|Year : 2022 | Volume
| Issue : 1 | Page : 7-16
COVID-19 associated acute kidney injury in the second wave of pandemic in India: A single-center retrospective report
Subho Banerjee, Ruchir Dave, Hari Shankar Meshram, Sanshriti Chauhan, Vivek B Kute, Himanshu V Patel, Sudeep Desai, Priyash Tambi, Nauka Shah, Akash Shah
Department of Nephrology and Clinical Transplantation, Institute of Kidney Diseases and Research Centre, Dr. Hl Trivedi Institute of Transplantation Sciences, Ahmedabad, Gujarat, India
|Date of Submission||10-Dec-2021|
|Date of Decision||19-Dec-2021|
|Date of Acceptance||27-Apr-2022|
|Date of Web Publication||31-May-2022|
Hari Shankar Meshram
Department of Nephrology and Clinical Transplantation, Institute of Kidney Diseases and Research Centre, Dr. Hl Trivedi Institute of Transplantation Sciences, Ahmedabad, Gujarat
Source of Support: None, Conflict of Interest: None
Introduction: Acute kidney injury (AKI) in coronavirus disease (COVID-19) is understudied, especially after the initial pandemic wave and in South East Asian Region. Materials and Methods: This was a single-center retrospective cohort of 856 hospitalized COVID-19 cases between March 26, 2021, and June 7, 2021 in India to study the spectrum of AKI in COVID-19. The primary outcome was to analyze predictors of AKI. Other secondary outcome measured was mortality in AKI. Results: The incidence of AKI was 38.1%. The incidence of hemodialysis requirement was 3.5%. The proportion of AKI I, II, and III was 80.2%, 8.2%, and 11.6%, respectively. The mortality in AKI was statistically significantly higher than in non-AKI compared to AKI. Among the laboratory markers, the highest area under the curve (AUC) in the receiver operator curve was reached for red cell distribution width [AUC = 0.77 (0.73–0.81); P < 0.01]. The predictors for AKI calculated by multivariable logistic regression model in the cohort were obesity (hazard ratio (HR) = 3.2 (1.08–9.73); P = 0.04) and baseline European Cooperative Oncology Group (ECOG ≥ 3) (HR = 3.4 (1.77–6.69); P < 0.01). Similarly, the risk factors for developing AKI III included male sex (HR = 1.33 (1.05–1.68); P = 0.02) and ECOG ≥ 3 (HR = 1.5 [1.18–1.9]; P < 0.01). Conclusion: The incidence of AKI is high in hospitalized patients in the COVID-19 second wave. The mortality associated with AKI remains high. The comorbidity burden was not linked with AKI.
Keywords: Biomarkers, COVID-19 variants, hemodialysis, severe acute respiratory syndrome-CoV-2
|How to cite this article:|
Banerjee S, Dave R, Meshram HS, Chauhan S, Kute VB, Patel HV, Desai S, Tambi P, Shah N, Shah A. COVID-19 associated acute kidney injury in the second wave of pandemic in India: A single-center retrospective report. Saudi Crit Care J 2022;6:7-16
|How to cite this URL:|
Banerjee S, Dave R, Meshram HS, Chauhan S, Kute VB, Patel HV, Desai S, Tambi P, Shah N, Shah A. COVID-19 associated acute kidney injury in the second wave of pandemic in India: A single-center retrospective report. Saudi Crit Care J [serial online] 2022 [cited 2022 Jul 3];6:7-16. Available from: https://www.sccj-sa.org/text.asp?2022/6/1/7/346351
| Introduction|| |
Globally, as of November 2, 2021, there have been 246,951,274 confirmed cases of coronavirus disease (COVID-19), including 5,004,855 deaths, as per the WHO. India remains the second-worst affected nation and topped the COVID-19 tally during the second wave (May-June 2021). From the beginning of the pandemic, acute kidney injury (AKI) has adversely impacted the prognosis and outcome of severe acute respiratory syndrome (SARS-CoV-2) infection in published literature. The pathophysiology of AKI in COVID-19 has been postulated to be multifactorial and is incompletely elucidated. There exists a knowledge gap in the impact of AKI in different geographic regions and at different pandemic waves, as the existing data corresponds to the initial phase of the pandemic, mostly from the western world. Moreover, there is limited data from Indian ethnicity with this complex and serious issue. With the emergence of different strains like Delta variant B.1.617.2 COVID-19 which is considered highly contagious, the burden of AKI is expected to expand. Similarly, with better preparedness and evolvement of better management strategies in the second wave, the outcome of AKI is expected to improve. With this rationale, we conducted a retrospective analysis studying the pattern and outcomes of AKI in COVID-19 cases during the second wave of pandemics (Late March 2021 to early June 2021) in India.
