Abstract
Introduction
Persistent cardiac symptoms after COVID-19, termed post-acute sequelae of SARS-CoV-2 infection with cardiovascular involvement (PASC-CVS), remain poorly understood. This study aims to reveal myocardial involvement and long-term cardiac magnetic resonance (CMR) changes in PASC-CVS patients, providing insights into disease progression and recovery.
Methods
This prospective single-center study in China enrolled 110 patients with PASC-CVS from March 2023 to April 2024. All participants underwent multiparametric CMR imaging to explore abnormal CMR manifestations in PASC-CVS patients. Clinical symptoms and CMR examinations were collected again one year later. Statistical analysis included the Wilcoxon signed-rank test and logistic regression.
Results
Compared to controls, PASC-CVS patients exhibited significant differences in multiparametric CMR parameters, including Quantification of late gadolinium enhancement (LGE) (%) [odds ratio (OR): 6.36], Global extracellular volume (ECV) (ms) (OR: 1.33), Heart rate (bpm) (OR: 1.08), left ventricular global longitudinal strain (LV GLS) (%) (OR: 1.44), and Global native T1 (ms) (OR: 1.01). The combined multiparametric CMR parameters (Quantification of LGE, Global ECV, Heart rate, LV GLS, Global Post T1, and Global native T1) achieved an area under the curve (AUC) of 0.94, indicating excellent predictive value. At the one-year follow-up (n = 101), significant clinical improvement was observed, with a reduction in PASC scores (a 12-point symptom-based threshold framework) from 14.1 to 5.4 (P < 0.001). Repeat CMR in 20 patients revealed improvements in right ventricular ejection fraction (RV EF) (52.1% to 59.5%, P < 0.001) and global T2 (43.6 ms to 39.5 ms, P < 0.001). However, the proportion of patients showing recovery in Quantification of LGE was 0%, suggesting limited clinical significance of its subtle changes.
Conclusions
Our study reveals myocardial involvement in PASC-CVS patients, with follow-up improvements in RV EF and global T2, underscoring the utility of multiparametric CMR in monitoring and managing PASC-CVS.
Keywords: cardiac magnetic resonance imaging, long coronavirus disease 2019, longitudinal study, multiparametric imaging, post-acute sequelae of SARS-CoV-2 infection with cardiovascular involvement
1. Introduction
Since December 2019, the Coronavirus Disease 2019(COVID-19) pandemic has rapidly spread worldwide, with over 777 million cases reported as of January 2025 (1). Studies indicate that 10%–20% of individuals infected with SARS-CoV-2 may develop Post-acute Sequelae of SARS-CoV-2 Infection (PASC), commonly referred to as Long COVID (2, 3). PASC encompasses a range of symptoms that persist or emerge after COVID-19 recovery, typically lasting 4 to 12 weeks or longer (4). The World Health Organization (WHO) defines PASC as the persistence or emergence of new symptoms three months after SARS-CoV-2 infection, lasting at least two months without an alternative explanation (2).
Recent research has advanced the understanding of Long COVID. A prospective cohort study proposed a symptom-based definition, developing a data-driven scoring framework where a PASC score ≥12 classifies patients as PASC-positive (5, 6). Post-Acute Sequelae of SARS-CoV-2 Cardiovascular Syndrome (PASC-CVS) refers to a heterogeneous condition characterized by cardiovascular symptoms, though standard diagnostic methods often lack objective evidence of cardiovascular disease (4).
Although endomyocardial biopsy remains the gold standard for diagnosing myocarditis (6), its limitations include sampling error, limited sensitivity, and inherent risks, including rare mortality (7). Cardiac magnetic resonance (CMR) imaging has emerged as a non-invasive, highly sensitive tool for evaluating myocardial function, structure, and tissue characterization. Beyond conventional measures like ventricular ejection fraction (EF) and volumes, CMR enables myocardial tissue characterization through parametric mapping techniques, which detect diffuse fibrosis, inflammation, and edema (8, 9). Thus, multiparametric CMR may identify patients at high risk of cardiac sequelae and myocardial damage in PASC.
Numerous studies have assessed COVID-19's impact on myocardial tissue using CMR. Puntmann et al. (10) reported cardiac involvement in 78% of 100 recently recovered COVID-19 patients, with ongoing myocardial inflammation in 60% and scarring evident on native T1 and late gadolinium enhancement (LGE) assessment. Similar findings have been reported in smaller studies, including those focusing on recovering athletes. Hanneman et al. (11) found elevated native T1 values associated with cardiac symptoms at 3–6 and 12–18 months post-mild COVID-19.However, these studies were not longitudinal, and the reported frequency of cardiac symptoms was low. In contrast, our study quantifies cardiac symptoms and evaluates their relationship with imaging findings. To date, few long-term longitudinal studies have examined multiparametric CMR findings in PASC-CVS.
Therefore, this study utilizes multiparametric CMR to: (i) explore abnormal imaging characteristics in PASC-CVS patients; and (ii) investigate longitudinal changes in multiparametric CMR findings through a one-year follow-up.
2. Materials and methods
This single-center longitudinal cohort study was approved by the institutional research ethics board (Shanghai Public Health Clinical Center). All participants provided written informed consent.
