease. 19
This study was a retrospective, observational study and included only a limited number of cases. ECG findings from larger multicentre cohorts are warranted to investigate the role of ECG in predicting cardiac injury and prognosis in COVID‐19.
In summary, presence of cardiac injury in COVID‐19 patients is associated with poor clinical outcome. ECG repolarization abnormalities such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in identifying these patients. Larger multicentre cohorts are warranted to investigate the role of ECG findings in predicting cardiac injury in COVID‐19.
Conflict of interest
None declared.
Funding
This work was supported by the National Natural Science Foundation of China (81872673 to C.X.), grants from the National Key Research and Development Program of China (2017YFC1200203 to C.X.), and by the COVID‐19 research projects of Fudan University (to M.C.).
Acknowledgements
The authors would like to thank Dr. Zhenghua Zhang for his assistant in data collection and all participants in the study cohort.
Chen, L. , Feng, Y. , Tang, J. , Hu, W. , Zhao, P. , Guo, X. , Huang, N. , Gu, Y. , Hu, L. , Duru, F. , Xiong, C. , and Chen, M. (2020) Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement. ESC Heart Failure, 7: 4408–4415. 10.1002/ehf2.12991.
Liang Chen, Yi Feng, and Jia Tang contributed equally to this work.
Contributor Information
Firat Duru, Email: firat.duru@usz.ch.
Chenglong Xiong, Email: xiongchenglong@fudan.edu.cn.
Mingquan Chen, Email: mingquanchen@fudan.edu.cn.
References
- 1. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W. China Novel Coronavirus I, Research TA novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020; 382: 727–733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID‐19 in Wuhan, China: a retrospective cohort study. Lancet 2020; 395: 1054–1062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, Yu T, Wang Y, Pan S, Zou X, Yuan S, Shang Y. Clinical course and outcomes of critically ill patients with SARS‐CoV‐2 pneumonia in Wuhan, China: a single‐centered, retrospective, observational study. Lancet Respir Med 2020; 8: 475–481. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y, Li Y, Wang X, Peng Z. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus‐infected pneumonia in Wuhan, China. JAMA 2020; 323: 1061–1069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: 497–506. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, Ma K, Xu D, Yu H, Wang H, Wang T, Guo W, Chen J, Ding C, Zhang X, Huang J, Han M, Li S, Luo X, Zhao J, Ning Q. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ 2020; 368: m1091. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Guan W‐J, Ni Z‐Y, Hu Y, Liang W‐H, Ou C‐Q, He J‐X, Liu L, Shan H, Lei C‐L, Hui DSC, Du B, Li L‐J, Zeng G, Yuen K‐Y, Chen R‐C, Tang C‐L, Wang T, Chen P‐Y, Xiang J, Li S‐Y, Wang J‐L, Liang Z‐J, Peng Y‐X, Wei L, Liu Y, Hu Y‐H, Peng P, Wang J‐M, Liu J‐Y, Chen Z, Li G, Zheng Z‐J, Qiu S‐Q, Luo J, Ye C‐J, Zhu S‐Y, Zhong N‐S. China Medical Treatment Expert Group for CClinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020; 382: 1708–1720. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Guo T, Fan Y, Chen M, Wu X, Zhang L, He T, Wang H, Wan J, Wang X, Lu Z. Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID‐19). JAMA Cardiol 2020; 5: 811. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, Gong W, Liu X, Liang J, Zhao Q, Huang H, Yang B, Huang C. Association of cardiac injury with mortality in hospitalized patients with COVID‐19 in Wuhan, China. JAMA Cardiol 2020; 5: 802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. He J, Wu B, Chen Y, Tang J, Liu Q, Zhou S, Chen C, Qin Q, Huang K, Lv J, Chen Y, Peng D. Characteristic electrocardiographic manifestations in patients with COVID‐19. Can J Cardiol 2020; 36: 966.e1–966.e4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Doyen D, Moceri P, Ducreux D, Dellamonica J. Myocarditis in a patient with COVID‐19: a cause of raised troponin and ECG changes. Lancet 2020; 395: 1516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Bangalore S, Sharma A, Slotwiner A, Yatskar L, Harari R, Shah B, Ibrahim H, Friedman GH, Thompson C, Alviar CL, Chadow HL, Fishman GI, Reynolds HR, Keller N, Hochman JS. ST‐Segment elevation in patients with COVID‐19—a case series. N Engl J Med 2020; 382: 2478–2480. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID‐19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020. 10.1001/jama.2020.2648 [DOI] [PubMed] [Google Scholar]
- 14. Fried JA, Ramasubbu K, Bhatt R, Topkara VK, Clerkin KJ, Horn E, Rabbani L, Brodie D, Jain SS, Kirtane A, Masoumi A, Takeda K, Kumaraiah D, Burkhoff D, Leon M, Schwartz A, Uriel N, Sayer G. The variety of cardiovascular presentations of COVID‐19. Circulation 2020; 141: 1930–1936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Kochi AN, Tagliari AP, Forleo GB, Fassini GM, Tondo C. Cardiac and arrhythmic complications in patients with COVID‐19. J Cardiovasc Electrophysiol 2020; 31: 1003–1008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Varga Z, Flammer AJ, Steiger P, Haberecker M, Andermatt R, Zinkernagel AS, Mehra MR, Schuepbach RA, Ruschitzka F, Moch H. Endothelial cell infection and endotheliitis in COVID‐19. Lancet 2020; 395: 1417–1418. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Chen L, Li X, Chen M, Feng Y, Xiong C. The ACE2 expression in human heart indicates new potential mechanism of heart injury among patients infected with SARS‐CoV‐2. Cardiovasc Res 2020; 116: 1097–1100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Nicin L, Abplanalp WT, Mellentin H, Kattih B, Tombor L, John D, Schmitto JD, Heineke J, Emrich F, Arsalan M, Holubec T, Walther T, Zeiher AM, Dimmeler S. Cell type‐specific expression of the putative SARS‐CoV‐2 receptor ACE2 in human hearts. Eur Heart J 2020; 41: 1804–1806. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Sara JD, Sugrue A, Kremen V, Qiang B, Sapir Y, Attia ZI, Ackerman MJ, Friedman PA, Lerman A, Noseworthy PA. Electrocardiographic predictors of coronary microvascular dysfunction in patients with non‐obstructive coronary artery disease: utility of a novel T wave analysis program. Int J Cardiol 2016; 203: 601–606. [DOI] [PubMed] [Google Scholar]