| Materials and Methods|| |
The ethical approval for the study was obtained from the institutional ethical committee (ECRJ143/Inst/GJ/20131RR-19) as a part of the SARS-CoV-2 project with the following registration number: Institute of Kidney Diseases and Research Centre, Dr. HL Trivedi Institute of Transplantation Sciences (IKDRC-ITS) EC/App/28May21106. The informed consent for publication of the report was not required as the data is retrospectively collected and anonymized. The reporting of the study is done as specified by the STROBE checklist for observational studies.
Design, settings, and participants
This was a retrospective observational study conducted at the Department of Nephrology and Clinical Transplantation, IKDRC-ITS, Ahmedabad, India. The center serves as an organ transplant center that predominantly performs renal transplantation other than liver transplants, but because of the COVID-19 surge of the second wave the center was transiently converted to a dedicated COVID-19 ward. All probable and confirmed COVID-19 cases admitted to our dedicated COVID-19 center were eligible for the study. The cases were mostly referred from the nearby dedicated COVID-19 centers given the unavailability of COVID-19 beds. The time frame for the first COVID-19 case to the last case included in the study was 26 March 2021 to7 June 2021[Figure 1]. Of a total of 1052 cases, 856 were included in the analysis. The data of organ transplant recipients (n = 107) were excluded from the analysis, which the authors have previously reported.,, Age < 18 years was not an exclusion criterion. The data of hemodialysis patients (n = 85) are also excluded from the analysis.
During the study, the management of COVID-19 was done as per the national guidelines for COVID-19 and as per the international consensus. We compared the data COVID-19 cases with AKI versus non-AKI. The baseline creatinine was adapted from the last value within 7–365 days of admission. In case, the baseline data for creatinine is not retrieved then, it was calculated using the MDRD formula, as suggested by KDIGO., AKI was managed as per the consensus report of the 25th Acute Disease Quality Initiative Workgroup. No investigational therapies were utilized in managing AKI. As a modality of RRT, only hemodialysis sessions through femoral/internal jugular temporary catheter were used. No acute peritoneal dialysis or continuous replacement renal therapy was performed due to logistic issues.
Baseline data were retrieved through electronic records which included age vitals on admission, history of smoking, comorbid conditions, Charlson's comorbidity index, and functional status on admission based on the European Cooperative Oncology Group (ECOG) score. The laboratory parameters were also collected systematically through the institutional electronic software. This included hemoglobin, total leukocyte count, lymphocyte percentage (L); absolute lymphocyte count (ALC); red cell distribution width (RDW), platelet count (PLT), serum albumin, lactate dehydrogenase (LDH), ferritin, D-dimer, procalcitonin, highly sensitive C-reactive protein. The values are reported as standard units with reference values specified by the institutional laboratory.
The primary outcome was to measure the risk factors for the development of AKI. The secondary outcome was the pattern of AKI and the biomarkers for prediction of AKI, along with mortality differences between AKI and non-AKI.
Definitions in the context of the study
The COVID-19 cases were defined as probable and confirmed as per the World health organization. Severe COVID-19 was defined as a case requiring oxygen support of high flow oxygen, nonrebreather mask, or higher. The definition was AKI was adapted from KDIGO, which states AKI was an increase in serum creatinine value by >50% within 7 days from the baseline value, OR an increase in serum creatinine by ≥0.3 mg/dL from baseline within 48 h, OR Oliguria. AKI was stratified into 3 stages as per the KDIGO, where stage 1 corresponds to an increase by 1.5–1.9 times or an increase in serum creatinine by ≥0.3 mg/dL stage 2 implies an increase by 2–2.9 times the baseline value and stage 3 means requirement of RRT irrespective of serum creatinine, or a serum creatinine increase by 4 mg/dl or more than 3 times the baseline serum creatinine level. The urine output criteria were not utilized as the data was not captured data electronically and also there were unreliable records in the case files due to logistic issues faced in the pandemic. The estimated GFR was calculated using the CKD-EPI equation. ECOG score is a validated tool developed for stratifying and assessing the patients in the initial encounter. Charlson's comorbidity index is a validated tool for the measurement of comorbidity burden with higher scores entailing higher chances of mortality.