2.1. Study population
Participants were prospectively recruited between March 2023 and April 2024. The study included two groups: PASC-CVS patients and controls. Inclusion criteria for the PASC-CVS group were: (i) Age ≥18 years; (ii) Presence of cardiovascular symptoms (e.g., post-exertional malaise, palpitations, chest pain, fatigue) that first occurred within 2 months after SARS-CoV-2 infection. Inclusion criteria for the control group were: (i) No history of SARS-CoV-2 infection; or (ii) Previous SARS-CoV-2 infection but remained asymptomatic. Exclusion criteria for both groups were: (i) Cardiac pacemaker implantation; (ii) Uncontrolled hypertension; (iii) History of severe cardiovascular diseases (e.g., moderate to severe coronary artery disease, myocardial infarction, valvular dysfunction, atrial fibrillation, heart failure, cardiomyopathy); (iv) Severe renal insufficiency (creatinine clearance <30 mL/min/1.73m2); (v) Chronic mental illness requiring clinical treatment; (vi) Pregnancy; (vii) Contraindications for MRI; (viii) Severe image artifacts (e.g., significant respiratory motion artifacts).
2.2. Data collection and follow-up
Before CMR examination, clinical data were collected, including gender, age, and clinical symptoms (e.g., smell/taste abnormalities, post-exertional malaise, chronic cough, brain fog, thirst, palpitations, chest pain, fatigue, sexual desire/capacity changes, dizziness, gastrointestinal issues, and abnormal movements). For the PASC-CVS group, a follow-up assessment, including clinical data collection and CMR, was conducted 10–14 months after the initial examination using the same protocol.
2.3. CMR scanning protocol
Cardiac MRI was performed using two 3.0 T MR scanners (uMR780 and uMR870, United Imaging Healthcare, Shanghai, China). The total scanning duration for each subject is approximately 45 min. A standardized MRI protocol was used to evaluate myocarditis based on alterations in myocardial tissue composition, as previously described (12). Cine imaging was performed using balanced steady-state free precession (bSSFP) sequences to acquire short-axis (SA) and long-axis (LA) images covering the entire heart. Pre- and post-contrast T1 maps were obtained using a Modified Look-Locker inversion recovery (MOLLI) 5(3)3 sequence during diastole, with SA images acquired at basal, mid-ventricular, and apical levels. T2 mapping was performed using T2-prepared bSSFP sequences during diastole at the same locations as T1 mapping. Late gadolinium enhancement (LGE) imaging was conducted after intravenous injection of 0.2 mL/kg gadoterate meglumine (Dotarem, Guerbet, France), with delayed enhancement images acquired 10 min post-injection to assess myocardial delayed enhancement. Multiparametric image analysis was performed to derive global cardiac function metrics, myocardial strain and strain rate, as well as T1, extracellular volume (ECV), and T2 maps. Detailed sequence parameters are provided in Supplementary Table S1.
2.4. Cardiac MRI analysis
Image analysis was performed using Circle cvi42 software version 5.13 (Circle, Calgary, AB, Canada). The following parameters were derived: left and right ventricular end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), cardiac output (CO), ejection fraction (EF), and cardiac index (CI). Myocardial deformation parameters, including global longitudinal, circumferential, and radial peak systolic strains (GLS, GCS, GRS) and peak diastolic strain rates (GLSR, GCSR, GRSR), were analyzed. Tissue characterization parameters, such as T2 values, native T1 values, post-contrast T1 values, and extracellular volume (ECV), were also derived. Endocardial and epicardial contours were manually delineated on T1 and T2 slices, and cvi42 T1 and T2 mapping techniques were used to generate T1 and T2 maps by fitting exponentially recovering (T1) and decaying (T2) curves at the pixel level.
Two radiologists (Y.W.G and A.S., with 1 and 2 years of experience) independently analyzed the MRI studies using Circle cvi42 software, blinded to all clinical information. The presence, location (apical, midventricular, or basal segments), and pattern of late gadolinium enhancement (LGE) were independently assessed by two radiologists (A.S. and F.S., with 2 and 6 years of experience). LGE lesions were quantitatively analyzed using the CVI LGE quantification tool, with lesions defined as areas with signal intensity more than 3 standard deviations above the reference normal myocardium.
2.5. Intra- and interobserver reliability
The intra- and interobserver reliability of all CMR parameters was evaluated in 110 PASC-CVS patients and 55 controls. Intraclass correlation coefficients (ICC) were used to assess reliability. Intraobserver reliability was determined by repeated measurements by one radiologist (A.S.) after a minimum 1-month interval, blinded to prior results. Interobserver reliability was assessed independently by another radiologist (Y.W.G.), blinded to the first radiologist's measurements. Results are presented in Supplementary Table S7.
2.6. Statistical analysis
Statistical analysis was performed using IBM SPSS Statistics (version 20.0) and RStudio (version 2024.12.0). Categorical data are presented as counts (percentages), and continuous variables as mean ± standard deviation (SD) or median [interquartile range (IQR)]. Normality was assessed using the Shapiro–Wilk test. Proportions were compared using Fisher's exact or χ2 tests, as appropriate; for non-paired continuous variables, Student's t-test was used for normally distributed data, while the Mann–Whitney U test was applied for non-normally distributed data. For paired comparisons, McNemar's test was used for categorical variables, and the Wilcoxon signed-rank test was used for continuous variables. Logistic regression was used to analyze CMR parameters, with variables significant at P < 0.1 in univariable analysis included in multivariable models. Multivariate logistic regression identified predictors of PASC-CVS. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value of single and combined predictors, with area under the curve (AUC) and optimal cutoff points determined by Youden's index. The DeLong test was employed to compare the performance among the parameters of the selected CMR independent predictors and the combined parameter. All tests were two-sided, with P < 0.05 considered statistically significant. Intra- and interobserver repeatability were assessed using ICC.
