Abstract
Introduction
The coronavirus disease 2019 (COVID-19) pandemic has caused significant mortality since late 2019. Patients undergoing kidney transplantation (KT) are prone to COVID-19 due to immunosuppressive drug use and various comorbidities such as hypertension and diabetes.
Methods
One hundred thirty-three KT recipients with COVID-19 were included in this retrospective cohort study. Hospital mortality was considered a primary outcome, while acute kidney injury (AKI) was considered a secondary outcome. Demographic information, maintenance immunosuppression, medical history, laboratory information, and echocardiographic and electrocardiography results of patients were recorded. Patients were also followed for 2 months post-discharge for post-COVID-19 symptoms, readmission, and transplant function.
Results
Regarding the primary outcome of the 133 patients, 13 died and 120 survived. The deceased patients were significantly older (median age, 64 vs. 50.5 years; p = 0.04) and had a significantly higher median serum creatinine level (p = 0.002) and lower median glomerular filtration rate (p = 0.010) than patients who survived. The incidence of AKI was 47.3%, more common in deceased patients (p = 0.038) than in patients who survived. Troponin levels were significantly higher in deceased patients and those with AKI (p = 0.0004 and p = 0.039, respectively) than in patients who survived and those without AKI. A multivariable Cox regression analysis revealed that older age (adjusted hazard ratio, 1.13; 95% confidence interval, 1.01–1.27) and AKI (adjusted hazard ratio, 3.43; 95% confidence interval, 1.34–8.79) were associated with in-hospital mortality.
Conclusion
In conclusion, kidney recipients with COVID-19 had a higher mortality rate than the general population, with a higher prevalence in older individuals and those who experienced AKI during hospitalization than in patients who survived and those without AKI.
Keywords: kidney injury, cardiac effects, COVID-19, kidney transplantation, mortality
Abbreviations: ACE2, angiotensin-converting enzyme 2; AKI, acute kidney injury; CCI, Charlson Comorbidity Index; CI, confidence interval; CKD, chronic kidney disease; CNI, calcineurin inhibitors; CRP, C-reactive protein; CVD, cardiovascular disease; DD, diastolic dysfunction; ECG, electrocardiography; EF, ejection fraction; GFR, glomerular filtration rate; HR, hazard ratio; ICU, intensive care unit; IQR, interquartile range; IVC, inferior vena cava; KT, kidney transplantation; LDH, lactate dehydrogenase; MMF, mycophenolate mofetil; RRT, renal replacement therapy
Summary
The research reported here focused on the importance of cardiac effects and renal outcomes in patients undergoing kidney transplantation who are prone to COVID-19-related mortality and morbidity.
1. Introduction
In December 2019, the world encountered a newly discovered infectious disease called coronavirus disease 2019 (COVID-19), which was first diagnosed in Wuhan, China. Since May 2022, a total of 514,256,035 COVID-19 cases have been confirmed worldwide, and at least 6,264,027 deaths have been reported to date (1). Iran was one of the most affected countries worldwide until May 2022, with a total of 6.9 million cases detected. Of them, 135,000 patients died and 6.5 million recovered (2). Clinically, most patients present with asymptomatic or mild flu-like symptoms. However, some experience a severe manifestation of acute respiratory syndrome that requires mechanical ventilation and intensive care unit (ICU) hospitalization associated with a high risk of mortality (3). Patients with underlying diseases, such as cardiovascular disease (CVD), lung cancer, chronic kidney disease (CKD), hypertension, and obesity, are at increased risk of severe COVID-related disease and death (4). In addition to receiving immunosuppressive drugs, kidney transplantation (KT) recipients suffer from chronic diseases such as CVD, high blood pressure, and diabetes (5). There are many unresolved questions about COVID-19 in this population, including its effects on graft function, the risk factors of in-hospital mortality among kidney recipients, and whether cardiac complications occur among renal transplant recipients with COVID-19.
2. Objectives
This study evaluated the morbidity and mortality rates of COVID-19 in KT recipients at the kidney transplant center of Labbafinezhad Hospital in Iran during the 2-year COVID-19 pandemic.
3. Material and methods
3.1. Study design
All KT recipients diagnosed with COVID-19 and admitted to Labbafinezhad Hospital in Tehran, Iran, from December 2019 to September 2021 were enrolled in this retrospective cohort study. This study was approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran (IR.SBMU.MSP.REC.1399.003).
3.2. Study subjects
A total of 140 confirmed COVID-19 cases were identified based on a positive nasopharyngeal real-time reverse-transcriptase polymerase chain reaction test. A total of 140 adult kidney recipients (aged >18 years) admitted with confirmed COVID-19 during the study period were included. We excluded seven patients on dialysis with a history of KT.
