
Journal of Shanghai Jiao Tong University (Medical Science) ›› 2026, Vol. 46 ›› Issue (3): 322-331.doi: 10.3969/j.issn.1674-8115.2026.03.006
• Clinical research • Previous Articles
Quyang Danzeng, Xiao Huoyuan, Zhang Qingchen, Liu Yuting, Kang Sang, Feng Rui, Pan Jingwei(
)
Received:2025-08-13
Accepted:2026-02-13
Online:2026-03-28
Published:2026-03-30
Contact:
Pan Jingwei
E-mail:jwpan@sjtu.edu.cn
Supported by:CLC Number:
Quyang Danzeng, Xiao Huoyuan, Zhang Qingchen, Liu Yuting, Kang Sang, Feng Rui, Pan Jingwei. Diagnostic value of cardiac magnetic resonance for myocardial injury in patients with mild COVID-19 infection[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(3): 322-331.
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URL: https://xuebao.shsmu.edu.cn/EN/10.3969/j.issn.1674-8115.2026.03.006
| Variable | Overall (n=101) | Control group (n=37) | cTnI(+) group (n=26) | cTnI(-) group (n=38) | P value |
|---|---|---|---|---|---|
| Age/year | 32.00 (25.00, 41.00) | 32.00 (24.00, 42.00) | 29.50 (25.00, 38.75) | 36.50 (28.00, 40.75) | 0.273 |
| Male/n% | 49 (48.51) | 17 (45.95) | 19 (73.08) | 13 (34.21) | 0.009 |
| Time interval between symptom onset and CMR examination/n% | <0.001 | ||||
| 1 week | 32 (31.68) | 0 (0) | 18 (69.23) | 14 (36.84) | |
| 2 week | 12 (11.88) | 0 (0) | 5 (19.23) | 7 (18.42) | |
| 1 month | 16 (15.84) | 0 (0) | 3 (11.54) | 13 (34.21) | |
| 3 month | 2 (1.98) | 0 (0) | 0 (0) | 2 (5.26) | |
| 6 month | 2 (1.98) | 0 (0) | 0 (0) | 2 (5.26) | |
| Symptoms at presentation/n% | <0.001 | ||||
| Chest tightness | 11 (10.89) | 0 (0) | 5 (19.23) | 6 (15.79) | |
| Chest pain | 23 (22.77) | 0 (0) | 10 (38.46) | 13 (34.21) | |
| Palpitation | 14 (13.86) | 0 (0) | 2 (7.69) | 12 (31.58) | |
| Dizziness | 10 (9.90) | 0 (0) | 8 (30.77) | 2 (5.26) | |
| Fatigue | 4 (3.96) | 0 (0) | 1 (3.85) | 3 (7.89) | |
| Other symptoms | 2 (1.98) | 0 (0) | 0 (0) | 2 (5.26) | |
| Comorbidity/n% | <0.001 | ||||
| Positive | 5 (4.95) | 0 (0) | 3 (11.53) | 2 (5.26) | |
| Negative | 96 (95.05) | 37 (100.00) | 23 (88.46) | 36 (94.74) |
Tab 1 Comparison of baseline clinical characteristics among the three groups
| Variable | Overall (n=101) | Control group (n=37) | cTnI(+) group (n=26) | cTnI(-) group (n=38) | P value |
|---|---|---|---|---|---|
| Age/year | 32.00 (25.00, 41.00) | 32.00 (24.00, 42.00) | 29.50 (25.00, 38.75) | 36.50 (28.00, 40.75) | 0.273 |
| Male/n% | 49 (48.51) | 17 (45.95) | 19 (73.08) | 13 (34.21) | 0.009 |
| Time interval between symptom onset and CMR examination/n% | <0.001 | ||||
| 1 week | 32 (31.68) | 0 (0) | 18 (69.23) | 14 (36.84) | |
| 2 week | 12 (11.88) | 0 (0) | 5 (19.23) | 7 (18.42) | |
| 1 month | 16 (15.84) | 0 (0) | 3 (11.54) | 13 (34.21) | |
| 3 month | 2 (1.98) | 0 (0) | 0 (0) | 2 (5.26) | |
| 6 month | 2 (1.98) | 0 (0) | 0 (0) | 2 (5.26) | |
| Symptoms at presentation/n% | <0.001 | ||||
| Chest tightness | 11 (10.89) | 0 (0) | 5 (19.23) | 6 (15.79) | |