3. Results
3.1. Baseline clinical characteristics
Among 120 participants evaluated for eligibility, 10 were excluded (Figure 1). The final cohort included 110 participants, with 35.5% being male (n = 39) and a mean age of 42.7 ± 15.6 years. No significant differences in smoking history or other comorbidities were observed between groups. Telephone follow-up was conducted in 101 PASC patients, with 9 patients lost to follow-up due to missing contact information or lack of cooperation. The mean time from virus detection to the initial CMR examination was 204.7 ± 113.0 days (median 169.0 days, IQR 108.3–261.0).
Figure 1.
Recruitment pathway for patients in this study. PASC-CVS, Post-Acute Sequelae of SARS-CoV-2 Cardiovascular Syndrome.
The clinical symptoms of the PASC-CVS subjects, collected during the initial CMR examination, are presented in Supplementary Table S3 and Supplementary Figure S1. The definitions of clinical symptoms are provided in Supplementary Table S2. The total PASC score for the PASC-CVS group was 14.1 ± 4.4 points. The most prevalent symptoms were fatigue (95%), palpitations (94%), and post-exertional malaise (91%).
3.2. CMR findings
Among the 55 controls, 15 had no history of SARS-CoV-2 infection and 40 had prior infection but remained asymptomatic. Subgroup analysis showed no significant differences in CMR parameters between these two control subgroups (Supplementary Table S4). The global MRI parameters are summarized in Tables 1 and S5. Compared to controls, the PASC-CVS group exhibited significant differences in key cardiac parameters. Specifically, the PASC-CVS group had elevated heart rate (74.4 ± 12.7 bpm vs. 66.4 ± 9.4 bpm, P < 0.001), and Global ECV values (31.6 ± 2.6% vs. 28.9 ± 2.2%, P < 0.001). Additionally, LV GCS (−19.7 ± 3.1% vs. −20.9 ± 2.3%, P = 0.008) and LV GLS (−15.1 ± 2.5% vs. −16.2 ± 1.9%, P = 0.010) were impaired in the PASC-CVS group. Quantification of LGE was significantly higher in the PASC-CVS group (0.7 ± 0.3% vs. 0.3 ± 0.2%, P < 0.001), and the prevalence of LGE was also significantly increased (71.8% vs. 37.0%, P < 0.001).
Table 1.
Baseline characteristics and CMR findings of PASC-CVS compared to control.
| Characteristic | PASC-CVS(n = 110) | Control(n = 55) | P value |
|---|---|---|---|
| Age（years） | 42.65 ± 15.55 | 42.87 ± 13.89 | 0.740 |
| Male Gender（N，%） | 39 (35.45) | 25 (45.45) | 0.283 |
| BMI（kg/m2） | 23.02 ± 3.61 | 23.52 ± 3.61 | 0.397 |
| Smoking history（N，%） | 19 (17.27) | 9 (16.36) | 1.000 |
| Comorbidities | |||
| Hypertension（N，%） | 17 (15.45) | 8 (14.55) | 1.000 |
| Diabetes（N，%） | 7 (6.36) | 2 (3.64) | 0.719 |
| Hyperlipidemia（N，%） | 14 (12.73) | 4 (7.27) | 0.428 |
| Heart Failure（N，%） | 0 (0.00) | 0 (0.00) | NA |
| Severe coronary artery disease（N，%） | 0 (0.00) | 0 (0.00) | NA |
| Other Heart Diseases（N，%） | 0 (0.00) | 0 (0.00) | NA |
| History of Coronary Artery Surgery（N，%） | 0 (0.00) | 0 (0.00) | NA |
| History of Psychotropic Medication（N，%） | 2 (1.82) | 0 (0.00) | 0.553 |
| Depression（N，%） | 0 (0.00) | 0 (0.00) | NA |
| Anxiety（N，%） | 13 (11.82) | 0 (0.00) | 0.005* |
| Blood biomarkers | |||
| Troponin-positive（N，%） | 1（0.91） | NA | NA |
| COVID-19 vaccination | |||
| At least three dose prior to MRI（N，%） | 99 (90.00) | 54 (98.18) | 0.063 |
| Cardiac MRI | |||
| Days from virus detection to CMR (days) | 204.73 ± 112.98 | NA | NA |
| Days from virus detection to CMR (days)# | 169.00 (108.25–261.00) | NA | NA |
| uMR 780（N，%） | 84 (76.36) | 36 (65.45) | 0.194 |
| LV EDV（mL） | 118.38 ± 27.17 | 121.17 ± 23.74 | 0.454 |
| LV ESV（mL） | 50.45 ± 20.71 | 48.54 ± 12.12 | 0.726 |
| LV SV（mL） | 67.92 ± 15.03 | 72.63 ± 14.97 | 0.139 |
| LV CO（/min） | 5.02 ± 1.35 | 4.79 ± 1.05 | 0.236 |
| LV EF（%） | 57.83 ± 7.38 | 60.05 ± 5.50 | 0.150 |
| LV CI（/min/m2） | 2.91 ± 0.68 | 2.77 ± 0.48 | 0.197 |
| RV EDV（mL） | 114.53 ± 27.95 | 125.15 ± 30.45 | 0.037* |
| RV ESV（mL） | 56.06 ± 20.86 | 57.42 ± 17.27 | 0.414 |
| RV SV（mL） | 58.47 ± 15.98 | 67.73 ± 16.99 | 0.001* |
| RV CO（/min） | 4.33 ± 1.38 | 4.47 ± 1.22 | 0.435 |
| RV EF（%） | 51.51 ± 9.86 | 54.38 ± 6.30 | 0.091 |
| RV CI（/min/m2） | 2.51 ± 0.75 | 2.57 ± 0.54 | 0.504 |
| Heart rate (bpm) | 74.40 ± 12.71 | 66.41 ± 9.37 | <0.001* |