3.3. Data collection
Trained medical personnel gathered data from the patients' medical records using a research-made checklist. The checklist included information such as demographic characteristics, smoking status, past medical history, maintenance immunosuppression, clinical presentation, time from transplantation to sudden acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection, baseline clinical characteristics before COVID-19 (baseline creatinine and baseline glomerular filtration rate [GFR], laboratory data on admission, echocardiography and electrocardiography [ECG] findings during hospitalization, and in-hospital medical management). Echocardiographic findings consisted of ejection fraction (EF), diastolic dysfunction (DD), right ventricular function, presence of pericardial effusion, and inferior vena cava (IVC) collapsibility, considered abnormal when the IVC diameter was >1.7 cm and respiratory collapsibility was <50%, which was used as a marker of volume status. ECG findings included rhythm type, presence of a bundle branch block, RR interval (time that elapsed between two successive R-waves of the QRS), and corrected QT interval calculated based on Hodges formula (QTc = QT + 1.75[heart rate - 60]). Outcomes of acute kidney injury (AKI), increase in serum creatinine by ≥0.3 mg/dL within 48 h, need for renal replacement therapy (RRT) during admission, and in-hospital mortality were extracted. Additionally, the Charlson Comorbidity Index (CCI) was used as a weighted index for patients with comorbid conditions (6).
3.4. Follow-up and outcomes
Risk factors for mortality and outcome predictors were evaluated. The primary outcome was in-hospital mortality, while the secondary outcome was AKI during hospitalization.
Finally, in the follow-up phase of the study, participants were followed up for 2 months to assess post-COVID-19 complications such as chronic COVID-19-related symptoms, hospital readmission, and allograft function.
3.5. Statistical analysis
The normality of continuous variables was assessed using the Kolmogorov–Smirnov test and Q-Q plot. Continuous variables are described as median and interquartile range (IQR), while categorical variables are described as frequency and percentage.
Student's t-test or the Mann–Whitney U test was used to compare the mean of each continuous variable between non-survivors and survivors. To compare the frequency of the different categorized variables between groups, appropriate statistical tests such as Fisher's exact test or the chi-squared test was used.
Univariate and multivariable Cox regression models were used to identify the association between under-researched factors and in-hospital mortality. For selecting the best variables to enter the last multivariable model, a backward stepwise approach with a value of p < 0.2 was used. In addition, the sex variable that did not have statistical criteria for entering the multivariable model (p > 0.2) due to approved clinical effects and the probable role of residual confounding of this variable was adjusted in the last multivariable model. The proportional hazards assumption was verified using the Schoenfeld residual test. The last multivariable Cox regression model was fitted following the least amount of Akaike Information Criterion and Bayesian Information Criterion, with a log likelihood value closer to zero. All of the statistical analyses were conducted at a significance level of <0.05, with a 95% confidence interval (CI). The analysis was performed using the STATA software version 14.
4. Results
4.1. Demographic and baseline characteristics
A total of 133 KT recipients (80 men [60.15%]) were admitted with confirmed COVID-19 during the study period, with a median age of 52 years (IQR, 41–63 years). Sixteen patients (12.03%) underwent transplantation for the second or third time. The median time between the last transplantation and COVID-19 admission was 108 months (IQR, 48–108 months). The most prevalent comorbid conditions among patients were hypertension (n = 85 [63.91%]), diabetes (n = 38 [28.57%]), and ischemic heart disease (n = 14 [10.53%]), with a CCI of 3. Thirteen in-hospital deaths occurred (9.77%). The deceased patients had a significantly higher median age (p = 0.004) and were predominantly male, although the difference was not statistically significant (10/13 [76.92%]; p = 0.243). The time since transplantation was longer among those who did not survive COVID-19 (p = 0.029). The median baseline creatinine level in the cohort during the months before hospitalization was 1.4 mg/dL (IQR, 1.1–1.9). However, patients who did not survive had significantly higher median serum creatinine levels (p = 0.002) and a lower median GFR (p = 0.010) than those who did. The most common immunosuppressive treatment among KT recipients is a triple-drug regimen with calcineurin inhibitors (CNIs), mycophenolate mofetil (MMF), and prednisolone. None of the immunosuppressive drugs were associated with an increased risk of mortality (p > 0.05). The patients' demographic and medical characteristics are shown in Table 1 .
Table 1.
Demographic and past medical history characteristics of COVID-19 patients with history of kidney transplantation.
