A Comprehensive Comparison of Clinical Presentation and Outcomes of Kidney Transplant Recipients with COVID-19 during Wave 1 versus Wave 2 at a Tertiary Care Center, India
Data comparing the clinical spectrum of COVID-19 in kidney transplant recipients (KTRs) during the first and second waves of the pandemic in India is limited. Our single-center retrospective study compared the clinical profile, mortality, and associated risk factors in KTRs with COVID-19 during the 1st wave (1st February 2020 to 31st January 2021) and the second wave (1st March-31st August 2021). 156 KTRs with PCR confirmed SARS-CoV-2 infection treated at a tertiary care hospital in New Delhi during the 1st and the second waves were analyzed. The demographics and baseline transplant characteristics of the patients diagnosed during both waves were comparable. Patients in the second wave reported less frequent hospitalization, though the intensive care unit (ICU) and ventilator requirements were similar. Strategies to modify immunosuppressants such as discontinuation of antinucleoside drugs with or without change in calcineurin inhibitors and the use of steroids were similar during both waves. Overall patient mortality was 27.5%. The demographics and baseline characteristics of survivors and nonsurvivors were comparable. A higher percentage of nonsurvivors presented with breathing difficulty, low SpO2, and altered sensorium. Both wave risk factors for mortality included older age, severe disease, ICU/ventilator requirements, acute kidney injury (AKI) needing dialysis, Chest Computerized Tomographic (CT) scan abnormalities, and higher levels of inflammatory markers particularly D-dimer and interleukin-6 levels. Conclusions. KTRs in both COVID-19 waves had similar demographics and baseline characteristics, while fewer patients during the second wave required hospitalization. The D-dimer and IL-6 levels are directly correlated with mortality.
Coronavirus disease 2019 (COVID-19), an infectious disease first identified in 2019 in Wuhan, China, has since spread worldwide [1–5]. The World Health Organization (WHO) declared the coronavirus outbreak a pandemic on 11th March 2020 . COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is mainly spread by infected persons during close contact and via respiratory droplets produced when people cough or sneeze [1, 2, 7]. Infected individuals develop flu-like symptoms that include, but are not limited to, sore throat, fever, cough, runny nose, sneezing, loss of smell, fatigue, and shortness of breath [2, 8–10]. Severe cases display symptoms such as difficulty in breathing, persistent chest pain or pressure, and confusion. They can progress to a more severe and systemic disease characterized by pneumonia, Acute Respiratory Distress Syndrome (ARDS), sepsis and septic shock, multiorgan failure, including acute kidney injury (AKI), and cardiac and cerebrovascular injury with fatal outcomes [2, 8–10]. Age more than 60 years and underlying comorbidities such as diabetes, hypertension, cerebrovascular disease, cardiac disease, chronic lung disease, chronic kidney disease, immune suppression, and cancer are major risk factors associated with the severe form of COVID-19 [11–14].
According to WHO, as of 5th November 2021, 248,467,363 confirmed cases of COVID-19, including 5,027,183 deaths, have been reported globally . In India, the first case was detected on 30th January 2020, and since then, the numbers have steadily increased; on 5th November 2021, a total of 34,366,987confirmed COVID-19 cases, including 1,42,826 active cases, 33,763,104 cured/discharged individuals, and 4,61,057 deaths, were reported . The pandemic spread in different countries across the world at different timelines and with varied intensity. In India, the first wave commenced in March 2020 with daily cases peaking in mid-September 2020 and finally declining in January 2021, whereas the second wave was observed from March 2021, peaking in April 2021 and showing a steady remission by August 2021 .
The COVID-19 pandemic has dramatically impacted all aspects of medicine, including the care of patients with immune-mediated kidney diseases and KTRs [18–33]. The use of immunosuppressive medications and the presence of multiple comorbidities puts KTRs at high risk of COVID-19 . Studies reporting on the outcomes of COVID-19 in KTRs have demonstrated increased morbidity and mortality in transplant patients [18–34]. Our recent publication also reported a 27% mortality rate in KTRs with COVID-19, which increases to 44% in hospitalized patients and 100% in patients requiring ventilation . Following the resurgence of COVID-19 in various countries, investigators have compared the epidemiology and disease outcomes between the first, second, and in some cases, third COVID-19 waves [21, 25, 33, 35–54]. However, data on the effects of the second wave of COVID-19 on KTR patients and its comparison with the first wave scenario is limited and reveals diverging results [21, 25, 45, 47, 53]. Currently, only one single-center study has been reported from India that has retrospectively investigated the impact of the first and second waves of COVID-19 on KTR; however, the study duration of the second wave was limited to 31st May 2021 . Here, we present a recent comparison between KTRs with SARS-CoV2 infections during India’s two COVID-19 pandemic waves after the decline in the trajectory of second wave cases across the country. We have documented the differences and similarities observed in clinical outcomes and hospital management of KTRs with SARS-CoV2 infections between the first wave (1st February 2020 to 31st January 2021) and the second wave (1st March 2021 till 31st August 2021), focussing primarily on mortality, associated risk factors, and the impact of treatment options on the outcome.
2. Materials and Methods
2.1. Study Design and Population
A retrospective study on the effect of COVID-19 on KTRs in India, between the study period 1st February 2020 and 31st August 2021, was conducted at a tertiary care hospital in New Delhi, India. 156 KTRs (154 living and 2 deceased donors) identified with SARS-CoV2 real-time reverse transcription-polymerase chain reaction (RT-PCR) confirmed infection and treated as either out-patient or hospitalized were included in the analysis. The two waves of COVID-19 in India, the first wave from 1st February 2020 to 31st January 2021 and the second wave from 1st March 2021 till 31st August 2021, were analyzed separately. The study evaluated the clinical symptoms, risk factors, laboratory profile, disease management, and mortality rate in KTRs.