| Global native T1（ms） | 1,153.53 ± 66.53 | 1,132.86 ± 49.10 | 0.076 |
| Global post T1（ms） | 454.75 ± 58.60 | 384.98 ± 73.74 | <0.001* |
| Global ECV（%） | 31.56 ± 2.59 | 28.86 ± 2.22 | <0.001* |
| Global T2（ms） | 42.24 ± 3.56 | 42.45 ± 2.74 | 0.778 |
| LV GRS（%） | 35.42 ± 9.04 | 35.91 ± 7.63 | 0.730 |
| LV GCS（%） | −19.65 ± 3.09 | −20.87 ± 2.25 | 0.008* |
| LV GLS（%） | −15.12 ± 2.49 | −16.23 ± 1.89 | 0.010* |
| LV sGRSR (/s) | 2.29 ± 0.81 | 2.35 ± 1.07 | 0.403 |
| LV sGCSR (/s) | −1.08 ± 0.23 | −1.06 ± 0.18 | 0.553 |
| LV sGLSR (/s) | −0.90 ± 0.22 | −0.90 ± 0.27 | 0.550 |
| LGE（N，%） | 79 (71.82) | 17 (36.96) | <0.001* |
| Quantification of LGE (%) | 0.73 ± 0.26 | 0.34 ± 0.16 | <0.001* |
| Distribution of LGE（N，%） | |||
| Basal | 72 (65.45) | 5 (10.87) | <0.001* |
| Mid | 33 (30.00) | 13 (28.26) | 0.980 |
| Apical | 5 (4.55) | 1 (2.17) | 0.671 |
| subepicardial | 0 (0.00) | 0 (0.00) | NA |
| epicardial | 4 (3.64) | 0 (0.00) | 0.320 |
| mid-myocardial layer | 79 (71.82) | 17 (36.96) | <0.001* |
Values are presented as mean ± standard deviation or number (%) unless otherwise specified; #, Data are expressed as the median, with the interquartile range in parentheses, for continuous variables; *, P＜0.05 statistically significant; BMI, body mass index [weight [kg]/height2 [m2]]; LV, left ventricular; EDV, end-diastolic volume; ESV, end-systolic volume; SV, stroke volume; CO, cardiac output; EF, ejection fraction; CI, cardiac index; RV, right ventricular; Global native T1, global native T1 relaxation time; Global post T1, global post-contrast T1 relaxation time; Global ECV, global extracellular volume fraction; Global T2, global T2 relaxation time; GRS, global radial strain; GCS, global circumferential strain; GLS, global longitudinal strain; sGRSR, systolic radial strain rate; sGCSR, systolic circumferential strain rate; sGLSR, systolic longitudinal strain rate; LGE, late gadolinium enhancement.
Furthermore, some CMR parameters showed significant differences between controls and the PASC Score ≥ 12 group but not between controls and the PASC Score < 12 group. For example, heart rate was significantly higher in the PASC Score ≥ 12 group compared to controls (74.9 ± 12.8 bpm vs. 66.4 ± 9.4 bpm, P < 0.001), but not in the PASC Score < 12 group (70.5 ± 11.8 bpm vs. 66.4 ± 9.4 bpm, P = 0.180). Similarly, Global native T1 values were elevated in the PASC Score ≥ 12 group (1,154.8 ± 66.7 ms vs. 1,132.9 ± 49.1 ms, P = 0.034) but not in the PASC Score < 12 group (1,143.8 ± 67.2 ms vs. 1,132.9 ± 49.1 ms, P = 0.506). LV GCS (−19.5 ± 3.2% vs. −20.9 ± 2.3%, P = 0.006) and LV GLS (−14.9 ± 2.5% vs. −16.2 ± 1.9%, P = 0.002) were also significantly impaired in the PASC Score ≥ 12 group compared to controls, but not in the PASC Score < 12 group (LV GCS: −20.5 ± 2.4% vs. −20.9 ± 2.3%, P = 0.357; LV GLS: −16.6 ± 1.5% vs. −16.2 ± 1.9%, P = 0.505) (Figures 2, S2).
Figure 2.
Comparison of CMR parameters among control, PASC score <12, and PASC score ≥12 groups. Distribution of CMR parameters among three groups: Control (n = 55), PASC Score <12 (n = 13), and PASC Score ≥12 (n = 97). Parameters with statistical differences between Control and PASC Score ≥ 12 are shown in this figure, with P values <0.05 indicating statistical significance. Significant differences are marked with asterisks, with the number of asterisks corresponding to the level of significance (e.g., *p < 0.05, **p < 0.01, ***p < 0.001). The remaining parameters without statistical significance are displayed in Supplementary Figure S2. Abbreviations are consistent with those used in Table 1.
3.3. Logistic regression and ROC curve analysis
To assess the ability of CMR parameters to distinguish PASC-CVS from controls, logistic regression and receiver operating characteristic (ROC) curve analyses were performed. Multivariate analysis identified six independent predictors of PASC-CVS: Quantification of LGE (OR: 6.36, 95% CI: 3.05–15.86, P < 0.001), Global ECV (OR: 1.33, 95% CI: 1.04–1.78, P = 0.034), Global post T1 (OR: 1.02, 95% CI: 1.01–1.03, P = 0.002), Global native T1 (OR: 1.01, 95% CI: 1.00–1.02, P = 0.049), LV GLS (OR: 1.44, 95% CI: 1.09–1.97, P = 0.015), and Heart rate (OR: 1.08, 95% CI: 1.02–1.15, P = 0.014) (Table 2).
Table 2.