| Variables | All patients (n = 133) | Alive (n = 120) | Died (n = 13) | P-value |
|---|---|---|---|---|
| Demographic characteristics | ||||
| Age (years) | 52 (41–63) | 50.5 (40–62.5) | 64 (56–67) | 0.004 |
| Sex | 0.243 | |||
| Men | 80 (60.15) | 70 (58.33) | 10 (76.92) | |
| Female | 53 (39.85) | 50 (41.67) | 3 (23.08) | |
| Smoker (Yes) | 7 (5.26) | 6 (5.00) | 1 (7.69) | 0.522 |
| Underlying diseases (Yes) | ||||
| Hypertension | 85 (63.91) | 76 (63.33) | 9 (69.23) | 0.769 |
| Ischemic Heart Diseases (IHD) | 14 (10.53) | 11 (9.17) | 3 (23.08) | 0.140 |
| Heart Failure (HF) | 7 (5.30) | 6 (5.04) | 1 (7.69) | 0.525 |
| Diabetes | 38 (28.57) | 32 (26.67) | 6 (46.15) | 0.193 |
| Glomerulonephritis (GN) | 5 (3.76) | 5 (4.17) | 0 (0.0) | 1.000 |
| Autosomal Dominant Polycystic Kidney Disease (ADPKD) | 7 (5.26) | 6 (5.00) | 1 (7.69) | 0.522 |
| Frequent kidney transplantation |
1.000 |
|||
| First | 117 (87.97) | 105 (87.50) | 12 (92.31) | |
| Second | 13 (9.77) | 12 (10.00) | 1 (7.69) | |
| Third | 3 (2.26) | 3 (2.50) | 0 (0.00) | |
| Time between last kidney transplantation to hospital admission (months) | 108 (48–180) | 108 (48–174) | 168 (108–228) | 0.029 |
| Cancer | 2 (1.50) | 2 (1.67) | 0 (0.00) | 1.000 |
| Chronic pulmonary disease | 2 (1.50) | 2 (1.67) | 0 (0.00) | 1.000 |
| Charlson Comorbidity Index (CCI) | 3 (2–4) | 3 (3–4) | 3 (2–3) | 0.312 |
| Baseline laboratory results before admission | ||||
| Creatinine (mg/dL) | 1.4 (1.1–1.9) | 1.38 (1.1–1.8) | 2.16 (1.7–2.65) | 0.002 |
| GFR (mL/min) | 54 (38–71) | 56 (39–71) | 42 (26–46) | 0.010 |
| Drug history (yes) | ||||
| Cyclosporine | 75 (56.39) | 67 (55.83) | 8 (61.54) | 0.649 |
| Tacrolimus | 46 (36.48) | 44 (36.67) | 5 (38.46) | 0.899 |
| MMF/MPA | 132 (99.25) | 119 (99.17) | 13(100) | 1.000 |
| Prednisolone | 131 (98.50) | 119 (99.17) | 12(92.31) | 0.187 |
| mTOR Inhibitor | 8 (6.02) | 8 (6.67) | 0 (0.00) | 1.000 |
| ACEI/ARB | 61 (45.86) | 54 (45.00) | 7 (53.85) | 0.543 |
| NOAC | 3 (2.26) | 2 (1.67) | 1 (7.69) | 0.267 |
| B-Blocker | 40 (30.08) | 35 (29.17) | 5 (38.46) | 0.488 |
| ASA | 34 (25.56) | 29 (24.17) | 5 (38.46) | 0.262 |
| Values are n(%), median (Q1-Q3) | ||||
mTOR: mammalian target of rapamycin, ACEI: angiotensin converting enzyme inhibitor, ARB: angiotensin receptor blocker, NOAC: novel oral anticoagulants.
4.2. Clinical presentations, laboratory results, and in-hospital medications
The patients' clinical presentations, laboratory results, and in-hospital medical treatment are presented in Table 2 . The most prevalent signs and symptoms were cough (n = 81 [60.90%]), fever (n = 78 [58.65%]), and dyspnea (n = 68 [51.13%]). The median time from symptom initiation to admission was 7 days (IQR, 3–10). There was no statistically significant difference in the signs and symptoms at presentation between survivors and non-survivors (p > 0.05). However, patients who did not survive had lower oxygen saturation (SpO2) levels on admission (median [IQR], 91 [87–92] vs. median [IQR], 94 [91–96]; p = 0.003) than those who did.
Table 2.
Clinical presentations, laboratory results and in-hospital medication of COVID-19 patients with history of kidney transplantation.