| Chest pain | 23 (22.77) | 0 (0) | 10 (38.46) | 13 (34.21) | |
| Palpitation | 14 (13.86) | 0 (0) | 2 (7.69) | 12 (31.58) | |
| Dizziness | 10 (9.90) | 0 (0) | 8 (30.77) | 2 (5.26) | |
| Fatigue | 4 (3.96) | 0 (0) | 1 (3.85) | 3 (7.89) | |
| Other symptoms | 2 (1.98) | 0 (0) | 0 (0) | 2 (5.26) | |
| Comorbidity/n% | <0.001 | ||||
| Positive | 5 (4.95) | 0 (0) | 3 (11.53) | 2 (5.26) | |
| Negative | 96 (95.05) | 37 (100.00) | 23 (88.46) | 36 (94.74) |
| Variable | Control group (n=37) | cTnI(+) group (n=26) | cTnI(-) group (n=38) | P value | |||
|---|---|---|---|---|---|---|---|
| All | Control vs cTnI(+) | Control vs cTnI(-) | cTnI(+) vs cTnI(-) | ||||
| LVEF/% | 63.60 | 62.24 | 63.61 | 0.672 | 0.269 | 0.996 | 0.447 |
| LVEDV/mL | 117.11 (89.39, 131.87) | 103.76 (87.43, 133.41) | 113.30 (93.08, 139.83) | 0.562 | 0.388 | 0.971 | 0.328 |
| LVESV/mL | 39.95 (34.87, 51.58) | 38.32 (35.14, 49.57) | 38.56 (31.85, 57.64) | 0.949 | 0.742 | 0.979 | 0.823 |
| LVSV/mL | 73.22 (59.30, 83.28) | 65.75 (53.35, 81.94) | 70.90 (59.03, 85.21) | 0.440 | 0.281 | 0.998 | 0.204 |
Tab 2 Comparison of left ventricular function and volumetric parameters among the three groups
| Variable | Control group (n=37) | cTnI(+) group (n=26) | cTnI(-) group (n=38) | P value | |||
|---|---|---|---|---|---|---|---|
| All | Control vs cTnI(+) | Control vs cTnI(-) | cTnI(+) vs cTnI(-) | ||||
| LVEF/% | 63.60 | 62.24 | 63.61 | 0.672 | 0.269 | 0.996 | 0.447 |
| LVEDV/mL | 117.11 (89.39, 131.87) | 103.76 (87.43, 133.41) | 113.30 (93.08, 139.83) | 0.562 | 0.388 | 0.971 | 0.328 |
| LVESV/mL | 39.95 (34.87, 51.58) | 38.32 (35.14, 49.57) | 38.56 (31.85, 57.64) | 0.949 | 0.742 | 0.979 | 0.823 |
| LVSV/mL | 73.22 (59.30, 83.28) | 65.75 (53.35, 81.94) | 70.90 (59.03, 85.21) | 0.440 | 0.281 | 0.998 | 0.204 |
| Variable | Control group (n=37) | cTnI(+) group (n=26) | cTnI(-) group (n=38) | P value | |||
|---|---|---|---|---|---|---|---|
| All | Control vs cTnI(+) | Control vs cTnI(-) | cTnI(+) vs cTnI(-) | ||||
| GRS/% | 30.20 (27.80, 39.01) | 30.24 (27.21, 43.19) | 32.62 (28.52, 37.93) | 0.990 | 0.933 | 0.344 | 0.880 |
| GCS/% | -20.87 | -17.38 | -20.27 | <0.001 | <0.001 | 0.352 | <0.001 |
| GLS/% | -14.68 | -13.48 | -13.86 | 0.288 | 0.153 | 0.911 | 0.621 |
| CSBasal/% | -18.86 | -15.18 | -17.46 | <0.001 | <0.001 | >0.999 | 0.008 |
| CSMid/% | -20.97 | -16.04 | -20.32 | <0.001 | <0.001 | 0.326 | <0.001 |
| CSApi/% | -23.14 (-24.56, -21.06) | -22.54 (-25.57, -19.88) | -24.81 (-27.47, -22.47) | 0.035 | 0.775 | 0.231 | 0.027 |
Tab 3 Comparison of global and segmental left ventricular strain parameters among the three groups
| Variable | Control group (n=37) | cTnI(+) group (n=26) | cTnI(-) group (n=38) | P value | |||
|---|---|---|---|---|---|---|---|
| All | Control vs cTnI(+) | Control vs cTnI(-) | cTnI(+) vs cTnI(-) | ||||
| GRS/% | 30.20 (27.80, 39.01) | 30.24 (27.21, 43.19) | 32.62 (28.52, 37.93) | 0.990 | 0.933 | 0.344 | 0.880 |