Univariate and multivariate logistic regression analysis of multiparametric CMR in PASC-CVS.
| Variable | Univariate | Multivariate | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P value | OR | 95% CI | P value | |
| LV EDV（mL） | 1.00 | 0.98–1.01 | 0.516 | |||
| LV ESV（mL） | 1.01 | 0.99–1.03 | 0.533 | |||
| LV SV（mL） | 0.98 | 0.96–1.00 | 0.062 | |||
| LV CO（l/min） | 1.17 | 0.9–1.55 | 0.266 | |||
| LV EF（%） | 0.95 | 0.89–1.00 | 0.053 | |||
| LV CI（l/min/m2） | 1.51 | 0.87–2.71 | 0.154 | |||
| RV EDV（mL） | 0.99 | 0.98–1.00 | 0.030 | |||
| RV ESV（mL） | 1.00 | 0.98–1.01 | 0.674 | |||
| RV SV（mL） | 0.97 | 0.94–0.99 | 0.001* | 0.97 | 0.93–1 | 0.083 |
| RV CO（l/min） | 0.92 | 0.72–1.18 | 0.511 | |||
| RV EF（%） | 0.96 | 0.92–1.00 | 0.055 | |||
| RV CI（l/min/m2） | 0.88 | 0.54–1.44 | 0.614 | |||
| Heart rate (bpm) | 1.07 | 1.03–1.11 | <0.001* | 1.08 | 1.02–1.15 | 0.014* |
| Global native T1（ms） | 1.01 | 1.00–1.01 | 0.045 | 1.01 | 1.00–1.02 | 0.049* |
| Global post T1（ms） | 1.02 | 1.01–1.02 | <0.001* | 1.02 | 1.01–1.03 | 0.002* |
| Global ECV（%） | 1.55 | 1.31–1.87 | <0.001* | 1.33 | 1.04–1.78 | 0.034* |
| Global T2（ms） | 0.98 | 0.89–1.08 | 0.704 | |||
| LV GRS（%） | 0.99 | 0.96–1.03 | 0.728 | |||
| LV GCS（%） | 1.19 | 1.05–1.38 | 0.012* | |||
| LV GLS（%） | 1.27 | 1.08–1.51 | 0.005* | 1.44 | 1.09–1.97 | 0.015* |
| LV sGRSR (/s) | 0.93 | 0.65–1.33 | 0.675 | |||
| LV sGCSR (/s) | 0.67 | 0.13–3.01 | 0.611 | |||
| LV sGLSR (/s) | 1.07 | 0.26–4.16 | 0.924 | |||
| Quantification of LGE (%) | 5.94 | 3.49–11.14 | <0.001* | 6.36 | 3.05–15.86 | <0.001* |
*, P＜0.05 Statistically significant; OR, Odds Ratio; CI, Confidence Interval; Abbreviations are consistent with those used in Table 1.
Compared with the ROC curves of other independent predictors, the Combined parameter demonstrated superior performance in distinguishing PASC-CVS from controls (P < 0.05) (Supplementary Table S6), with an AUC of 0.94 (95% CI: 0.91–0.97, P < 0.001), sensitivity of 83.0%, and specificity of 91.0%. Among individual parameters, Quantification of LGE exhibited an AUC of 0.89 (95% CI: 0.84–0.94, P < 0.001), sensitivity of 78.0%, specificity of 87.0%, and a cutoff value of 0.52%. Global ECV showed an AUC of 0.75 (95% CI: 0.67–0.82, P < 0.001), sensitivity of 51.0%, specificity of 91.0%, and a cutoff value of 31.15%. Global post T1 had an AUC of 0.75 (95% CI: 0.67–0.83, P < 0.001), sensitivity of 55.0%, specificity of 87.0%, and a cutoff value of 450.85 ms. Global native T1 showed an AUC of 0.58 (95% CI: 0.50–0.67, P = 0.045), sensitivity of 20.0%, specificity of 98.0%, and a cutoff value of 1,210.39 ms. LV GLS exhibited an AUC of 0.62 (95% CI: 0.53–0.71, P = 0.005), sensitivity of 45.0%, specificity of 76.0%, and a cutoff value of −14.91%. Heart rate demonstrated an AUC of 0.69 (95% CI: 0.60–0.77, P < 0.001), sensitivity of 54.0%, specificity of 75.0%, and a cutoff value of 71.50 bpm (Figure 3, Table 3).
Figure 3.
ROC curves of six CMR parameters and combined model
this ROC curve demonstrates the ability of six CMR parameters, which were identified as independent predictors through multivariate analysis, and their combined model to distinguish PASC-CVS from the control group: combined (red solid), quantification of LGE (blue dashed), global ECV (purple dashed), heart rate (green dashed), LV GLS (yellow dashed), global post T1 (black dashed), and global native T1 (gray dashed). The red solid curve, representing the combined model, shows the highest AUC of 0.94, indicating excellent discrimination. The blue dashed curve, for Quantification of LGE, has an AUC of 0.89. The purple, green, yellow, black, and gray dashed curves represent Global ECV, Heart rate, LV GLS, Global Post T1, and Global native T1 with AUCs of 0.75, 0.69, 0.62, 0.75, and 0.58, respectively.
Table 3.