| Variables | All patients (n = 133) | Survivors (n = 120) | Non-survivors (n = 13) | P-value |
|---|---|---|---|---|
| Signs and Symptoms in admission (Yes) | ||||
| Dyspnea | 68 (51.13) | 64 (53.33) | 4 (30.77) | 0.150 |
| Cough | 81 (60.90) | 76 (63.33) | 5 (38.46) | 0.122 |
| Fever | 78 (58.65) | 73 (60.83) | 5 (38.46) | 0.120 |
| Gastrointestinal Symptoms | 56 (42.11) | 50 (41.67) | 6 (42.11) | 0.756 |
| Bradycardia | 6 (4.51) | 0 (0.00) | 6 (5.00) | 1.000 |
| Time between being symptomatic to hospital admission (days) | 7 (3–10) | 7 (3–9.5) | 7 (4–12) | 0.509 |
| Hemodynamic assessment | ||||
| Systolic blood pressure (mmHg) | 120 (110–135) | 120 (110–133.5) | 125 (110–140) | 0.583 |
| Diastolic blood pressure (mmHg) | 77 (70–80) | 77 (70–80) | 75 (70–80) | 0.701 |
| Pulse Rate (PR, pulse / min) | 85 (78.5–97.5) | 85 (78–99) | 80 (80–88) | 0.799 |
| Respiratory Rate (RR, per 1/min) | 20 (18–22) | 19.5 (18–21.5) | 20 (18–24) | 0.218 |
| SPO2 (%) | 93 (91–96) | 94 (91–96) | 91 (87–92) | 0.003 |
| Laboratory values (In admission) | ||||
| White Blood Cell (WBC, cells per cubic millimeter (cmm)) | 5500 (4100–7600) | 5500 (4050–7500) | 5200 (4700–8200) | 0.758 |
| Neutrophil (%) | 80 (72–86) | 80 (70.5–85) | 89 (80–95) | 0.012 |
| Lymphocyte (%) | 16 (10–25) | 16 (10–25) | 10 (5–16) | 0.018 |
| Platelets (per microliter of blood) | 173,000 (132000–210,000) | 178,500 (138500–227,000) | 126,000(84000–161,000) | 0.011 |
| Calcium (serum, mg/dL) | 8.5 (8–9) | 8.5 (8–9) | 7.5 (7.2–8.3) | 0.0006 |
| Potassium (K, mmol/L) | 4.5 (4–4.9) | 4.5 (4–4.9) | 4.8 (4.6–5.2) | 0.085 |
| Magnesium (Mg, mmol/L) | 1.8 (1.5–2.2) | 1.8 (1.5–2.2) | 2 (1.7–3) | 0.161 |
| C-Reactive Protein (CRP,mg/L) | 31 (14–59) | 25.5 (12.5–51.5) | 70 (52–80) | 0.0002 |
| Ferritin (ng/mL) | 443.5 (322–589) | 443 (277–572) | 554 (440–654) | 0.045 |
| D-Dimer (ng/mL) | 361.5 (166–850.5) | 345 (164–799) | 629 (221–2126) | 0.257 |
| Procalcitonin (ng/mL) | 0.4 (0.1–0.7) | 0.3 (0.09–0.6) | 0.65 (0.18–9.9) | 0.157 |
| Serum Interleukin-6 (IL-6, pg/ml) | 35.4 (12.9–102) | 22.35 (8.4–58.5) | 92.7 (18.8–172) | 0.070 |
| Lactate Dehydrogenase (LDH, U/L) | 444 (357–570) | 435 (354–531) | 682 (533–811) | 0.0005 |
| Creatinine (mg/dL) | 1.72 (1.33–2.79) | 1.7 (1.3–2.35) | 2.9 (2.34–3.9) | 0.004 |
| Albumin (g/dL) | 3.4 (3.1–3.7) | 3.4 (3.2–3.8) | 3 (2.5–3.4) | 0.005 |
| Bilirubin (mg/dL) | 0.7 (0.5–1) | 0.7 (0.5–0.9) | 0.9 (0.7–1.2) | 0.077 |
| Aspartate Aminotransferase (AST, U/L) | 26 (19–35) | 25 (19–35) | 30 (28–35) | 0.062 |
| Alanine Transaminase (ALT,U/L) | 19 (13–30) | 19.5 (13–30) | 19 (15–24) | 0.749 |
| Troponin I (ng/mL) | 0.004 (0.001–0.01) | 0.003 (0.001–0.01) | 0.075 (0.008–0.3) | 0.0004 |
| In-hospital medications/ procedures (Yes) | ||||
| Cyclosporine / dose reduction (n = 81) | 40 (49.38) | 34 (47.22) | 6 (66.67) | 0.312 |
| Cyclosporine / DC (n = 81) | 9 (11.11) | 6 (8.33) | 3 (33.33) | 0.058 |
| Tacrolimus /dose reduction (n = 57) | 22 (38.60) | 22 (41.51) | 0 (0.00) | 0151 |
| Tacrolimus /DC (n = 55) | 5 (9.09) | 1 (1.96) | 4 (100.00) | < 0.001 |
| MMF/DC (n = 132) | 120 (90.91) | 108 (90.76) | 12 (92.31) | 1.000 |
| Methyl pulse (total dose, n = 133) | 54 (40.60) | 45 (37.50) | 6 (69.23) | 0.037 |
| Dexamethasone (n = 133) | 63 (47.37) | 54 (45.00) | 9 (69.23) | 0.143 |
| Hydrocortisone (n = 133) | 34 (25.56) | 30 (25.00) | 4 (30.77) | 0.739 |
| Tocilizumab (n = 133) | 6 (4.51) | 3 (2.50) | 3 (23.08) | 0.012 |
| Time of Tocilizumab administration (day) | 7 (7–9) | 9 (7–9) | 7 (3–7) | 0.099 |
| Remdesivir (n = 133) | 73 (54.89) | 64 (53.33) | 9 (69.23) | 0.382 |
| Time between symptoms onset to Remdesivir treatment (days) | 2 (0–8) | 1.5 (0–8) | 7 (0–12) | 0.135 |
| Time between hospitalization to Remdesivir treatment (days) | 1 (0–1) | 1 (0–1) | 1 (0–1) | 0.297 |
| Plasmapheresis (n = 133) | 31 (23.31) | 19 (15.83) | 12 (92.31) | < 0.001 |
| Hemoperfusion (n = 133) | 5 (3.76) | 1 (0.83) | 4 (30.77) | < 0.001 |
| Anticoagulant drug (Prophylaxis, n = 133) | 100 (75.19) | 95 (79.17) | 5 (38.46) | 0.001 |
| Anticoagulant drugs (Treatment, n = 133) | 30 (22.56) | 22 (18.33) | 8 (61.54) | < 0.001 |
| Heparin (n = 133) | 94 (70.68) | 83 (69.17) | 11 (84.62) | 0.344 |
| NOAC (n = 132) | 10 (7.58) | 9 (7.56) | 1 (7.69) | 1.000 |
| LMWH (n = 133) | 31 (23.31) | 29 (24.17) | 2 (15.38) | 0.732 |
| Values are n(%), median (Q1-Q3) | ||||
LMWH: low molecular weight heparin.