The present study is a retrospective post-COVID-19 kidney transplant recipient pooled data analysis which excludes any compromise of personal or medical information of the subject. The study was approved by the designated institutional authority of the host institution, Indraprastha Apollo Hospital, New Delhi, to carry out data analysis and publication of manuscript/manuscripts.
2.2. Clinical Management of COVID-19 in KTRs
Treatment and follow-up of all patients were according to the hospital’s clinical protocol. COVID-19 infection was diagnosed as per the guidelines of the WHO [55, 56]. Patients with positive SARS-CoV-2 RT-PCR from nasopharyngeal or oropharyngeal swabs were considered laboratory-confirmed cases. The disease severity and assessment parameters were as per the Chinese Centre for Disease Control (China CDC) criteria . KTRs with positive SARS-CoV-2 RT-PCR were identified as mild or severe and managed accordingly by a designated COVID-19 treating team in consultation with the treating nephrologist, as described before .
Patients were evaluated as per unit protocol (Figure 1) and were followed up for a minimum of 90 days (except in the case of a fatality).
2.3. Data Collection
Data were collected retrospectively from the medical records of the hospital or patients’ follow-up submissions. Details of any asymptomatic home-isolated patients noncompliant with one or all prescribed drugs or investigation protocols were recorded and included in the study.
Collected data included demographics, transplantation history, comorbidities, concomitant medications, COVID-19-related symptoms, therapy during hospitalization, supportive measures needed during hospitalization, laboratory investigations (other than SARS-CoV-2 RT-PCR), and therapeutic outcomes (mortality and recovery). The onset symptom data were collected on first clinical reporting either by telephone for domiciliary patients or from triage notes for hospitalized patients. Based on the Body Mass Index criteria for the Asian population , the mean BMI was calculated.
The PCR test was repeated every 15 days until negative on two consecutive days.
The primary outcome of the study was to assess the mortality rate associated with COVID-19 in KTRs. The secondary outcomes included the spectrum of clinical presentation, immunosuppressive regimen, laboratory investigations, and pharmacological management of COVID-19 disease in the KTRs and their correlation with ICU admission, AKI, and acquired comorbidities (bacterial, fungal, or viral infections). AKI was defined using the Kidney Disease Improving Global Outcomes (KDIGO)-2012  criteria with baseline serum creatinine. Chest CT scan done in patients with poor oxygen saturation levels regardless of the ongoing treatment was quantified based on the CT severity score index .
2.5. Statistical Analysis
The data was analyzed as described before . Briefly, statistical analysis was performed on pooled data tabulated using Microsoft Excel, using the Statistical Package for Social Science (SPSS) version 16.0 (SPSS Inc., Chicago, IL). Continuous variables are represented as mean ± standard deviation (SD), and median and interquartile range (IQR) and qualitative variables are reported as numbers and percentages. For normally distributed variables, mean difference and 95% confidence intervals (CIs) were reported. For skewed variables, the median difference and its 95% CI were calculated using the Hodges Lehmann method; R-software version 3.6.1 was applied for determining the same.
Unpaired Student’s t-test was performed to compare the mean between survivor and nonsurvivor groups for normally distributed variables having homogeneity of variance. The Welch test was applied when the homogeneity of variance between the groups was violated. For inflammatory markers and some biomarkers, the nonparametric Mann–Whitney U test was applied due to skewed distribution. The Chi-square and Fisher’s exact test were applied to find the association between mortality and qualitative variables; the odds ratio and its 95% CI were reported.
To compare the discriminate power of biomarkers, Receiver Operating Characteristic (ROC) curve was applied. Multivariable logistic regression (MLR) to find the independent risk factors for nonsurvivors could not be performed due to the small number of cases, and some of the variables had zero count. MLR was performed to evaluate the independent effect of each biomarker on survivor status, adjusting age, hemoglobin (Hb), total leucocyte count (TLC), platelet count, blood urea, albumin level, fungal infection, chronic allograft dysfunction, and CAD/PVD. Bonferroni correction was applied, keeping into consideration the small sample size and multiple variable testing. The p value of less than 0.001 was considered statistically significant.
3.1. Demographics, Comorbidities, and Baseline Transplant Characteristics of KTRs
156 KTRs with positive SARS-CoV-2 RT-PCR were included in the study, out of which 72 KTRs were from the 1st wave of COVID-19 and 84 KTRs from the 2nd wave. Table 1 shows the demographics and comorbidities of the KTRs recorded at the time of presentation. The average age, weight, and height of the KTRs were 49.47 ± 13.1 years, 68.9 ± 14.99 kg, and 1.67 ± 0.09 meters, respectively. No significant difference was observed between the mean age, weight, height, median time interval from transplant to COVID-19, comorbidities, and baseline immunosuppressive regimens of the KTRs diagnosed during the 1st wave or the 2nd wave of the COVID-19 pandemic. Similarly, the mean BMI value was also comparable between the two waves. Notably, during both periods, the male to female ratio was skewed toward the male population; however, the difference between the gender distributions was more pronounced in the 2nd wave with male patients (Table 1).
3.2. Clinical Presentation
COVID-19 symptoms presented at the time of diagnosis are listed in Table 2. The major symptoms reported were fever (n = 140, 89.7%) and cough (n = 117, 75.0%); body ache (n = 77, 49.4%), sore throat (n = 53, 34.0%), and breathing difficulty (n = 48, 30.8%) were the other prominent complaints followed by distaste (n = 36, 23.1%), loose motion (n = 32, 20.5%), loss of smell (n = 22, 14.1%), running nose (n = 16, 10.3%), altered sensorium (n = 13, 8.3%), extreme weakness (n = 9, 5.8%), and incidental detection (n = 4, 5.6) (Table 2). Symptoms including sore throat (n = 35/84, 41.7% vs 18/72, 25%; p value .028), body aches (n = 50/84, 59.5% vs 27/72, 37.5%; p value .006), loss of smell (n = 18/84, 21.4% vs 4/72, 5.6%; p value .005), distaste (n = 26/84, 31% vs 8/72, 11.1%; p value .003), loose motions (n = 25/84, 29.8% vs 7/72, 9.7%; p value .002), and running nose (n = 15/84, 17.9% vs 1/72, 1.4%; p value .001) were reported more frequently during the second wave.