All statistical rigor was performed through IBM stats version 25 (IBM Corp, Armonk, NY, USA). In all the results of the study, a two-tailed P < 0.05 was considered statistically significant. All categorical values are described as numbers with their respective percentages. Depending on the sample size of the variables a Chi-square test, Chi-square test with Yates's correction, or fisher's exact test was performed for the categorical variables. The data distribution for continuous variables were primarily screened for normality distribution through the Kolmogorov Smirnov test, Shapiro–Wilk test, histogram, and Q-Q plots. The continuous data were thus expressed as mean (standard deviation [SD]) or median (interquartile range) as per the results of the test. For the continuous data, an independent sample t-test or Mann–Whitney test was done based on normality. For the prediction of risk factors in the development of AKI, a Cox regression analysis was done with AKI as a dependent variable. The results were interpolated as HR with their respective 95% upper and lower limit of CI. A Kaplan Meier plot with the Hall Wellner band was made with statistical analysis by log-rank (Mantel-cox) for comparison of AKI versus non-AKI. Receiver operator curve (ROC) curves were plotted for the laboratory parameters along with detailed laboratory violin plots for mortality. Kaplan plot and Violin plots were generated through R-software studies.
| Results|| |
A total of 862 hospitalized COVID-19 cases were included for the analysis. Three twenty-nine (38.1%) of 862 COVID-19 cases' clinical course was complicated by AKI. KGIGO stage composition included AKI I (264, 80.2%), AKI II (27, 8.2%), and AKI III (38, 11.6%).
Demographic and baseline characteristics
[Table 1] shows the baseline features of the There was no statistically significant difference in the mean (SD) ages of the AKI versus non-AKI cohort (55.2 [16.3] vs. 53.3 [15.8] years; P = 0.13). However, age group 70-80 had higher numbers of AKI cases (P < 0.01) In further analysis, the AKI stage II cohort (60 vs. 53 years; P = 0.02) were older compared to non-AKI, while there was no statistically significant difference between AKI I versus non-AKI (54 vs. 54.4 years; P = 0.73). AKI III cohort was older to non-AKI (57 vs. 54 years; P = 0.24), but the difference did not achieve a statistically significant level. In gender analysis, more of the male sex developed AKI (68.1% vs. 58.3%; P < 0.01). There were no differences in terms of co-morbidities for the development of AKI. The majority of the cases with ECOG ≥3 (32.2% vs. 17.8%) landed into AKI when compared to lower ECOG scores. In the study, ten cases had a history of previous COVID-19 infection, but there was no difference in terms of AKI.
Laboratory characteristics of the cohort
[Figure 1], [Figure 2], [Figure 3] describes the ROC curves for the admission laboratory values studied in the analysis. Among the laboratory parameter which are positive markers for AKI, the highest area under the curve (AUC) in the ROC curve was reached for RDW (AUC = 0.77 (0.73-0.81); P < 0.01). The other significant laboratory values in decreasing order of their AUC involves blood urea (AUC = 0.68 [0.64–0.73]; P < 0.01), hemoglobin (AUC = 0.63 [0.57–0.69]; P < 0.01), LDH (AUC = 0.63 [0.58–0.68]; P < 0.01), IL-6 (AUC = 0.63 [0.57–0.68]; P < 0.01) and D-dimer (AUC = 0.62 [0.57–0.67]; P < 0.01). Among the laboratory values which are negative markers PLTs (AUC = 0.34 [0.29–0.40]; P < 0.01) had lowest AUC followed by Serum Sodium (AUC = 0.44 [0.39–0.49]; P = 0.03). Lymphopenia did not reached significance in ROC analysis.