| GCS/% | -20.87 | -17.38 | -20.27 | <0.001 | <0.001 | 0.352 | <0.001 |
| GLS/% | -14.68 | -13.48 | -13.86 | 0.288 | 0.153 | 0.911 | 0.621 |
| CSBasal/% | -18.86 | -15.18 | -17.46 | <0.001 | <0.001 | >0.999 | 0.008 |
| CSMid/% | -20.97 | -16.04 | -20.32 | <0.001 | <0.001 | 0.326 | <0.001 |
| CSApi/% | -23.14 (-24.56, -21.06) | -22.54 (-25.57, -19.88) | -24.81 (-27.47, -22.47) | 0.035 | 0.775 | 0.231 | 0.027 |
| Variable | Control group (n=37) | cTnI(+) group (n=26) | cTnI(-) group (n=38) | P value | |||
|---|---|---|---|---|---|---|---|
| All | Control vs cTnI(+) | Control vs cTnI(-) | cTnI(+) vs cTnI(-) | ||||
| T2WI/n(%) | <0.001 | <0.001 | >0.999 | <0.001 | |||
| Negative | 37 (100.00) | 16 (61.54) | 37 (97.37) | ||||
| Positive | 0 (0) | 10 (38.46) | 1 (2.63) | ||||
| Native T1 mapping/ms | 1 216.90 (1 189.00, 1 238.69) | 1 268.90 (1 248.67, 1 317.00) | 1 235.49 (1 203.37, 1 264.05) | <0.001 | <0.001 | 0.007 | <0.001 |
| LGE/n(%) | <0.001 | <0.001 | <0.001 | <0.001 | |||
| Negative | 37 (100.00) | 2 (7.69) | 28 (73.68) | ||||
| Positive | 0 (0) | 24 (92.31) | 10 (26.32) | ||||
| LGE/% | / | 18.05 (14.43, 22.18) | 0.00 (0.00, 8.10) | / | / | / | <0.001 |
| LGE segment/n(%) | <0.001 | <0.001 | 0.004 | <0.001 | |||
| LAT | 0 (0) | 14 (58.33) | 3 (30.00) | ||||
| ANT | 0 (0) | 5 (20.83) | 5 (50.00) | ||||
| SEPT | 0 (0) | 4 (16.67) | 1 (10.00) | ||||
| Other segment | 0 (0) | 1 (4.17) | 1 (10.00) | ||||
| LGE location/n(%) | <0.001 | <0.001 | 0.003 | <0.001 | |||
| Mid-level | 37 (100.00) | 2 (8.33) | 4 (40.00) | ||||
| Basal-level | 0 (0) | 5 (20.83) | 3 (30.00) | ||||
| Apical-level | 0 (0) | 3 (12.50) | 0 (0) | ||||
| Basal-Mid | 0 (0) | 6 (25.00) | 3 (30.00) | ||||
| Mid-Apical | 0 (0) | 5 (20.83) | 0 (0) | ||||
| All-level | 0 (0) | 3 (12.50) | 0 (0) | ||||
| LGE pattern/n(%) | <0.001 | <0.001 | <0.001 | <0.001 | |||
| Mid-wall | 0 (0) | 5 (20.83) | 4 (40.00) | ||||
| Subepicardial | 0 (0) | 3 (12.50) | 1 (10.00) | ||||
| Mid-Subepi | 0 (0) | 15 (62.50) | 5 (50.00) | ||||
| Subendocardial | 0 (0) | 1 (4.17) | 0 (0) | ||||
Tab 4 Comparison of left ventricular myocardial tissue characterization parameters among the three groups
| Variable | Control group (n=37) | cTnI(+) group (n=26) | cTnI(-) group (n=38) | P value | |||
|---|---|---|---|---|---|---|---|
| All | Control vs cTnI(+) | Control vs cTnI(-) | cTnI(+) vs cTnI(-) | ||||
| T2WI/n(%) | <0.001 | <0.001 | >0.999 | <0.001 | |||
| Negative | 37 (100.00) | 16 (61.54) | 37 (97.37) | ||||
| Positive | 0 (0) | 10 (38.46) | 1 (2.63) | ||||
| Native T1 mapping/ms | 1 216.90 (1 189.00, 1 238.69) | 1 268.90 (1 248.67, 1 317.00) | 1 235.49 (1 203.37, 1 264.05) | <0.001 | <0.001 | 0.007 | <0.001 |
| LGE/n(%) | <0.001 | <0.001 | <0.001 | <0.001 | |||
| Negative | 37 (100.00) | 2 (7.69) | 28 (73.68) | ||||
| Positive | 0 (0) | 24 (92.31) | 10 (26.32) | ||||