Receiver operating characteristic curve analysis of CMR independent predictors and combined parameters in distinguishing PASC-CVS from control.
| Parameter | AUC | 95% CI | Sensitivity | Specificity | Cutoff value |
|---|---|---|---|---|---|
| Combined | 0.94 | 0.91–0.97 | 0.83 | 0.91 | 0.70 |
| Quantification of LGE (%) | 0.89 | 0.84–0.94 | 0.78 | 0.87 | 0.52 |
| Global ECV（ms） | 0.75 | 0.67–0.82 | 0.51 | 0.91 | 31.15 |
| Global post T1（ms） | 0.75 | 0.67–0.83 | 0.55 | 0.87 | 450.85 |
| Heart rate (bpm) | 0.69 | 0.60–0.77 | 0.54 | 0.75 | 71.50 |
| LV GLS (%) | 0.62 | 0.53–0.71 | 0.45 | 0.76 | −14.91 |
| Global native T1（ms） | 0.58 | 0.5–0.67 | 0.2 | 0.98 | 1,210.39 |
ROC, receiver operating characteristic; AUC, area under the curve; GLS, global longitudinal strain; ECV, extracellular volume; LGE, late gadolinium enhancement; LV, left ventricular.
3.4. Clinical and CMR findings one year later
One year later, with a median follow-up time of 371.00 days (340.00–409.00), significant improvements were observed in the PASC score among patients (n = 101), decreasing from 14.1 ± 4.4 to 5.4 ± 5.1, although 9.9% of patients showed no improvement (Supplementary Table S3). Among the 20 patients who underwent repeat CMR, several cardiac parameters demonstrated significant changes (Table 4, Figures 4, S3). Notably, RV EF increased from 52.1 ± 6.8% to 59.5 ± 5.0% (p < 0.001), and Global T2 values decreased from 43.6 ± 4.0 ms to 39.5 ± 2.6 ms (p < 0.001), while LV GLS improved from −15.3 ± 2.1% to −17.0 ± 2.0% (p = 0.011). The Quantification of LGE decreased from 0.78 ± 0.24% to 0.64 ± 0.25% (p = 0.027), with Cohen's d values for Quantification of LGE and LV GLS being less than 0.8, indicating moderate effect sizes. The proportions of patients showing recovery (defined as values below the mean of the control group) for RV EF, Global T2, LV GLS, and Quantification of LGE were 85%, 90%, 80%, and 0%, respectively. Among patients who underwent follow-up CMR, those without clinical improvement exhibited worsening CMR findings, while patients with clinical improvement showed corresponding improvements in CMR parameters (Figure 5).
Table 4.
Characteristics and CMR findings of PASC-CVS patients at one-year follow-up.
| Characteristic | Baseline(n = 20) | Follow-up(n = 20) | P value (Cohen's d) |
|---|---|---|---|
| Median Follow-Up Time (days)# | NA | 416.00 (402.00–429.00) | NA |
| PASC Score | 15.10 ± 4.29 | 4.90 ± 5.62 | <0.001* (1.52) |
| Improvement（N，%） | NA | 17 (85.00) | NA |
| LV EDV（mL） | 120.36 ± 21.80 | 113.77 ± 15.77 | 0.294 (0.34) |
| LV ESV（mL） | 49.25 ± 9.87 | 45.81 ± 6.62 | 0.076 (0.37) |
| LV SV（mL） | 71.12 ± 14.01 | 67.96 ± 12.78 | 0.522 (0.23) |
| LV CO（l/min） | 5.08 ± 0.84 | 4.82 ± 0.88 | 0.294 (0.24) |
| LV EF（%） | 59.00 ± 4.08 | 59.48 ± 4.97 | 0.546 (−0.09) |
| LV CI（l/min/m2） | 2.83 ± 0.46 | 2.75 ± 0.49 | 0.571 (0.12) |
| RV EDV（mL） | 115.27 ± 24.46 | 113.77 ± 15.77 | 0.841 (0.06) |
| RV ESV（mL） | 54.71 ± 11.60 | 45.81 ± 6.62 | 0.001* (0.84) |
| RV SV（mL） | 60.56 ± 16.60 | 67.96 ± 12.78 | 0.097 (−0.36) |
| RV CO（l/min） | 4.34 ± 1.14 | 4.82 ± 0.88 | 0.165 (−0.32) |
| RV EF（%） | 52.12 ± 6.83 | 59.48 ± 4.97 | <0.001* (−0.90) |
| RV CI（l/min/m2） | 2.42 ± 0.62 | 2.75 ± 0.49 | 0.143 (−0.37) |
| Heart rate (bpm) | 72.75 ± 11.90 | 71.70 ± 10.62 | 0.869 (0.07) |
| Global native T1（ms） | 1,125.18 ± 42.86 | 1,129.49 ± 41.91 | 0.522 (−0.12) |
| Global post T1（ms） | 461.91 ± 61.02 | 390.52 ± 44.06 | 0.001* (0.86) |
| Global ECV（%） | 31.06 ± 1.52 | 30.05 ± 1.47 | 0.090 (0.51) |
| Global T2（ms） | 43.61 ± 3.95 | 39.52 ± 2.60 | 0.001* (0.97) |
| LV GRS（%） | 33.34 ± 5.70 | 37.12 ± 7.88 | 0.053 (−0.45) |
| LV GCS（%） | −20.26 ± 2.38 | −20.44 ± 1.89 | 0.784 (0.10) |
| LV GLS（%） | −15.33 ± 2.14 | −16.95 ± 1.96 | 0.011* (0.60) |
| LV sGRSR (/s) | 2.25 ± 0.69 | 3.03 ± 1.67 | 0.097 (−0.41) |
| LV sGCSR (/s) | −1.09 ± 0.15 | −1.06 ± 0.21 | 0.571 (−0.15) |
| LV sGLSR (/s) | −0.93 ± 0.18 | −1.00 ± 0.16 | 0.216 (0.32) |
| LGE（N，%） | 17 (85.0) | 16 (80.0) | 0.707 |
| Quantification of LGE (%) | 0.78 ± 0.24 | 0.64 ± 0.25 | 0.027 * (0.54) |
| Distribution of LGE (N, %) | |||
| Basal | 16 (80.0) | 15 (75.0) | 0.739 |
| Mid | 7 (35.0) | 6 (30.0) | 0.108 |
| Apical | 1 (5.0) | 0 (0.0) | 0.317 |
| subepicardial | 0 (0.0) | 0 (0.0) | NA |
| epicardial | 0 (0.0) | 0 (0.0) | NA |
| mid-myocardial layer | 17 (85.0) | 16 (80.0) | 0.317 |
Values are presented as mean ± standard deviation or number (%) unless otherwise specified; #, presented as medians (IQR); *, P＜0.05 Statistically significant; Abbreviations are consistent with those used in Table 1.