Other classical laboratory predictors for AKI such as NLR, PLT, and ALC were not found to be statistically significant. [Table 3] shows the detailed laboratory features of the study, stratified with KDIGO AKI stages. For AKI stage 3, RDW, D-dimer, and NLR were amongst the most significant predictors. [Figure 1] and [Figure 2] describes the laboratory values in AKI and non-AKI groups with respect to mortality.
|Table 3: Cox regression analysis for assessing risk factors for acute kidney injury and acute kidney injury III|
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Risk factors for the development of acute kidney injury
A total of 30 cases died in the study. In Kaplan Meier curves [Figure 4], there was a significant difference in terms of mortality concerning AKI.
|Figure 4: Kaplan Meier analysis for mortality in AKI compared with non-AKI|
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In a multivariable cox regression analysis, the risk factors for development of AKI included obesity (HR = 3.2 [1.08–9.73]; P = 0.04) and baseline ECOG ≥3 (HR = 3.4 [1.77–6.69]; P < 0.01). Similarly, the risk factors for developing AKI III included male sex (HR = 1.33 [1.05–1.68]; P = 0.02) and ECOG ≥3 (HR = 1.5 [1.18–1.9]; P < 0.01). Increasing age, co-morbidities like DM, HTN were not associated with subsequent risk of AKI in the study. The co-existing history of drugs like ACEi/ARB or NSAID was not found to be a predictive risk factor for AKI [Table 2].
|Table 2: Laboratory parameters in different stages of acute kidney injury|
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| Discussion|| |
The complication of AKI in the course of any infection prolongs the agony, and COVID-19 is not an exception. Numerous studies have pointed that after acute respiratory distress syndrome, AKI was the next organ dysfunction that was strongly associated with morbidity and mortality in COVID-19. Compared to other causes of admission, COVID-19 itself is associated with a higher risk of AKI, as documented by a US multicenter study (n = 22,122) by Moledina et al. recently. In another report, the incidence and mortality with AKI are higher in COVID-19 than in Influenza. On a higher note, this is also quite evident from the literature search that there was a clear discrepancy in the available data with regards to various geographic regions and study duration with no high-level data from southeast Asian regions. In addition, most of the available data are from the initial pandemic waves where different strains have not emerged, and there was a lack of any management protocol. In our center, during the peak of the pandemic of the second wave (March-June 2021), the COVID-19 cases were linked with the Delta variant The clinical profile and outcomes of the delta variant are different. Hereabout, we would analyze and compare the data from the second wave of the pandemic in India, and the pattern of AKI involved.
Varying incidence of acute kidney injury across different regions
The incidence of AKI in our report was 38.1%, which is significantly higher compared to previous published high-level data. In a meta-analysis by Chan et al. the global prevalence of AKI and CRRT was 20.4% and 2.9% respectively and reported the delay in admission for AKI was linked with mortality. The meta-analysis (20 studies; n = 14,415) by Menon et al. found 11% incidence of AKI. In a meta-analysis (54 studies; n = 30,657) of Silver et al., the pooled prevalence of AKI and RRT was 28% and 9%. In a meta-analysis (n = 16,199) by Xu et al. incidence was around 20% in hospitalized cohort with a change in incidence as per ethnicity. Black race was associated with AKI and mortality in a US cohort. In a meta-analysis (22 studies; n = 17391) by Kunutsor et al. the incidence of AKI was 11% with 6.8% RRT and higher incidence in US population. In a meta-analysis (79 studies; n = 49 792) by Lin et al. reported that AKI was less prevalent in Chinese ethnicity, while higher in US patients. Mortality was higher in European patients (20 studies; n = 13 137). AKI was associated with heterogeneity in various regions of the world in another meta-analysis (20 studies; n = 13 137) by Robbins-Juarez et al. An Indian report (n = 718) by Sampathkumar et al. reported only 7% developing AKI during the first wave of the pandemic.