| LGE/% | / | 18.05 (14.43, 22.18) | 0.00 (0.00, 8.10) | / | / | / | <0.001 |
| LGE segment/n(%) | <0.001 | <0.001 | 0.004 | <0.001 | |||
| LAT | 0 (0) | 14 (58.33) | 3 (30.00) | ||||
| ANT | 0 (0) | 5 (20.83) | 5 (50.00) | ||||
| SEPT | 0 (0) | 4 (16.67) | 1 (10.00) | ||||
| Other segment | 0 (0) | 1 (4.17) | 1 (10.00) | ||||
| LGE location/n(%) | <0.001 | <0.001 | 0.003 | <0.001 | |||
| Mid-level | 37 (100.00) | 2 (8.33) | 4 (40.00) | ||||
| Basal-level | 0 (0) | 5 (20.83) | 3 (30.00) | ||||
| Apical-level | 0 (0) | 3 (12.50) | 0 (0) | ||||
| Basal-Mid | 0 (0) | 6 (25.00) | 3 (30.00) | ||||
| Mid-Apical | 0 (0) | 5 (20.83) | 0 (0) | ||||
| All-level | 0 (0) | 3 (12.50) | 0 (0) | ||||
| LGE pattern/n(%) | <0.001 | <0.001 | <0.001 | <0.001 | |||
| Mid-wall | 0 (0) | 5 (20.83) | 4 (40.00) | ||||
| Subepicardial | 0 (0) | 3 (12.50) | 1 (10.00) | ||||
| Mid-Subepi | 0 (0) | 15 (62.50) | 5 (50.00) | ||||
| Subendocardial | 0 (0) | 1 (4.17) | 0 (0) | ||||
| Statistic | Gender | Time interval between symptom onset and CMR examination | T2WI | Native T1 mapping | GCS | CSBasal | CSMid |
|---|---|---|---|---|---|---|---|
| r | 0.383 | 0.370 | -0.466 | -0.422 | -0.462 | -0.366 | -0.600 |
| P | 0.003 | 0.003 | <0.001 | <0.001 | 0.001 | 0.003 | <0.001 |
Tab 5 Correlation analysis of cTnI with baseline clinical characteristics as well as key CMR parameters
| Statistic | Gender | Time interval between symptom onset and CMR examination | T2WI | Native T1 mapping | GCS | CSBasal | CSMid |
|---|---|---|---|---|---|---|---|
| r | 0.383 | 0.370 | -0.466 | -0.422 | -0.462 | -0.366 | -0.600 |
| P | 0.003 | 0.003 | <0.001 | <0.001 | 0.001 | 0.003 | <0.001 |
| Variable | Univariate Logistic regression analysis | Multivariate Logistic regression analysis | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| β | S.E | Z | P | OR (95% CI) | β | S.E | Z | P | OR (95% CI) | ||
| Male | -0.27 | 0.42 | -0.65 | 0.515 | 0.762 (0.336‒1.727) | ||||||
| Time interval between symptom onset and CMR examination | |||||||||||
| 1 week | 1.000 (Ref) | ||||||||||
| 2 week | 0.34 | 0.68 | 0.49 | 0.623 | 1.400 (0.366‒5.350) | ||||||
| 1 month | -0.21 | 0.62 | -0.41 | 0.683 | 0.778 (0.233‒2.599) | ||||||
| 3 month | 0.00 | 1.46 | 0.00 | 1.000 | 1.000 (0.057‒17.411) | ||||||
| 6 month | 16.57 | 1 696.73 | 0.01 | 0.992 | 15 651 360.793 (0.000‒Inf) | ||||||
| Symptoms at presentation | |||||||||||
| Chest tightness | 1.000 (Ref) | ||||||||||
| Chest pain | -0.27 | 0.77 | -0.37 | 0.723 | 0.762 (0.170‒3.422) | ||||||
| Palpitation | -1.35 | 0.83 | -1.63 | 0.103 | 0.260 (0.051‒1.313) | ||||||
| Dizziness | 0.83 | 1.10 | 0.82 | 0.413 | 2.286 (0.316‒16.512) | ||||||
| Fatigue | -18.13 | 1 978.09 | -0.01 | 0.993 | 0.000 (0.000‒Inf) | ||||||
| Other symptoms | -0.56 | 1.55 | -0.36 | 0.718 | 0.57 (0.028‒11.85) | ||||||
| Comorbidity | |||||||||||
| Negative | 1.000 (Ref) | ||||||||||
| Positive | 0.37 | 0.94 | 0.39 | 0.695 | 1.450 (0.226‒9.319) | ||||||