Non-survivors had a higher neutrophil count (p = 0.012) and C-reactive protein (CRP; p = 0.0002), ferritin (p = 0.045), lactate dehydrogenase (LDH; p = 0.0005), creatinine (p = 0.004), and troponin I (p = 0.0004) levels as well as a lower lymphocyte count (p = 0.018), platelet count (p = 0.011), and serum calcium (p = 0.0006) and albumin (p = 0.005) levels than survivors.
Immunosuppressive treatment was administered during the hospitalization. Of the 81 patients treated with the cyclosporine-based regimen and 57 patients treated with the tacrolimus-based regimen, the CNI dose was reduced in 49.38% and 38.60%, respectively. Antimetabolites (MMF or azathioprine) and mammalian target of rapamycin inhibitors (sirolimus) were withdrawn in more than 90% of patients. In addition, CNI inhibitors were discontinued in five patients, four of whom did not survive. Among the patients with severe COVID-19, 40.6% were treated with high-dose steroids (methylprednisolone), while 4.51% were treated with tocilizumab, which was associated with decreased in-hospital mortality (p < 0.05). Of all patients with COVID-19, 55% received antiviral treatment.
Therapeutic plasmapheresis was performed in 31 critically ill patients, 19 of whom survived (61.2%; p < 0.001). Five critically ill patients were treated with hemoperfusion; of them, one survived (p < 0.001).
Ninety-five of 100 patients who received prophylactic anticoagulants survived (p = 0.001). Of 30 patients treated with therapeutic doses of anticoagulants, 22 (73.35%) survived (p < 0.001; Table 2).
Assessment of the ECG results revealed a median RR interval of 720 ms (IQR, 600–800). Patients who died had a shorter median RR interval (p = 0.016) than those who did not. Mild DD was the most common echocardiographic finding (n = 62 [61.39%]) in the cohort. Non-survivors had a significantly lower median EF (p = 0.016) than survivors. The prevalence of a decreased IVC collapsibility index as a marker of volume overload was 24.36%. Overall, 76.92% and 50% of patients in the mortality group had mild DD and abnormal IVC collapsibility during hospitalization, respectively (p < 0.05; Table 3 ).
Table 3.
Cardiac examinations and in-hospital outcomes of COVID-19 patients with history of kidney transplantation.
| Variables | All patients (n = 133) | Survivors (n = 120) | Non-survivors (n = 13) | P-value |
|---|---|---|---|---|
| ECG results | ||||
| AF (Yes) | 4 (3.05) | 3 (2.54) | 1 (7.69) | 0.345 |
| BBB (Yes) | 6 (4.62) | 6 (5.13) | 0 (0.00) | 1.000 |
| QT interval | 360 (320–400) | 360 (320–400) | 360 (360–400) | 0.300 |
| PR interval | 720 (600–800) | 720 (600–800) | 680 (600–700) | 0.016 |
| Echocardiography results | ||||
| Ejection Fraction (EF, %) | 50 (50–55) | 52.5 (50–55) | 50 (45–50) | 0.014 |
| Diastolic Dysfunction (DD) | ||||
| Normal | 38 (37.62) | 36 (40.91) | 2 (15.38) | |
| Mild | 62 (61.39) | 52 (59.09) | 10 (76.92) | 0.020 |
| Moderate to severe | 1 (0.99) | 0 (0.00) | 1 (7.69) | |
| RV size | ||||
| Abnormal | 9 (11.39) | 7 (10.45) | 2 (16.67) | 0.620 |
| Normal | 70 (88.61) | 60 (89.55) | 10 (83.33) | |
| RV function | ||||
| Abnormal | 7 (8.86) | 6 (8.96) | 1 (8.33) | 1.000 |
| Normal | 72 (91.14) | 61 (91.04) | 11 (91.67) | |
| IVC collapsibility | ||||
| Abnormal | 19 (24.36) | 13 (19.70) | 6 (50.00) | 0.024 |
| Normal | 59 (75.64) | 53 (80.30) | 6 (50.00) | |
| Plural Effusion (PE,Yes) | 10 (12.66) | 7 (10.45) | 3 (25.00) | 0.173 |
| In-hospital outcomes | ||||
| Length of hospital stay (days) | 8 (5–12) | 7 (5–10) | 16 (10–24) | 0.0002 |
| ICU admission (Yes) | 22 (16.54) | 9 (7.50) | 13 (100.00) | < 0.001 |
| ICU duration (days) | 9 (4–15) | 5 (3–11) | 10 (7–19) | 0.123 |