3.3. Clinical Outcome and Hospital Management
Details of clinical outcomes and treatment modalities of KTRs with COVID-19 are summarized in Table 3. Out of 156 KTRs included in the study, 78 (50%) were hospitalized and 78/156 patients with mild COVID-19 symptoms (50%) remained domiciliary. Less frequent hospitalization was observed during the second wave than during the first wave (n = 34/84, 40.5% vs n = 44/72, 61.1%, p value 0.01). However, patients requiring room air management, oxygen, ventilators, and ICU stay were comparable between the first and the second wave cohorts.
Immunosuppressive treatment regimens were modified in the majority of patients during both waves. In 128 (82.0%) patients, antinucleoside drugs were stopped, whereas in 5 (3.2%) patients, the dose was reduced, and in 19 (12.1%) patients, the treatment was continued as before; four patients (2.6%) were not taking antinucleoside drugs, to begin with. The antinucleoside drugs were stopped in more patients during the second wave than during the first wave (n = 75/84, 89.3% vs n = 53/72, 73.6%, p value 0.011).
CNIs remained unchanged in most patients (n = 116, 74.4%), and the administration was stopped in 22.5% (n = 36) patients; only 2 (1.3%) patients underwent a dose reduction of CNIs. The CNI drug treatment was altered in more patients during the first wave; however, the difference was not statistically significant.
Other specific treatments included the use of steroids (n = 156, 100%), ivermectin (n = 105, 67.3%), doxycycline (n = 102, 65.8%), remdesivir (n = 45, 38.8%), azithromycin, (n = 67, 42.9%), favipiravir (n = 53, 34%), fluvoxin (n = 44, 28.2%), convalescent plasma (n = 32, 20.5%), nintedanib (n = 8, 15.3%), tocilizumab (n = 10, 6.4%), HCQS (n = 9, 5.8%), antibiotics (n = 77, 49.4%), and antifungals (n = 33, 21.2%). Frequency of patients treated with steroids (100%), Azithromycin (n = 30/72, 41.7% vs 37/84, 44%, p value 0.765), remdesivir (n = 24/72, 33.3% vs 21/84, 25%, p value 0.252), HCQS (n = 7/72, 9.7% vs 2/84, 2.4%, p value 0.082), nintedanib (n = 1/72, 1.5% vs 7/84, 8.4%, p value 0.075), and antifungals (n = 18/72, 25% vs 15/84, 17.9%, p value 0.276) during both the waves were statistically comparable. During the second wave, fewer patients were treated with tocilizumab (n = 9/72, 12.5% vs 1/84, 1.2%, p value 0.004), convalescent plasma (n = 22/72, 30.6% vs 10/84, 11.9%, p value 0.004), and antibiotics (n = 42/72, 58.3% vs 35/84, 41.7%, p value 0.038) compared to administration of ivermectin (n = 40/72, 55.6% vs 65/84, 77.4%, p value 0.004) and doxycycline (n = 38/72, 53.5% vs 64/84, 76.2%, p value 0.003) although the observed differences were not found statistically significant. Notably, only patients from second wave were treated with antivirals favipiravir (n = 0/72, vs 53/84, 63.1%, p value <0.001) and fluvoxin (n = 0/72, vs 37/84, 52.4%, p value <0.001).
128/156 patients were also treated for Thromboprophylaxis by means of either antiplatelet treatment (n = 3, 1.9%), or low molecular weight heparin (LMWH) (n = 52, 33.3%), or oral anticoagulants (OAC) (n = 74, 47.4%). Significant differences were observed in Thromboprophylaxis treatment between both waves; LMWH treatment was preferred during the first wave (n = 31/72, 43% vs 21/84, 25%) compared to OAC (n = 20/72, 27.8% vs 54/84, 64.4%), which was used more during the second wave. Out of the 27 (17.3%) patients not treated for Thromboprophylaxis, the majority were in the first wave (n = 19/72, 26.4% vs n = 8/84, 9.5%).
AKI was observed in 65 (41.7%) patients, out of which 25 (16%) patients needed dialysis support. The frequency of patients with AKI and that of patients with AKI that needed dialysis were comparable between the two waves.
CT scan of the chest was performed on 67 patients that showed poor oxygen saturation levels despite ongoing treatment. CT findings were quantified based on the CT severity score index. Out of 67 patients, 23 (34.3%) had a CT score <10, 10 (14.9%) had a CT score 11–14, and 34 (50.7%) had a CT score ≥15. Patients that underwent a CT scan were higher during the second wave (n = 36 vs n = 31). However, the distribution of patients across the CT severity score index was comparable between the two waves.
3.4. Mortality in COVID-19-Infected KTRs and Comparison of Risk Factors for Mortality in the Two Waves
The overall patient mortality rate observed was 27.5% [95% CI: 20.7–35.2] (43/156). A detailed comparison of the demographics, immunosuppression regimen, clinical profile, treatment, clinical outcomes, and possible risk factors for mortality between survivors and nonsurvivors is summarized in Tables 4 and 5.
No significant difference was observed between survivors and nonsurvivors with regard to gender, blood group, BMI, and comorbidities (Table 4).
At the time of diagnosis, the frequencies of surviving and nonsurviving patients presenting COVID-19-related symptoms such as fever, cough, sore throat, body aches, loss of smell, distaste, loose motion, and extreme weakness were comparable. However, significantly higher percentage of nonsurvivors, compared to surviving patients, presented with symptoms of breathing difficulty (n = 24/43, 55.8% vs n = 24/113, 21.2%, p = 0.001) with low SpO2 (87.74 ± 7.82 vs 95.47 ± 3.36, p < 0.001) and altered sensorium (n = 13/43, 30.8% vs n = 0/113, 0%, p < 0.001) (Tables 4 and 5).