Figure 4.
Comparison of CMR parameters among the control, baseline, and follow-up. Distribution of CMR parameters among three groups: Control (n = 55), Baseline (n = 20), and Follow-up (n = 20). Parameters with statistical differences between Baseline and Follow-up are shown in this figure, with P values <0.05 indicating statistical significance. Significant differences are marked with asterisks, with the number of asterisks corresponding to the level of significance (e.g., *p < 0.05, **p < 0.01, ***p < 0.001). The remaining parameters without statistical significance are displayed in Supplementary Figure S3. Abbreviations are consistent with those used in Table 1.
Figure 5.
Cardiac magnetic resonance (CMR) imaging of post-acute sequelae of SARS-CoV-2 infection with cardiovascular symptoms (PASC-CVS) patients: initial and follow-up. (A–J) CMR images of a 31-year-old female PASC-CVS patient with a stable PASC score of 12 at both initial and follow-up. Initial CMR showed native T1 mapping values of 1,202 ms, extracellular volume (ECV) mapping values of 32%, and global longitudinal strain (GLS) of −17.6%. Follow-up CMR revealed an increase in native T1 to 1,244 ms, ECV to 33%, and a slight reduction in GLS to −16.5%. New mid-myocardial late gadolinium enhancement (LGE) was observed in the interventricular septum on follow-up imaging. Images from two different projections (short-axis and four-chamber) are shown to better illustrate the LGE lesion (E,J). (K–R) CMR images of a 70-year-old female PASC-CVS patient with significant clinical improvement, as evidenced by a reduction in PASC score from 16 at initial to 2 at follow-up. Initial CMR showed T2 mapping values of 47 ms, non-ischemic mid-myocardial LGE in the interventricular septum and lateral wall, and GLS of −13.7%. Follow-up CMR demonstrated a reduction in T2 mapping values to 37 ms, complete resolution of the previously observed LGE, and improvement in GLS to −17.1%. The LGE lesion is depicted in both short-axis and four-chamber views (N,R).
4. Discussion
This study is one of the few longitudinal investigations aimed at revealing subtle CMR abnormalities in PASC-CVS and conducting follow-up assessments. Multivariate analysis identified six independent predictors of PASC-CVS: Quantification of LGE, Global ECV, Global post T1, Global native T1, LV GLS, and Heart rate. The combination of these predictors demonstrated significant predictive value, highlighting the potential utility of multiparametric CMR as a valuable source of imaging biomarkers for PASC-CVS. At the one-year follow-up, overall improvement in clinical symptoms and a reduction in CMR abnormalities were observed, notably in RV EF, Global T2, and LV GLS, with 85%, 90%, and 80% of PASC-CVS patients showing improvement in these parameters, respectively. This underscores the value of multiparametric CMR in guiding rehabilitation strategies and optimizing long-term patient management.
This study employed a broad definition of PASC based on the presence of cardiovascular symptoms after SARS-CoV-2 infection. The 12-point PASC score and its ≥12 threshold were applied post hoc for exploratory stratification of symptom burden. We acknowledge that this scoring system was derived from non-Chinese populations, and its external validity in Chinese cohorts should be interpreted cautiously. For instance, the researchers who developed the PASC scoring system reported 41% of patients in the original study experienced loss of or change in smell or taste, whereas only 4.8% of patients reported such symptoms in a study conducted in China (13). Future studies validating symptom-based PASC definitions in diverse ethnic and geographic populations are warranted to enhance cross-cohort comparability.
LV GLS was significantly impaired in the PASC-CVS group compared to the control group (−15.1% vs. −16.2%, P = 0.010), while no significant difference was observed in LV EF. This suggests that LV GLS may be a more sensitive indicator of subclinical cardiac dysfunction in PASC-CVS patients (14, 15). The increased heart rate (74.4 bpm vs. 66.4 bpm, P < 0.001) may be associated with clinical symptoms such as palpitations. Patients in the PASC-CVS group exhibited myocardial fibrosis, as evidenced by elevated Global ECV values (31.6% vs. 28.9%, P < 0.001). These findings are consistent with previous studies (4, 10, 15–18). Importantly, these objective CMR abnormalities provide a potential pathophysiological basis for the highly prevalent symptoms of post-exertional malaise (91%) and fatigue (95%) observed in our cohort, highlighting the clinical significance of subclinical LV GLS impairment and elevated native T1 in PASC-CVS. In our study, the majority of patients displayed LGE in a non-ischemic distribution pattern, was predominantly localized at the basal and mid-ventricular segments of the LV, with a subepicardial and/or mid-wall distribution (19, 20). The prevalence of LGE was 70.9%, consistent with the 78.8% reported by Popovic et al. (21).
Furthermore, some CMR parameters showed significant differences between controls and the PASC Score ≥ 12 group but not between controls and the PASC Score < 12 group. This may suggest that the severity of clinical symptoms in PASC-CVS is associated with the extent of CMR abnormalities. We acknowledge that the clinical severity of SARS-CoV-2 infection has varied over time due to viral mutations, which may influence the generalizability of our findings to future variants.