Risk factors for acute kidney injury
Our data compare well with data from China, France and the UK with all these studies showing a greater preponderance of COVID-19 associated AKI in males with the average age group and demographic distribution being similar. In general, in our report co-existence of comorbidities like hypertension or diabetes was not associated with AKI, unlike current literature. In a meta-analysis (20 studies; n = 10, 180) by Yang et al. the odds of AKI in critical cases were 30 times higher than in noncritical cases, which was similar to ours. In a meta-analysis (24 studies; n = 4963) by Yang et al. the incidence was higher in severe COVID-19 cases with a significant number of urinary dysfunctions. In a meta-analysis (142 studies; n = 49,048) by Fu et al. the incidence of AKI was 28.6% and older age, male sex, and co-morbidities like hypertension and diabetes correlated with a higher risk of AKI, which is contrary to ours. A Chinese meta-analysis by Zang et al. showed abnormal laboratory biomarkers to be associated with a high risk of AKI which is contrasting to our report. In a meta-analysis (32 studies; n = 25,566) by Fabrizi et al. the pooled incidence of AKI was higher in hypertensive patients unlike ours where hypertension had no association.
The meta-analysis (15; n = 3615) by Lim et al., had similar reports to us. In a meta-analysis by Hansrivijit et al. AKI was associated with co-morbidities with 8.3% overall incidence and 19.9% in critically ill patients, unlike ours in which no co-morbidity association was reported. Obesity was recently reported to be associated with a higher risk of AKI in a study by Martín-Del-Campo et al. A recent meta-analysis (41 studies; n = 21060) by Li et al. studied co-morbidity association with AKI, and found an increased risk of AKI with many co-morbidities, contrary to us. The largest Indian report (n = 2650) showed 7.2% AKI. The report was done during the first wave, and the presence of co-morbid conditions was linked with AKI, contrary to our report. The cases with severe COVID-19 and cases with mortality had a higher incidence of AKI in our report, simulating the bulk of the studies.,,,
Follow-up of acute kidney injury
We were not able to do a follow-up of the general patients, as ours is a dedicated transplant center; hence, follow-up of general patients was not possible. However, in the follow-up of AKI in kidney transplant recipients from our center, there was almost complete AKI recovery. In a report by Chin et al. from the US, of 3993 hospitalized cases, 46% had AKI had high proportion had residual kidney damage at discharge, mandating the follow-up of discharged patients. There were reports of COVID-19 associated thrombotic microangiopathy, collapsing glomerulopathy, and other renal findings., At the advent of the pandemic, initial reports concerned the global community for increased risk of COVID-19 associated glomerulopathy. In post mortem analysis, COVID-19 kidney biopsies have been reported with varied abnormalities. The problem statement is recently further highlighted by a US cohort (n = 1,726,683) reported by Bow et al. where a large proportion of discharged patients had persistently deranged renal functions. Only with follow-up studies, we would be able to measure the true burden of acute kidney disease/chronic kidney disease in post-COVID-19 survivors. Till then a conscious and vigilant approach is required to detect any early decline in renal functions, following discharge of COVID-19 patients.
Need of biomarkers for predicting acute kidney injury
There has been a need to authenticate biomarkers for predicting the risk and progression of AKI. Inflammatory markers have been associated with AKI in COVID-19 and are potential targets as therapeutics. Urinary biomarkers like neutrophil gelatinase-associated lipocalin, monocyte chemoattractant protein, and kidney injury molecule-1 and higher epidermal growth factors have shown promising results. Hypophosphatemia is recently been shown to have an independent risk factor for AKI. In a recent report, quantification of SARA-CoV-2 viral load in urine has prognostic implications in course of AKI. In consideration of the cost-effectiveness of AKI biomarkers, a single parameter like RDW, which has been a routine part of complete blood count is a handy tool that can be explored and studied further. RDW stood out as one of the most sensitive and specific tests for predicting AKI in our cohort.
There could be information bias, as the data collected on comorbidities are obtained by different levels of health care professionals during the pandemic, and during isolation of cases, there could be some missing details of history. Our report corresponds to the mortality in hospitalized patients and it does not represent the general population data of 1% mortality, as there is referral bias also for the center as the center is a tertiary care center in the pandemic. There was not a fixed protocol of medications for the patients, as so analysis of those concerning AKI was tricky to comment upon.
| Conclusion|| |
Evolving strains of COVID-19 will have clinical implications, and our report proves that the incidence of AKI is higher in the second wave of the pandemic in India. Furthermore, the co-morbid burden was less, still, AKI cases were more. We did not find a significant link with the classically described biomarkers like lymphopenia, or D-dimers for the prediction of AKI. All these findings, call for further data collection, strategy implementation, and continued research in the field of AKI.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]