Significantly higher percentage of nonsurviving patients required a ventilator (n = 26/43, 60.5% vs n = 0/113, p < 0.001) and an ICU stay (n = 37/43, 86% vs n = 12/113, 10.6%, p < 0.001). Incidence of AKI (n = 36/43, 83.7% vs n = 29/113, 25.7%, p < 0.001) and requirement of dialysis support (n = 21/43, 48.8% vs n = 4/113, 3.5%, p < 0.001) were also significantly higher in nonsurvivors. Statistically significant risk factors that were observed in nonsurvivors included older age (p = 0.001), anemia (p < 0.001), low platelet count (p < 0.001), higher total leucocyte count (p < 0.001), kidney dysfunction as diagnosed by elevated serum creatinine (p < 0.001) and blood urea (p < 0.001), and higher levels of inflammatory markers, such as IL-6 level (p < 0.001), procalcitonin (p < 0.001), D-dimer (p < 0.001), CRP (p < 0.001), Ferritin (p < 0.001), LDH (p < 0.001), and CT score >15 (p < 0.001).
The impact of each biomarker on the survival status of KTRs as evaluated by multivariate logistic regression (MLR) analysis is summarized in Table 6. Only D-dimer and IL6 rise correlated with an increase in mortality; interestingly, every 5-unit increase in IL6 level increased the odds of mortality risk by 2.4% (Table 6).
Additional Receiver Operating Characteristic (ROC) curves were performed to determine the diagnostic values of inflammatory markers; all inflammatory biomarkers were found to be significant for diagnostic purposes (Figure 2). Furthermore, the area under the curve (AUC) was calculated to compare the different classifiers. For purposes of medical diagnosis, AUC values between 0.9 and 1, 0.8–0.9, 0.7–0.8, 0.6–0.7, and 0.5–0.6 were considered excellent, good, fair, poor, and failed, respectively . Based on this classification, IL-6 and CRP were the most acceptable diagnostic markers, followed by procalcitonin and D-dimer (Figure 2).
We have detailed a retrospective analysis comparing clinical outcomes and hospital management of 156 KTRs with confirmed COVID-19 between the first wave (1st February 2020 to 31st January 2021) and the second wave (1st March 2021 till 31st August 2021) of the pandemic. We identified 72 KTRs during the 1st wave and 84 KTRs during the 2nd wave. In contrast to a similar study by Kute et al. , our patient cohort included both domiciliary patients exhibiting milder symptoms of COVID-19 and hospitalized patients with moderate to severe COVID-19 symptoms. The demographics (age, weight, height, BMI, and blood group distribution) of the patients were comparable between the two waves. Interestingly, as reported previously , we did observe a male predominance in the patient cohort, the difference being more pronounced in the 2nd wave.
No significant differences in terms of comorbidities, oxygen/ventilator requirement, ICU stay, the incidence of AKI (with or without the need for dialysis), and chest CT severity score index were observed between the first and the second wave cohorts. Similar to a previous report , we observed more mild COVID-19 cases that did not require hospitalization during the second wave.
The baseline immunosuppressive regimens comprising steroids and CNI were comparable between the patients from both waves. The consensus regarding the susceptibility of KTRs in developing severe COVID-19 is that immunosuppressive treatment impairs the immune response [19, 24, 28]. Treatment guidelines documented in the literature suggest modification of immunosuppression for better COVID-19 management . In our study, immunosuppressive treatments were modified in the majority of patients during both waves in conjunction with other treatment options . The antinucleoside drugs were stopped in more patients during the second wave (89.3% vs 72%) whereas CNI drug treatment was altered for more patients during the first wave.
According to a recent study , the use of HCQS and tocilizumab decreased, and that of dexamethasone and remdesivir increased during the second wave. Although not statistically significant, we also observed a decrease in the use of tocilizumab, convalescent plasma, and antibiotics and an increase in treatment with ivermectin and doxycycline during the second wave. Prescriptions for antivirals favipiravir and fluvoxin were administered only during the second wave. The variation between the preferred treatment options in our study and the previous report  could be explained by the difference in the patient cohort; our study population included both domiciliary and hospitalized patients while the previous study  mainly focussed on hospitalized patients. The modification in treatment made during the second wave could be a result of recent studies demonstrating the efficacies of investigational treatments or drugs that were tried during the first wave, and the introduction of new therapies, thereby providing empirical data for deciding which treatment to follow .
During the second wave of COVID-19, India was challenged with the emergence of coronavirus disease-associated mucormycosis in both active and recovered patients, which contributed significantly to the increase in morbidity and mortality of COVID-19 [54, 64]. In the present study, during the 2nd wave, 3 KTRs with COVID-19 developed mucormycosis, while during the 1st wave, only one patient reported the same. Notably, even though the overall use of antifungal drugs was comparable in the two waves, documented culture positive fungal infections were higher among nonsurvivors (p = 0.015).
In our study, we observed an overall patient mortality rate of 27.5% (43/156), similar to results reported previously by us  as well as studies conducted across several countries [12, 19, 21–23, 28, 30, 45, 65–70]. Mortality was significantly associated with ventilator requirement, ICU admission, the incidence of AKI, and the requirement of dialysis support.
The distribution of survivors and nonsurvivors as to gender, BMI, comorbidities, and blood group was comparable; however, as reported previously , the frequency of nonsurvivors with blood group A was higher (14/66; 38.8%). Studies have shown that individuals with blood group A were susceptible to developing the disease with unfavorable outcomes [71–73], possibly due to a lack of anti-A antibodies that have been shown to provide protection against SARS-COV-2 viral infection . Additionally, the blood group A is linked to higher susceptibility of comorbidities that contribute to high mortality of severe COVID-19 [75, 76].
Notably, the nonsurvivors significantly presented breathing difficulty with low SpO2 and altered sensorium, supporting that KTRs with severe COVID-19 presentation were more susceptible to mortality.