| Non-Invasive Ventilation (NIV, Yes) | 11 (8.27) | 4 (3.33) | 7 (53.85) | < 0.001 |
| Endotracheal intubation (Yes) | 13 (9.77) | 1 (0.83) | 12 (92.31) | < 0.001 |
| Acute Kidney Injury (AKI, Yes) | 63 (47.37) | 53 (44.17) | 10 (76.92) | 0.038 |
| AKI requiring RRT (Yes) | 9 (6.77) | 3 (2.50) | 6 (46.15) | < 0.001 |
| Values are n(%), median (Q1-Q3) | ||||
| Troponin I and AKI association | ||||
| Troponin I | 0.04(0.001–0.01) | 0.006(0.0015–0.013) | 0.002(0.001–0.01) | 0.039 |
4.3. In-hospital outcomes
The median hospitalization duration in the study group was 8 days (IQR, 5–12 days). This duration was longer among non-survivors (median, 16 days [IQR, 10–24]; p = 0.0002) than among survivors. Of the 133 patients admitted to the ICU, 13 (9.77%) died. The incidence of AKI during hospitalization (47.37%) was more prevalent among non-survivors (p = 0.038) than among survivors. In addition, of 63 patients with AKI during hospitalization, 9 (19.29%) required RRT and eventually six died (p < 0.001; Table 3).
Regarding the relationship between troponin I levels and the incidence of AKI, troponin levels were significantly higher in patients with AKI (p = 0.039) than in those without AKI. However, there was no statistically significant relationship between abnormal IVC collapsibility and AKI events (p = 0.065; Table 3).
4.4. Risk factors for in-hospital mortality
The results of the univariate and multivariate Cox regression analyses are summarized in Table 4 . The univariate Cox regression analysis showed a significant association between aspartate transaminase (AST) (p = 0.016), creatinine (p = 0.003), SpO2 (p = 0.002), anticoagulant prophylaxis during hospitalization (p = 0.007), and time since transplantation (p = 0.03) with in-hospital mortality. After adjusting for potential factors, multivariate Cox regression analysis revealed that older age (adjusted hazard ratio [HR], 1.13; 95% CI, 1.01–1.27) and AKI (adjusted HR, 3.43; 95% CI, 1.34–8.79) were associated with in-hospital mortality. High SpO2 levels on admission (adjusted HR, 0.79; 95% CI, 0.66–0.94), female sex (adjusted HR, 0.01; 95% CI, 0.0003–0.71), and anticoagulant prophylaxis (adjusted HR, 0.06; 95% CI, 0.005–0.68) during hospitalization were negatively associated with in-hospital mortality among KT recipients with COVID-19 disease (Table 4).
Table 4.
Factors associated with in-hospital mortality due to SARS-Cov2 infection among kidney transplantation patients based on univariate and multivariable Cox regression model.
| Variables | Crude HR*, 95% CI | P-value | Adjusted HR*, 95% CI | P-value |
|---|---|---|---|---|
| Age (years) | 1.03 (0.99–1.09) | 0.116 | 1.13 (1.01–1.27) | 0.031 |
| Sex | 0.397 | 0.033 | ||
| Male | Reference | Reference | ||
| Female | 0.57 (0.15–2.08) | 0.01 (0.0003–0.71) | ||
| Charlson Comorbidity Index (CCI) | 0.63 (0.38–1.05) | 0.081 | 1.09 (0.46–2.55) | 0.835 |
| Aspartate aminotransferase (AST, U/L) | 1.01 (1.003–1.03) | 0.016 | 1.01 (0.97–1.06) | 0.451 |
| Creatinine level in-admission (mg/dL) | 1.61 (1.18–2.21) | 0.003 | 3.43 (1.34–8.79) | 0.010 |
| ** SPO2 (%) | 0.89 (0.82–0.95) | 0.002 | 0.79 (0.66–0.94) | 0.009 |
| Anticoagulant drug (Prophylaxis) | 0.007 | 0.023 | ||
| No | Reference | Reference | ||
| Yes | 0.15 (0.04–0.60) | 0.06 (0.005–0.68) | ||
| Time between kidney transplantation to SARS-CoV2 infection (months) | 1.007 (1.0007–1.01) | 0.030 | 1.004 (0.98–1.02) | 0.708 |
| Acute Kidney Injury (AKI) during hospitalization | 0.112 | 0.805 | ||
| No | Reference | Reference | ||
| Yes | 2.86 (0.78–10.45) | 1.44 (0.07–26.69) |
*Hazard Ratio.
** *Oxygen saturation measured by pulse oximetry.
Proportionality assumption was checked based on Schoenfeld residual test, P-value = 0.4004.