Our study underscores the roles of Quantification of LGE, Global ECV, Global post T1, Global native T1, LV GLS, and heart rate as independent predictive factors, which could aid in the more precise identification of high-risk patients in clinical practice. The ROC analysis revealed the Combined parameter as the most discriminative, with the highest AUC of 0.94. Quantification of LGE followed with an AUC of 0.89, while Global ECV and Global post T1 showed AUCs of 0.75. LGE is indicative of tissue inflammation, necrosis, and fibrosis, suggesting that chronic inflammation is a primary mechanism of direct myocardial injury caused by COVID-19 (22–24). Additionally, earlier studies on other types of viral myocarditis have demonstrated that LGE carries prognostic significance, often correlating with a worse prognosis (25–27).
Our findings are consistent with previous studies (11, 28) and demonstrate an overall improvement in clinical symptoms among PASC patients, with a reduction in PASC scores from 14.1 ± 4.4 at baseline to 5.4 ± 5.1 at follow-up (n = 101). However, 9.9% of patients still showed no improvement, highlighting the necessity for repeat CMR assessments to evaluate the progression of myocardial involvement. However, the rate of follow-up CMR completion was relatively low, with only 20 out of 101 participants undergoing a second CMR examination. This high dropout rate was primarily due to patients’ reluctance to repeat CMR after symptom improvement, as well as the geographical challenges posed by the inclusion of patients from across the country, many of whom were unwilling to return to our center for follow-up.
Significant differences in cardiac function, including RV EF and LV GLS, were observed on repeat CMR. These findings are consistent with the study by Puntmann et al., who reported follow-up CMR findings at a median of 329 days after the initial CMR and observed a notable increase in RV EF (from 54.0 ± 5.6% to 55.4 ± 5.6%) (16). Additionally, improvements in T2 values were observed, indicating a reduction in myocardial edema. This finding aligns with the understanding that in PASC patients, myocardial edema tends to resolve over time as the acute phase subsides (21, 29). However, our study demonstrated a significant reduction in quantitative LGE at the one-year follow-up, which contrasts with previous reports suggesting that LGE without accompanying edema on a 6-month follow-up CMR indicates definitive fibrosis and irreversible myocardial injury (22, 30, 31). Nevertheless, this discrepancy may be attributed to the effect size of Quantification of LGE being less than 0.8, and the proportion of patients showing recovery was 0%, suggesting that the subtle changes in Quantification of LGE during follow-up may lack clinical significance. The subtle quantitative reduction likely reflects technical variability of the >3SD threshold or mild interstitial signal changes, rather than biological recovery of established fibrosis.
Overall, these longitudinal findings provide a reassuring prognostic outlook. The concurrent improvement in both symptoms and objective CMR parameters, together with patients’ reluctance to undergo repeat CMR due to symptom resolution, suggests that PASC-CVS follows a benign, self-limiting course in most patients without severe baseline disease.
5. Limitations
Our study has several limitations. First, it is a single-center study with a modest sample size, necessitating larger multicenter studies to validate and generalize our findings. Second, the reported prevalence of symptoms may be influenced by self-selection bias due to recruitment through invitations, potentially affecting cohort representativeness. Third, the follow-up CMR subgroup was limited in size and potentially subject to selection bias, which may have influenced the observed recovery patterns. Fourth, the absence of a histopathologic reference standard, such as endomyocardial biopsy, limits direct correlation of our pathophysiologic conclusions with histopathologic results. Future studies incorporating such standards are needed to further elucidate the underlying mechanisms.
6. Conclusion
Our study reveals myocardial involvement in PASC-CVS and supports the potential utility of multiparametric CMR, particularly through predictors such as Quantification of LGE, Global ECV, Global post T1, Global native T1, LV GLS, and heart rate, as a valuable source of imaging biomarkers for PASC-CVS. The observed improvements in clinical symptoms and CMR abnormalities (notably RV EF, Global T2, and LV GLS) at the one-year follow-up highlight the role of CMR in guiding long-term patient management. However, the persistence of symptoms in a subset of patients and the challenges in follow-up CMR completion underscore the need for further longitudinal studies to better understand the progression and prognostic implications of these findings. Future research should focus on validating these results in larger, multicenter cohorts and integrating histopathologic correlations to further elucidate the underlying mechanisms of myocardial injury in PASC-CVS.
Acknowledgments
We want to thank the teachers of the Shanghai Public Health Clinical Center for their assistance in conducting this study.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Science and Technology Commission of Shanghai Municipality (Grant No. 23TS1400600) and the National Natural Science Foundation of China (Grant No. 82172029).
Footnotes
Edited by: Francisco Miguel Vargas-Luna, University of Guanajuato, Mexico
Reviewed by: Ria Garg, Geisinger Wyoming Valley Hospital, United States
Simone Benvenuto, Institute for Maternal and Child Health Burlo Garofolo (IRCCS), Italy
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
Ethics statement
The studies involving humans were approved by Medical Ethics Committee of Shanghai Public Health Clinical Center. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. SY: Conceptualization, Investigation, Methodology, Resources, Software, Supervision, Validation, Writing – review & editing. JT: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. JS: Data curation, Investigation, Methodology, Resources, Software, Writing – original draft. YZ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft. YG: Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – original draft. ZZ: Conceptualization, Resources, Supervision, Writing – review & editing. HJ: Conceptualization, Methodology, Project administration, Resources, Supervision, Writing – review & editing. FS: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2026.1725291/full#supplementary-material
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.