Previous studies [12, 19, 21–23, 28, 30, 45, 65–70] have reported older age, anemia, low platelet count, higher total leucocyte count, kidney dysfunction as diagnosed by elevated serum creatinine, and blood urea, and CT score >15 and increased inflammatory markers (IL-6, procalcitonin, D-dimer, CRP, Ferritin, and LDH) as risk factors for mortality, which are also present in our study. Our analysis further shows that the increase in D-dimer and IL6 levels correlate with an increase in mortality and every 5-unit increase in IL6 levels increases mortality risk by 2.4%.
A comparison between the present work and similar studies from India as well as other countries [21, 25, 45, 47], detailing the similarities and differences between the work, is presented in Table 7. Despite the differences in geographical location and timeline of the epidemic waves, our study presents multiple similarities with other studies, especially with regard to milder symptoms and less hospitalization and COVID-19 treatment strategies during the second wave.
In summary, our study here provides a comprehensive comparison of the effect of COVID-19 on KTRs during the first and second waves of the disease outbreak in India, with relevance to mortality and risk factors associated with it. The inclusion of both hospitalized and home-isolated patients with milder symptoms in the total patient cohort allows us to provide a broader implication. In addition, the extended study period for the 2nd wave (until 31st August 2021) permitted us to include patients during the peak and remission of the second wave of the COVID-19 pandemic, thereby providing an inclusive patient cohort for analysis.
Our main limitation is that the study is a single-center study with a limited number of participants from a specific geographical location and therefore may not be sufficient to correctly represent the profile of the entire nation. Recent studies have also evaluated the effect of COVID-19 complications such as mucormycosis [54, 64], which adds more complexity to the treatment of KTRs. A multicentre study with a larger patient cohort, including a follow-up to study post-COVID-19 consequences, may not only validate our findings for the entire country but also promote awareness for better diagnosis and early management of post-COVID-19 complications.
In our patient cohort, combining both domiciliary and hospitalized individuals, we observed that the demographics and baseline transplant characteristics including the immunosuppressant regimen, comorbidities, requirement of ICU or ventilator, and incidence of AKI and radiological assessment by chest CT scan were similar between both waves. Interestingly, patients in the second wave reported less frequent hospitalization. Immunosuppressant treatments were modified during both waves as a strategy to build an immune response against the SARS-CoV-2 virus and treatment with antivirals favipiravir and fluvoxin was introduced in the second wave. Clinical symptoms such as breathing difficulty, low SpO2, and altered sensorium were presented at a higher rate in nonsurvivors. Common risk factors associated with mortality included older age, severe disease, ICU/ventilator requirements, acute kidney injury (AKI) needing dialysis, CT scan abnormalities, and higher levels of inflammatory markers particularly D-dimer and IL6 levels that correlated directly with mortality. Larger studies are needed to properly assess the outcomes of the second wave among KTRs and to address the potential use of IL6 and D-dimer as diagnostic biomarkers in identifying KTRs with severe COVID-19 disease.
All data used to support the findings of this study are available from the corresponding author upon request.
The study was approved by the Director Medical Services office of the host institution, Indraprastha Apollo Hospital, New Delhi, to compile and analyze data and publish manuscript/manuscripts.
Conflicts of Interest
There are no foreseen conflicts of interest associated with this manuscript.
We profusely acknowledge the support extended by Dr. A. K. Hooda Director, Medical Services, Indraprastha Apollo Hospital, for granting us permission to do the entire exercise; Dr. Rajeev Kumar Malhotra for statistical analysis; and Dr. Anupriya Khare Roy for editing the manuscript. The authors indeed acknowledge the assistance provided by Mr. Desmond Dyslva, Mr. Nirmal Maseeh, Mr. Santosh Singh, Mr. Mukul, Mr. Ravi Kumar, and Mr. Ashwani Gupta for the graphical inputs and data compilation.
M. Cascella, M. Rajnik, A. Aleem, S. C. Dulebohn, and R. Di Napoli, “Features, evaluation, and treatment of coronavirus (COVID-19),” StatPearls, StatPearls Publishing, reasure Island, FL, USA, 2021, http://www.ncbi.nlm.nih.gov/books/NBK554776/.View at: Google Scholar
Detail Question and Answers on COVID-19 for Public: MoHFW India, https://www.mohfw.gov.in/pdf/FAQ.pdf.
C. C. Lai, Y. H. Liu, C. Y. Wang et al., “Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): facts and myths,” Journal of Microbiology, Immunology, and Infection, vol. 53, no. 3, pp. 404–412, 2020.View at: Publisher Site | Google Scholar
D. S. Hui, E. I Azhar, T. A. Madani et al., “The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health-the latest 2019 novel coronavirus outbreak in Wuhan, China,” International Journal of Infectious Diseases, vol. 91, pp. 264–266, 2020.View at: Publisher Site | Google Scholar
Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV), 2021.
WHO Director-General’s opening remarks at the media briefing on COVID-19-11 March 2020, 2021, https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020.
Transmission|CDC, 2021, https://www.cdc.gov/coronavirus/2019-ncov/transmission/index.html.
Symptoms of COVID-19|CDC, 2021, https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.