4.5. Post-COVID-19 complications after discharge
Of the 120 discharged KT recipients, approximately 43 were lost to follow-up. Among 77 available patients, the most common post-COVID-19 complications were fatigue (n = 32 [42.11%]), hospital readmission (n = 13 [16.88%]), and loss of taste (ageusia) or smell (anosmia) (n = 9 [11.69%]). In addition, the median serum creatinine level at 2 months post-discharge was 1.4 mg/dL (IQR, 1.2–1.7). A significant improvement in renal function was noted after discharge with respect to the value on admission (p = 0.0008).
5. Discussion
COVID-19 is a global health issue associated with higher morbidity and mortality rates among patients with underlying comorbidities, including hypertension, diabetes, CVD, and CKD (4). Adult patients with primary and secondary immunosuppressive conditions are at a greater risk of developing severe COVID-19 and mortality than the general population (7). The present study was designed to determine the effect of COVID-19 on KT recipients, including graft function, cardiac involvement, outcomes, and factors associated with poor survival.
Our retrospective cohort study found that the COVID-19-related mortality rate in KT recipients was 9.77%, slightly higher than the average overall 6.8% mortality rate in the general population (8). This finding may be partly related to comorbidities such as hypertension, diabetes, ischemic heart disease, and immunosuppressive therapy (9). There was a remarkable male predominance in our cohort, similar to that in the general population, and men faced a mortality risk that was 2.4 times higher than that of women. A possible explanation for this might be the higher circulating angiotensin-converting enzyme 2 (ACE2) level in men, which is noteworthy as SARS-CoV-2 attacks cells via the ACE2 receptor (10). A higher COVID-19 mortality rate was reported among patients older than 50 years, especially those older than 60 years, in the general population, which was consistent with our results.
Based on our findings, the most prevalent signs and symptoms were similar to those observed in the general adult population (11). Although the presenting signs and symptoms did not differ between survivors and non-survivors, a low SpO2 at presentation was associated with an increased mortality rate. Recent evidence indicated that inflammatory responses play a pivotal role in the pathogenesis and outcomes of COVID-19. Thus, inflammatory markers such as procalcitonin, CRP, erythrocyte sedimentation rate, ferritin, d-dimer, and interleukin-6 (IL-6) could be considered predictors of COVID-19 severity and mortality (12). Similarly, this study found higher levels of CRP, ferritin, and LDH in non-survivors than in survivors. Neutrophil count, creatinine level, and cardiac enzyme level were higher, whereas lymphocyte and platelet counts and serum calcium and albumin levels were lower among non-survivors than among survivors. In addition, patients who died had a significantly lower median GFR at admission. In agreement with our data, a case series study indicated that lymphopenia and elevated ferritin, d-dimer, and troponin levels were observed in critical cases of COVID-19 (13). IL-6 and procalcitonin levels were markedly elevated in transplant recipients with severe COVID-19 and identified as risk factors for mortality (14), but we detected no difference in IL-6 and procalcitonin levels between survivors and non-survivors. These results must be interpreted cautiously, as a limited number of patients were evaluated for IL-6. Cravedi et al. indicated that baseline lymphocyte count and estimated GFR were significantly lower and LDH levels were higher in non-survivors, similar to our results. However, they reported no intergroup differences in white blood cell count, platelet count, or hemoglobin levels (15).
It remains unclear whether immunosuppressive treatment is an independent risk factor for a poor prognosis of COVID-19. In our study, none of the immunosuppression regimens were associated with mortality. This result is in accordance with those of two recent studies that indicated no impact of immunosuppression intensity on mortality rate (16). However, our previous study of a limited number of cases demonstrated a lower rate of admission among KT recipients than in the general population, particularly in more recent transplantations, which might imply the protective effects of immunosuppressive agents against cytokine storm activation due to COVID-19 (17). The current study had a median interval of 108 months between KT and COVID-19. The longer the duration of transplantation, the lower the level of immunosuppressive drugs.
According to our center protocol, this study applied a CNI dose reduction in 49.38% of patients treated with cyclosporine and 38.60% of patients treated with tacrolimus. Previous studies of solid organ transplant recipients showed that CNIs and antimetabolites were present in 18–29% and 66–88% of patients during the clinical course of COVID-19, respectively (18). Immunosuppressive drug management is a double-edged sword: its reduction can result in an increased risk of rejection, while its continuation may lead to severe illness. Therefore, managing immunosuppression in KT recipients remains challenging, and clinicians should make case-by-case decisions and assess the risks versus benefits of continuing immunosuppression using a multidisciplinary approach. However, it has been recommended that, among COVID-19 cases with mild involvement, antimetabolite agents (MMF and azathioprine) be discontinued at the time of hospital admission, but low doses of CNIs continued due to possible immunomodulatory effects (19); moreover, stress doses of prednisolone should be considered. In severe infection cases, an argument can be made for discontinuing antimetabolites and reducing CNIs while administering high doses of corticosteroid plus intravenous immunoglobulin (20).