| Native T1 mapping | 0.06 | 0.01 | 4.67 | <0.001 | 1.057 (1.032‒1.081) | 0.08 | 0.02 | 3.40 | <0.001 | 1.080 (1.033‒1.129) | |
| GCS | 0.65 | 0.13 | 4.92 | <0.001 | 1.917 (1.479‒2.488) | ||||||
| CSBasal | 0.41 | 0.09 | 4.54 | <0.001 | 1.511 (1.264‒1.806) | ||||||
| CSMid | 0.81 | 0.16 | 5.09 | <0.001 | 2.239 (1.642‒3.055) | 0.94 | 0.25 | 3.79 | <0.001 | 2.564 (1.574‒4.175) | |
| CSApi | 0.09 | 0.06 | 1.63 | 0.103 | 1.099 (0.981‒1.231) | ||||||
Tab 6 Univariate and multivariate Logistic regression analyses of COVID-19-associated myocardial injury
| Variable | Univariate Logistic regression analysis | Multivariate Logistic regression analysis | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| β | S.E | Z | P | OR (95% CI) | β | S.E | Z | P | OR (95% CI) | ||
| Male | -0.27 | 0.42 | -0.65 | 0.515 | 0.762 (0.336‒1.727) | ||||||
| Time interval between symptom onset and CMR examination | |||||||||||
| 1 week | 1.000 (Ref) | ||||||||||
| 2 week | 0.34 | 0.68 | 0.49 | 0.623 | 1.400 (0.366‒5.350) | ||||||
| 1 month | -0.21 | 0.62 | -0.41 | 0.683 | 0.778 (0.233‒2.599) | ||||||
| 3 month | 0.00 | 1.46 | 0.00 | 1.000 | 1.000 (0.057‒17.411) | ||||||
| 6 month | 16.57 | 1 696.73 | 0.01 | 0.992 | 15 651 360.793 (0.000‒Inf) | ||||||
| Symptoms at presentation | |||||||||||
| Chest tightness | 1.000 (Ref) | ||||||||||
| Chest pain | -0.27 | 0.77 | -0.37 | 0.723 | 0.762 (0.170‒3.422) | ||||||
| Palpitation | -1.35 | 0.83 | -1.63 | 0.103 | 0.260 (0.051‒1.313) | ||||||
| Dizziness | 0.83 | 1.10 | 0.82 | 0.413 | 2.286 (0.316‒16.512) | ||||||
| Fatigue | -18.13 | 1 978.09 | -0.01 | 0.993 | 0.000 (0.000‒Inf) | ||||||
| Other symptoms | -0.56 | 1.55 | -0.36 | 0.718 | 0.57 (0.028‒11.85) | ||||||
| Comorbidity | |||||||||||
| Negative | 1.000 (Ref) | ||||||||||
| Positive | 0.37 | 0.94 | 0.39 | 0.695 | 1.450 (0.226‒9.319) | ||||||
| Native T1 mapping | 0.06 | 0.01 | 4.67 | <0.001 | 1.057 (1.032‒1.081) | 0.08 | 0.02 | 3.40 | <0.001 | 1.080 (1.033‒1.129) | |
| GCS | 0.65 | 0.13 | 4.92 | <0.001 | 1.917 (1.479‒2.488) | ||||||
| CSBasal | 0.41 | 0.09 | 4.54 | <0.001 | 1.511 (1.264‒1.806) | ||||||
| CSMid | 0.81 | 0.16 | 5.09 | <0.001 | 2.239 (1.642‒3.055) | 0.94 | 0.25 | 3.79 | <0.001 | 2.564 (1.574‒4.175) | |
| CSApi | 0.09 | 0.06 | 1.63 | 0.103 | 1.099 (0.981‒1.231) | ||||||
| [1] | Siddiqi R, Farhan S H, Shah S A, et al. National trends in heart failure and acute myocarditis-related death before and during the COVID-19 pandemic[J]. J Am Heart Assoc, 2025, 14(10): e038987. |
| [2] | Puntmann V O, Martin S, Shchendrygina A, et al. Long-term cardiac pathology in individuals with mild initial COVID-19 illness[J]. Nat Med, 2022, 28(10): 2117-2123. |
| [3] | Holby S N, Richardson T L Jr, Laws J L, et al. Multimodality cardiac imaging in COVID[J]. Circ Res, 2023, 132(10): 1387-1404. |