N. Chen, M. Zhou, X. Dong et al., “Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study,” Lancet, vol. 395, no. 10223, pp. 507–513, 2020.View at: Publisher Site | Google Scholar
W. jie Guan, Z. Y. Ni, Y. Hu et al., “Clinical characteristics of coronavirus disease 2019 in China,” New England Journal of Medicine, vol. 382, no. 18, pp. 1708–1720, 2020.View at: Publisher Site | Google Scholar
N. Fadl, E. Ali, and T. Z. Salem, “COVID-19: risk factors associated with infectivity and severity,” Scandinavian Journal of Immunology, vol. 93, no. 6, Article ID e13039, 2021.View at: Publisher Site | Google Scholar
C. Kaeuffer, C. Le Hyaric, T. Fabacher et al., “Clinical characteristics and risk factors associated with severe COVID-19: prospective analysis of 1,045 hospitalised cases in North-Eastern France, March 2020,” Euro Surveillance, vol. 25, no. 48, 2020.View at: Publisher Site | Google Scholar
X. Liu, H. Zhou, Y. Zhou et al., “Risk factors associated with disease severity and length of hospital stay in COVID-19 patients,” Journal of Infection, vol. 81, no. 1, pp. e95–e97, 2020.View at: Publisher Site | Google Scholar
J. Tian, X. Yuan, J. Xiao et al., “Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study,” The Lancet Oncology, vol. 21, no. 7, pp. 893–903, 2020.View at: Publisher Site | Google Scholar
WHO Coronavirus (COVID-19) Dashboard, 2021, https://covid19.who.int.
MoHFW|Home, 2021, https://www.mohfw.gov.in/.
WHO coronavirus disease (COVID-19) dashboard with vaccination data, 2021. https://covid19.who.int.
O. Aubert, D. Yoo, D. Zielinski et al., “COVID-19 pandemic and worldwide organ transplantation: a population-based study,” The Lancet Public Health, vol. 6, no. 10, pp. e709–e719, 2021.View at: Publisher Site | Google Scholar
Y. Azzi, R. Bartash, J. Scalea, P. Loarte-Campos, and E. Akalin, “COVID-19 and solid organ transplantation: a review article,” Transplantation, vol. 105, no. 1, pp. 37–55, 2020.View at: Publisher Site | Google Scholar
D. Banerjee, J. Popoola, S. Shah, I. C. Ster, V. Quan, and M. Phanish, “COVID-19 infection in kidney transplant recipients,” Kidney International, vol. 97, no. 6, pp. 1076–1082, 2020.View at: Publisher Site | Google Scholar
F. I. Elec, S. D. Bolboacă, and A. Muntean, “Comparing the first and second wave of COVID-19 in kidney transplant recipients: an east-European perspective,” European Surgical Research, vol. 29, pp. 1–8, 2021.View at: Publisher Site | Google Scholar
P. K. Jha, D. K. Yadav, and V. Siddini, “A retrospective multi-center experience of renal transplants from India during COVID-19 pandemic,” Clinical Transplantation, vol. 35, 2021.View at: Publisher Site | Google Scholar
D. Kremer, T. T. Pieters, and M. C. Verhaar, “A systematic review and meta-analysis of COVID-19 in kidney transplant recipients: lessons to be learned,” American Journal of Transplantation, vol. 12, 2021.View at: Publisher Site | Google Scholar
V. B. Kute, A. K. Bhalla, S. Guleria et al., “Clinical profile and outcome of COVID-19 in 250 kidney transplant recipients: a multicenter cohort study from India,” Transplantation, vol. 105, no. 4, pp. 851–860, 2021.View at: Publisher Site | Google Scholar
V. B. Kute, H. S. Meshram, and V. V. Navadiya, “Consequences of the first and second COVID-19 wave on kidney transplant recipients at a large Indian transplant centre,” Nephrology, vol. 2, 2021.View at: Publisher Site | Google Scholar
M. A. Elhadedy, Y. Marie, and A. Halawa, “COVID-19 in renal transplant recipients: case series and a brief review of current evidence,” Nephron, vol. 145, no. 2, pp. 1–7, 2020.View at: Publisher Site | Google Scholar
M. Miarons, M. Larrosa-García, S. García-García et al., “COVID-19 in solid organ transplantation: a matched retrospective cohort study and evaluation of immunosuppression management,” Transplantation, vol. 105, no. 1, pp. 138–150, 2020.View at: Publisher Site | Google Scholar
M. Elias, D. Pievani, C. Randoux et al., “COVID-19 infection in kidney transplant recipients: disease incidence and clinical outcomes,” Journal of the American Society of Nephrology: Journal of the American Society of Nephrology, vol. 31, no. 10, pp. 2413–2423, 2020.View at: Publisher Site | Google Scholar
A. Kronbichler, P. Gauckler, M. Windpessl et al., “COVID-19: implications for immunosuppression in kidney disease and transplantation,” Nature Reviews Nephrology, vol. 16, no. 7, pp. 365–367, 2020.View at: Publisher Site | Google Scholar
L. R. Requião-Moura, T. V. D. Sandes-Freitas, L. A. Viana et al., “High mortality among kidney transplant recipients diagnosed with coronavirus disease 2019: results from the Brazilian multicenter cohort study,” PLoS One, vol. 16, no. 7, Article ID e0254822, 2021.View at: Publisher Site | Google Scholar
T. J. Kakkanattu, S. Sankarasubbaiyan, A. K. Yadav et al., “Outcome and determinants of outcome of COVID-19 infection among hemodialysis patients: findings from a national dialysis network program in India,” Kidney International Reports, vol. 6, no. 5, pp. 1429–1432, 2021.View at: Publisher Site | Google Scholar
N. Tau, D. Yahav, S. Schneider, B. Rozen-Zvi, M. Abu Sneineh, and R. Rahamimov, “Severe consequences of COVID-19 infection among vaccinated kidney transplant recipients,” American Journal of Transplantation, vol. 21, no. 8, pp. 2910–2912, 2021.View at: Publisher Site | Google Scholar
A. K. Yadav, S. Sankarasubbaiyan, B. G. M. Gowda, K. Shah, and V. Jha, “The high mortality and impact of vaccination on COVID-19 in hemodialysis population in India during the second wave,” Kidney International Reports, vol. 6, 2021.View at: Publisher Site | Google Scholar