Given the role of cytokine storm and inflammation in COVID-19 pathology, several studies reported that tocilizumab, an anti-IL-6 monoclonal antibody, could have beneficial effects in reducing inflammatory parameters and improving clinical symptoms (21). Our study showed that methylprednisolone pulse therapy and tocilizumab were associated with reduced in-hospital mortality rates.
Plasmapheresis has been suggested as a helpful approach in severe cases by alleviating inflammatory cytokine storm and decreasing viral load (22). Most individuals who underwent plasmapheresis and hemoperfusion in this study were critically ill, but their survival rate improved. This may be the result of prescribing blood purification techniques late in the disease course.
Thromboembolic events frequently occur in hospitalized patients with COVID-19, which might result in a higher risk of in-hospital mortality (23); therefore, most existing evidence indicates that prophylactic or therapeutic doses of anticoagulants are associated with a lower mortality risk (24). In this study, patients who received prophylactic or therapeutic doses of anticoagulants showed lower mortality rates.
COVID-19 is potentially associated with various ECG abnormalities and a consequent poor prognosis, including atrial fibrillation, QT interval prolongation, ST segment and T wave changes, and ventricular arrhythmia. However, sinus tachycardia is the most common abnormality observed (25). The current study demonstrated that the median RR interval was significantly lower among non-survivors than among living patients, while other ECG abnormalities were not prevalent in our patients. According to echocardiographic findings, survivors had a higher median EF in compression than non-survivors. Echocardiography performed in most cases of COVID-19 in the general population revealed a decreased EF, with a more severe EF reduction associated with worse prognosis (26). In this study, the positive troponin I level as a marker of myocardial involvement was significantly higher in survivors with AKI events and non-survivors, which may make it a prognostic factor. Reducing IVC collapsibility was also more frequent among non-survivors than among survivors. The IVC collapsibility index is a valuable marker for evaluating volume status, which can commonly occur in AKI events and may indicate upcoming cardiorenal worsening and poor prognosis; COVID-19 can be an essential cause in this regard (27).
The ICU admission rate among hospitalized patients in the general population was reportedly 8.6–34% in different cohort studies, while largest cohort studies reported non-invasive ventilation or intubation rates of 30–39%. Intubation predicted poor outcomes with a 40–100% mortality rate among patients on ventilation (28). All non-survivors in this study were admitted to the ICU, and the application of non-invasive ventilation and endotracheal intubation was more prevalent among non-survivors than among survivors. Also, the incidence of AKI during hospitalization was 47.37%, higher among non-survivors than among survivors. AKI is common in patients with COVID-19 with a wide reported range; however, AKI during hospitalization was more common among transplant recipients than among non-transplant patients admitted for COVID-19 (20% vs. 5%), especially among critically ill patients (29). ACE2, a SARS-CoV-2 receptor, is expressed in proximal tubule cells. Therefore, uptake of the virus into the proximal tubular epithelium is a possible explanation for this phenomenon.
Further adjustment analysis demonstrated that AST, creatinine, SpO2, prophylactic anticoagulant drugs during hospitalization, and the time between KT and SARS-CoV2 infection were associated with changes in mortality. In other words, rising creatinine and ALT levels during hospitalization increased the risk of in-hospital mortality, while an increase in SpO2 level and the receipt of prophylactic anticoagulants during hospitalization prevented in-hospital mortality. Our previous study of 2493 KT recipients demonstrated that a history of acute rejection during the past 12 months; diabetes; high neutrophil-to-lymphocyte ratio; low platelet count; elevated CRP, LDH, troponin, d-dimer levels; and a prolonged prothrombin time were associated with mortality and poor outcomes (17). A cohort study by Favà et al. demonstrated that a higher baseline LDH level at admission, obesity, or acute respiratory distress syndrome conferred a higher risk of death among KT recipients with COVID-19 (16). The persistence of symptoms, including fatigue and dyspnea for more than 60 days, was reported in a non-transplant population in northern Italy (30). In the present study, among surviving discharged KT recipients, fatigue, hospital readmission, and ageusia or anosmia were the most post-COVID-19 complications.
6. Conclusion
In conclusion, KT recipients with COVID-19 had a higher mortality rate than the general population, with a greater prevalence among older individuals and those who experienced AKI during hospitalization than among younger patients and those who did not have AKI. Non-survivors had a significantly lower EF and higher volume overload markers, such as a dilated IVC and reduced collapsibility index, than survivors.
Authorship
Study conception: Shiva Samavat.
Data curation: Hossein Amini, Shadi Ziaie, Nooshin Dalili, Shideh Anvari, and Elham Keykha.
Supervision: Bahareh Hajibaratali.
Method development: Shiva Samavat.
Drafting or revising article critically for important intellectual content: Malihe Rezaee.
Final approval of version to be submitted: Bahareh Hajibaratali and Hossein Amini.
Declaration of Competing Interest
None.
Acknowledgment
This study was supported by Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Iran. No specific grants from funding agencies or the commercial or nonprofit sectors were received.
Data availability
Data will be made available on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data will be made available on request.