| [4] | Puntmann V O, Carerj M L, Wieters I, et al. Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from coronavirus disease 2019 (COVID-19)[J]. JAMA Cardiol, 2020, 5(11): 1265-1273. |
| [5] | Huang L X, Li X, Gu X Y, et al. Health outcomes in people 2 years after surviving hospitalisation with COVID-19: a longitudinal cohort study[J]. Lancet Respir Med, 2022, 10(9): 863-876. |
| [6] | Goerlich E, Chung T H, Hong G H, et al. Cardiovascular effects of the post-COVID-19 condition[J]. Nat Cardiovasc Res, 2024, 3(2): 118-129. |
| [7] | Davis H E, McCorkell L, Vogel J M, et al. Long COVID: major findings, mechanisms and recommendations[J]. Nat Rev Microbiol, 2023, 21(3): 133-146. |
| [8] | Steffen Johansson R, Loewenstein D, Lodin K, et al. Long-term coronary microvascular and cardiac dysfunction after severe COVID-19 hospitalization[J]. JAMA Netw Open, 2025, 8(6): e2514411. |
| [9] | Goerlich E, Minhas A S, Mukherjee M, et al. Multimodality imaging for cardiac evaluation in patients with COVID-19[J]. Curr Cardiol Rep, 2021, 23(5): 44. |
| [10] | Puntmann V O, Valbuena S, Hinojar R, et al. Society for Cardiovascular Magnetic Resonance (SCMR) expert consensus for CMR imaging endpoints in clinical research: part I - analytical validation and clinical qualification[J]. J Cardiovasc Magn Reson, 2018, 20(1): 67. |
| [11] | Carrick D, Haig C, Rauhalammi S, et al. Pathophysiology of LV remodeling in survivors of STEMI: inflammation, remote myocardium, and prognosis[J]. JACC Cardiovasc Imaging, 2015, 8(7): 779-789. |
| [12] | Nagel E, Kwong R Y, Chandrashekhar Y S. CMR in nonischemic myocardial inflammation: solving the problem of diagnosing myocarditis or still diagnostic ambiguity [J]. JACC Cardiovasc Imaging, 2020, 13(1 Pt 1): 163-166. |
| [13] | Shi S B, Qin M, Shen B, et al. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China[J]. JAMA Cardiol, 2020, 5(7): 802-810. |
| [14] | Kotecha T, Knight D S, Razvi Y, et al. Patterns of myocardial injury in recovered troponin-positive COVID-19 patients assessed by cardiovascular magnetic resonance[J]. Eur Heart J, 2021, 42(19): 1866-1878. |
| [15] | Ballering A V, van Zon S K R, Olde Hartman T C, et al. Persistence of somatic symptoms after COVID-19 in the Netherlands: an observational cohort study[J]. Lancet, 2022, 400(10350): 452-461. |
| [16] | Gluckman T J, Bhave N M, Allen L A, et al. 2022 ACC expert consensus decision pathway on cardiovascular sequelae of COVID-19 in adults: myocarditis and other myocardial involvement, post-acute sequelae of SARS-CoV-2 infection, and return to play a report of the American college of cardiology solution set oversight committee[J]. J Am Coll Cardiol, 2022, 79(17): 1717-1756. |
| [17] | Wan E Y F, Mathur S, Zhang R, et al. Association of COVID-19 with short- and long-term risk of cardiovascular disease and mortality: a prospective cohort in UK Biobank[J]. Cardiovasc Res, 2023, 119(8): 1718-1727. |