S. Jasuja, G. Sagar, A. Bahl, and S. Verma, “COVID-19 infection clinical profile, management, outcome, and antibody response in kidney transplant recipients: a single centre experience,” International Journal of Nephrology, vol. 2021, Article ID e3129411, 2021.View at: Publisher Site | Google Scholar
G. Fan, Z. Yang, Q. Lin, S. Zhao, L. Yang, and D. He, “Decreased case fatality rate of COVID-19 in the second wave: a study in 53 countries or regions,” Transboundary and Emerging Diseases, vol. 68, no. 2, pp. 213–215, 2020.View at: Publisher Site | Google Scholar
V. K. Jain, K. P. Iyengar, and R. Vaishya, “Differences between first wave and second wave of COVID-19 in India,” Diabetes & Metabolic Syndrome, vol. 15, no. 3, pp. 1047-1048, 2021.View at: Publisher Site | Google Scholar
S. Saito, Y. Asai, N. Matsunaga et al., “First and second COVID-19 waves in Japan: a comparison of disease severity and characteristics,” Journal of Infection, vol. 82, no. 4, pp. 84–123, 2020.View at: Publisher Site | Google Scholar
I. Mollinedo-Gajate, F. Villar-Álvarez, M. L. Á. Zambrano-Chacón et al., “First and second waves of coronavirus disease 2019 in Madrid, Spain: clinical characteristics and hematological risk factors associated with critical/fatal illness,” Critical Care Explorations, vol. 3, no. 2, Article ID e0346, 2021.View at: Publisher Site | Google Scholar
S. Iftimie, A. F. López-Azcona, I. Vallverdú et al., “First and second waves of coronavirus disease-19: a comparative study in hospitalized patients in Reus, Spain,” PLoS One, vol. 16, no. 3, Article ID e0248029, 2021.View at: Publisher Site | Google Scholar
P. Asrani, M. S. Eapen, M. I. Hassan, and S. S. Sohal, “Implications of the second wave of COVID-19 in India,” The Lancet Respiratory Medicine, vol. 9, no. 9, pp. e93–e94, 2021.View at: Publisher Site | Google Scholar
C. O. V. I. D. Dutch, T. Coalition, F. H. J. Kaptein, and M. A. M. Stals, “Incidence of thrombotic complications and overall survival in hospitalized patients with COVID-19 in the second and first wave,” Thrombosis Research, vol. 199, pp. 143–148, 2021.View at: Publisher Site | Google Scholar
A. Borghesi, S. Golemi, N. Carapella, A. Zigliani, D. Farina, and R. Maroldi, “Lombardy, Northern Italy: COVID-19 second wave less severe and deadly than the first? A preliminary investigation,” Information Display, vol. 53, no. 5, pp. 370–375, 2021.View at: Publisher Site | Google Scholar
V. Soriano, P. Ganado-Pinilla, M. Sanchez-Santos et al., “Main differences between the first and second waves of COVID-19 in Madrid, Spain,” International Journal of Infectious Diseases, vol. 105, pp. 374–376, 2021.View at: Publisher Site | Google Scholar
C. Karagiannidis, W. Windisch, D. F. McAuley, T. Welte, and R. Busse, “Major differences in ICU admissions during the first and second COVID-19 wave in Germany,” Lancet Respiratory Medicine, vol. 9, no. 5, pp. e47–e48, 2021.View at: Publisher Site | Google Scholar
F. Villanego, A. Mazuecos, I. M. Pérez-Flores et al., “Predictors of severe COVID-19 in kidney transplant recipients in the different epidemic waves: analysis of the Spanish Registry,” American Journal of Transplantation, vol. 21, no. 7, pp. 2573–2582, 2021.View at: Publisher Site | Google Scholar
S. J. Salyer, J. Maeda, S. Sembuche et al., “The first and second waves of the COVID-19 pandemic in Africa: a cross-sectional study,” Lancet, vol. 397, no. 10281, pp. 1265–1275, 2021.View at: Publisher Site | Google Scholar
H. Georgery, A. Devresse, A. Scohy et al., “The second wave of COVID-19 disease in a kidney transplant recipient cohort: a single-center experience in Belgium,” Transplantation, vol. 105, no. 3, pp. e41–e42, 2021.View at: Publisher Site | Google Scholar
J. Kunno, B. Supawattanabodee, C. Sumanasrethakul, B. Wiriyasivaj, S. Kuratong, and C. Kaewchandee, “Comparison of different waves during the COVID-19 pandemic: retrospective descriptive study in Thailand,” Advances in Preventive Medicine, vol. 2021, Article ID e5807056, 2021.View at: Publisher Site | Google Scholar
H. Seong, H. J. Hyun, J. G. Yun et al., “Comparison of the second and third waves of the COVID-19 pandemic in South Korea: importance of early public health intervention,” International Journal of Infectious Diseases, vol. 104, pp. 742–745, 2021.View at: Publisher Site | Google Scholar
A. Capon, V. Sheppeard, N. Gonzalez et al., “Bondi and beyond. Lessons from three waves of COVID-19 from 2020,” Public Health Research & Practice, vol. 31, no. 3, Article ID 3132112, 2021.View at: Publisher Site | Google Scholar
S. Ryu, S. T. Ali, E. Noh, D. Kim, E. H. Y. Lau, and B. J. Cowling, “Transmission dynamics and control of two epidemic waves of SARS-CoV-2 in South Korea,” BMC Infectious Diseases, vol. 21, no. 1, p. 485, 2021.View at: Publisher Site | Google Scholar
P. Domingo, V. Pomar, I. Mur, I. Castellví, H. Corominas, and N. de Benito, “Not all COVID-19 pandemic waves are alike,” Clinical Microbiology and Infections, vol. 27, no. 7, pp. 1040.e7–1040.e10, 2021.View at: Publisher Site | Google Scholar
R. R. Tatapudi, V. R. Kopparti, A. Poosapati, S. Metta, A. R. Gongada, and B. Vedulla, “SARS-CoV-2 infection in kidney transplant recipients: a single-centre study of 20 cases from India,” International Journal of Nephrology, vol. 2021, Article ID 2243095, 7 pages, 2021.View at: Publisher Site | Google Scholar
A. Raut and N. T. Huy, “Rising incidence of mucormycosis in patients with COVID-19: another challenge for India amidst the second wave?” Lancet Respiratory Medicine, vol. 9, no. 8, p. e77, 2021.View at: Publisher Site | Google Scholar
Diagnostic testing for SARS-CoV-2, 2021, https://www.who.int/publications-detail-redirect/diagnostic-testing-for-sars-cov-2.
Recommendations for national SARS-CoV-2 testing strategies and diagnostic capacities, 2021, https://www.who.int/publications-detail-redirect/WHO-2019-nCoV-lab-testing-2021.1-eng.
Z. Wu and J. M. McGoogan, “Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese center for disease control and prevention,” JAMA, vol. 323, no. 13, pp. 1239–1242, 2020.View at: Publisher Site | Google Scholar
WHO Expert Consultation, “Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies,” Lancet, vol. 363, no. 9403, pp. 157–163, 2004.View at: Publisher Site | Google Scholar
A. Khwaja, “KDIGO clinical practice guidelines for acute kidney injury,” Nephron Clinical Practice, vol. 120, no. 4, pp. c179–184, 2012.View at: Publisher Site | Google Scholar
F. Pan, T. Ye, and P. Sun, “Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia,” Radiology, Article ID 200370, 2020.View at: Publisher Site | Google Scholar
J. N. Mandrekar, “Receiver operating characteristic curve in diagnostic test assessment,” Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer, vol. 5, no. 9, pp. 1315-1316, 2010.View at: Publisher Site | Google Scholar
V. Kute, S. Varugese, N. Prasad, S. Shroff, and S. K. Agarwal, “Renal transplant guidelines with reference to COVID-19 infection,” Indian Journal of Nephrology, vol. 30, no. 3, pp. 176–178, 2020.View at: Publisher Site | Google Scholar
A. Kataria, I. Yakubu, R. Winstead, M. Gowda, and G. Gupta, “COVID-19 in kidney transplantation: epidemiology, management considerations, and the impact on kidney transplant practice,” Transplant Direct, vol. 6, no. 8, Article ID e582, 2020.View at: Publisher Site | Google Scholar
S. B. Bansal, A. Rana, and M. Babras, “Risk factors and outcomes of COVID associated mucormycosis in kidney transplant recipients,” Transplant Infectious Disease, vol. 2, 2021.View at: Publisher Site | Google Scholar
V. Mahalingasivam, A. Craik, L. A. Tomlinson et al., “A systematic review of COVID-19 and kidney transplantation,” Kidney International Reports, vol. 6, no. 1, pp. 24–45, 2021.View at: Publisher Site | Google Scholar
S. A. Moosavi, A. Mashhadiagha, N. Motazedian, A. Hashemazar, A. H. Hoveidaei, and D. Bolignano, “COVID-19 clinical manifestations and treatment strategies among solid-organ recipients: a systematic review of cases,” Transplant Infectious Disease: An Official Journal of the Transplantation Society, vol. 22, no. 6, Article ID e13427, 2020.View at: Publisher Site | Google Scholar
M. Oltean, J. M. Søfteland, J. Bagge et al., “Covid-19 in kidney transplant recipients: a systematic review of the case series available three months into the pandemic,” Information Display, vol. 52, no. 11, pp. 830–837, 2020.View at: Publisher Site | Google Scholar
L. S. Nacif, L. Y. Zanini, D. R. Waisberg et al., “COVID-19 in solid organ transplantation patients: a systematic review,” Clinics, vol. 75, Article ID e1983, 2020.View at: Publisher Site | Google Scholar
S. Udomkarnjananun, S. J. Kerr, N. Townamchai et al., “Mortality risk factors of COVID-19 infection in kidney transplantation recipients: a systematic review and meta-analysis of cohorts and clinical registries,” Scientific Reports, vol. 11, no. 1, Article ID 20073, 2021.View at: Publisher Site | Google Scholar
P. Cravedi, S. S. Mothi, Y. Azzi et al., “COVID-19 and kidney transplantation: results from the TANGO international transplant consortium,” American Journal of Transplantation, vol. 20, no. 11, pp. 3140–3148, 2020.View at: Publisher Site | Google Scholar
N. Liu, T. Zhang, L. Ma et al., “The impact of ABO blood group on COVID-19 infection risk and mortality: a systematic review and meta-analysis,” Blood Reviews, vol. 48, Article ID 100785, 2021.View at: Publisher Site | Google Scholar
R. Mahmud, M. A. Rassel, F. B. Monayem et al., “Association of ABO blood groups with presentation and outcomes of confirmed SARS CoV-2 infection: a prospective study in the largest COVID-19 dedicated hospital in Bangladesh,” PLoS One, vol. 16, no. 4, Article ID e0249252, 2021.View at: Publisher Site | Google Scholar
B. B. Wu, D. Z. Gu, J. N. Yu, J. Yang, and W. Q. Shen, “Association between ABO blood groups and COVID-19 infection, severity and demise: a systematic review and meta-analysis,” Infection, Genetics and Evolution, vol. 84, Article ID 104485, 2020.View at: Publisher Site | Google Scholar
P. Guillon, M. Clément, V. Sébille et al., “Inhibition of the interaction between the SARS-CoV spike protein and its cellular receptor by anti-histo-blood group antibodies,” Glycobiology, vol. 18, no. 12, pp. 1085–1093, 2008.View at: Publisher Site | Google Scholar
F. Dentali, A. P. Sironi, W. Ageno et al., “Non-O blood type is the commonest genetic risk factor for VTE: results from a meta-analysis of the literature,” Seminars in Thrombosis and Hemostasis, vol. 38, no. 5, pp. 535–548, 2012.View at: Publisher Site | Google Scholar
S. Zhou and I. Welsby, “Is ABO blood group truly a risk factor for thrombosis and adverse outcomes?” World Journal of Cardiology, vol. 6, no. 9, pp. 985–992, 2014.View at: Publisher Site | Google Scholar