| [18] | Leitman M, Lysiansky M, Lysyansky P, et al. Circumferential and longitudinal strain in 3 myocardial layers in normal subjects and in patients with regional left ventricular dysfunction[J]. J Am Soc Echocardiogr, 2010, 23(1): 64-70. |
| [19] | Quinaglia T, Gongora C, Awadalla M, et al. Global circumferential and radial strain among patients with immune checkpoint inhibitor myocarditis[J]. JACC Cardiovasc Imaging, 2022, 15(11): 1883-1896. |
| [20] | Joy G, Artico J, Kurdi H, et al. Prospective case-control study of cardiovascular abnormalities 6 months following mild COVID-19 in healthcare workers[J]. JACC Cardiovasc Imaging, 2021, 14(11): 2155-2166. |
| [21] | Meindl C, Paulus M, Poschenrieder F, et al. Patients with acute myocarditis and preserved systolic left ventricular function: comparison of global and regional longitudinal strain imaging by echocardiography with quantification of late gadolinium enhancement by CMR[J]. Clin Res Cardiol, 2021, 110(11): 1792-1800. |
| [22] | Fu H, Zhang N, Zheng Y L, et al. Risk stratification of cardiac sequelae detected using cardiac magnetic resonance in late convalescence at the six-month follow-up of recovered COVID-19 patients[J]. J Infect, 2021, 83(1): 119-145. |
| [23] | Nensa F, Kloth J, Tezgah E, et al. Feasibility of FDG-PET in myocarditis: comparison to CMR using integrated PET/MRI[J]. J Nucl Cardiol, 2018, 25(3): 785-794. |
| [24] | Hanneman K, Houbois C, Kei T, et al. Multimodality cardiac imaging, cardiac symptoms, and clinical outcomes in patients who recovered from mild COVID-19[J]. Radiology, 2023, 308(1): e230767. |
| [25] | Kravchenko D, Isaak A, Zimmer S, et al. Cardiac MRI in patients with prolonged cardiorespiratory symptoms after mild to moderate COVID-19[J]. Radiology, 2021, 301(3): E419-E425. |
| [26] | Raman B, Cassar M P, Tunnicliffe E M, et al. Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge[J]. EClinicalMedicine, 2021, 31: 100683. |
| [27] | Sewanan LR, Di Tullio MR, Laine AF, et al. Absence of long-term structural and functional cardiac abnormalities on multimodality imaging in a multi-ethnic group of COVID-19 survivors from the early stage of the pandemic[J]. Eur Heart J Imaging Methods Pract, 2023, 1(2): qyad034. |
| [28] | Jerosch-Herold M, Rickers C, Petersen S E, et al. Myocardial tissue characterization in cardiac magnetic resonance studies of patients recovering from COVID-19: a meta-analysis[J]. J Am Heart Assoc, 2023, 12(6): e027801. |
| [29] | Siripanthong B, Nazarian S, Muser D, et al. Recognizing COVID-19-related myocarditis: the possible pathophysiology and proposed guideline for diagnosis and management[J]. Heart Rhythm, 2020, 17(9): 1463-1471. |
| [30] | Brandt Y, Lubrecht J M, Adriaans B P, et al. Quantification of left ventricular myocardial strain: comparison between MRI tagging, MRI feature tracking, and ultrasound speckle tracking[J]. NMR Biomed, 2024, 37(9): e5164. |
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