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Table of Contents
Year : 2023  |  Volume : 22  |  Issue : 3  |  Page : 300-308  

Impact of sepsis in patients admitted with covid-19 infection in a tertiary care center in Delhi. A retrospective cross-sectional study

1 Department of Medicine, University College of Medical Sciences, New Delhi, India
2 Department of Medicine, Hindu Rao Hospital, New Delhi, India

Date of Submission24-Mar-2022
Date of Decision23-Jul-2022
Date of Acceptance21-Oct-2022
Date of Web Publication4-Jul-2023

Correspondence Address:
Smita Nath
Department of Medicine, University College of Medical Sciences, New Delhi - 110 095
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/aam.aam_51_22

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Background: COVID-19 pandemic has emerged as one of the worst humanitarian crises in human history. Viral sepsis is implicated as a major source of morbidity and mortality in COVID-19 infection. The study provides an insight into impact of COVID -19 associated sepsis on the patient's clinical course and mortality. Materials and Methods: The study was conducted on 112 participants admitted with symptomatic COVID -19 infection in a COVID -19 designated center in New Delhi, India between July and October 2020. Result: 41.1% (n=46) of the participants had critical disease which includes sepsis. Out of 46 Critical patients 19 (41.3%) had sepsis, 21(45.7%) had septic shock and 6 (18.8%) had Sepsis with ARDS. Sepsis and septic shock at time of presentation was associated with higher mortality. Conclusion: Severe and critical illness was marked by advance age, comorbidities like Diabetes mellitus, high total leucocyte count and deranged renal and hepatic function in the study. Thus COVID-19 induced sepsis is an important determinant of disease severity precipitating multiorgan dysfunction and adverse outcome in patients.
Contexte: La pandémie de COVID-19 est devenue l'une des pires crises humanitaires de l'histoire de l'humanité. La septicémie virale est impliquée comme une source majeure de morbidité et de mortalité dans l'infection au COVID-19. L'étude donne un aperçu de l'impact de la septicémie associée au COVID -19 sur l'évolution clinique et la mortalité du patient. Matériels et méthodes: L'étude a été menée sur 112 participants admis avec une infection COVID -19 symptomatique dans un centre désigné COVID -19 à New Delhi, en Inde, entre juillet et octobre 2020. Résultat: 41,1 % (n = 46) des participants avaient un état critique maladie qui comprend la septicémie. Sur 46 patients critiques, 19 (41,3 %) avaient une septicémie, 21 (45,7 %) avaient un choc septique et 6 (18,8 %) avaient une septicémie avec SDRA. La septicémie et le choc septique au moment de la présentation étaient associés à une mortalité plus élevée. Conclusion: La maladie grave et critique était marquée par un âge avancé, des comorbidités comme le diabète sucré, un nombre total élevé de leucocytes et une fonction rénale et hépatique dérangée dans l'étude. Ainsi, la septicémie induite par le COVID-19 est un déterminant important de la gravité de la maladie, précipitant un dysfonctionnement multiorganique et des résultats indésirables chez les patients.
Mots-clés: COVID-19, réponse immunitaire, maladie respiratoire aiguë sévère coronavirus-2, septicémie virale

Keywords: COVID-19, immune response, severe acute respiratory illness coronavirus-2, viral sepsis

How to cite this article:
Nath S, Sharma H, Chilumula S, Rajkumar P, Dutta S, Jain S. Impact of sepsis in patients admitted with covid-19 infection in a tertiary care center in Delhi. A retrospective cross-sectional study. Ann Afr Med 2023;22:300-8

How to cite this URL:
Nath S, Sharma H, Chilumula S, Rajkumar P, Dutta S, Jain S. Impact of sepsis in patients admitted with covid-19 infection in a tertiary care center in Delhi. A retrospective cross-sectional study. Ann Afr Med [serial online] 2023 [cited 2023 Sep 28];22:300-8. Available from:

   Introduction Top

The cataclysm of the COVID-19 pandemic has plunged the world in an unprecedented crisis and has left human civilization scrambling to mitigate its devastating fallout. As of September 13th, 2021, the WHO dashboard reported 224,511,226 confirmed cases of COVID-19 globally including 4,627,540 deaths.[1] As the human race stares down, this devastating epoch in incredulity, factors contributing to morbidity and mortality have become the focus of health-care delivery system. The clinical trajectory of symptomatic infection in COVID-19 as defined by the WHO interim guidance report ranges from mild disease to critical illness characterized by acute respiratory distress syndrome (ARDS), sepsis, and septic shock.[2] Sepsis, septic shock, and ARDS are the three defining features of critical illness in COVID-19 infection. Critical illnesses including sepsis and septic shock occur in up to 40% of patients admitted for symptomatic COVID-19 and are major contributors to mortality in COVID-19 patients.[3] According to current sepsis-3 guidelines, sepsis is considered a life-threatening organ dysfunction due to the dysregulated host response to an infection.[4] In addition to lung disease and respiratory failure, the presence of organ dysfunction in the form of acute kidney injury (AKI), acute liver failure, thrombocytopenia, and lymphopenia in patients of COVID-19 fulfills the criteria of sepsis as established by current sepsis-3 definition.[4] Studies show that even after discharge up to 40% of patients had to be readmitted within 90 days of initial diagnosis of sepsis.[5] An extensive review of available literature clearly establishes the role of viral sepsis in determining the outcome of COVID-19 infection.

India has been battling the COVID-19 pandemic since the first confirmed case was reported in January 2020. Between March and June 2021 India witnessed a devastating second wave of COVID-19 surge leading to the crumbling of a strained health-care delivery system. The singular tragic second wave has brought the total number of confirmed cases to 29,359,115 including 367,081 deaths by 2nd week of June.[1] The majority of symptomatic COVID-19 infections result in mild disease, but 5% of patients suffer from severe lung injury and sepsis resulting in hospitalization and a case fatality rate of 1.5.[6] Conventionally, sepsis is attributed to bacterial infection, however, viruses are also a significant etiological agent of sepsis. Thus, critical illness in COVID-19 can be attributed to viral sepsis.[6] However, there is a dearth of data on the prevalence and impact of sepsis on COVID-19 patients from India. Hence, our study was undertaken with the primary objective to determine the prevalence of sepsis in patients with COVID-19 infection. Our secondary objective was to study the impact of sepsis on the patient's clinical course and outcome. A study of risk factors associated with sepsis in COVID-19 was also the secondary objective of our study. Our study was conducted on non intensive care unit (ICU) patients and given the scarcity of ICU beds at peak of the pandemic, the data can be used to augment treatment and patient monitoring in non-ICU settings.

   Methodology Top

Study design

We conducted a single-center cross-sectional retrospective study in a COVID-19-designated hospital in Delhi. Data from case records of a patient admitted with confirmed diagnosis of COVID-19-related illness between the months of July and September 2020 were included in the study.

Study participants

Patients were diagnosed with COVID-19, based on WHO interim guidelines dated May 27, 2020. COVID-19 was confirmed using reverse transcription–polymerase chain reaction analysis conducted on nasal and oropharyngeal swabs. Admitted patients were then classified as mild to critical disease-based WHO interim guidance report.[2] Patients with positive bacterial culture results and those on therapeutic steroid therapy were excluded from the study. Incomplete case records were also excluded from the study.

Data collection

Medical records were accessed for the collection of data. The phenotype of patients was established based on clinical profiles, laboratory data, nursing records, and radiological data on the day of admission. The date of onset was estimated from the day when the patient started experiencing symptoms. Laboratory data, vital signs, and clinical profiles were analyzed on the day of admission. Clinical outcome was defined as discharge and in-hospital death.


The diagnosis of sepsis was based on the third international consensus definition for sepsis and septic shock (Sepsis 3.0) criteria, which defined sepsis as a sequential organ failure assessment (SOFA) score ≥2 plus documented or suspected infection.[3] ARDS was diagnosed according to the Berlin criteria. The presence of AKI will be confirmed according to the kidney disease: improving global outcomes definition.

Statistical analysis

In our study, we analyzed data from patients' records during the provision of standard health-care services retrospectively. Hence, no sample size calculation was offered. Using nonprobability convivence sampling method, a total of 112 case records were analyzed.

The baseline characteristics of all enrolled patients in the sepsis and nonsepsis groups were summarized and comparison was drawn with respect to variables of age, sex, comorbidities, clinical presentation, and laboratory parameters severity of the disease. The outcome was determined as death or discharge. Differences between various groups were studied Student's t-test, the Chi-square test, Fisher's exact test, Shapiro–Wilk test, and Wilcoxon Mann–Whitney U test as appropriate. Continuous variables will be presented as the mean (standard deviation [SD]) and standard error of the mean or median [interquartile range (IQR)], whereas categorical or ranked data will be reported as counts and proportions. Two-tailed P < 0.05 was regarded as statistically significant. To explore the risk factors associated with sepsis and subsequent outcome selected variables along with age and gender were included in multivariate logistic regression. The odds ratio and 95% confidence interval were plotted accordingly.

The data were analyzed using Social sciences SPSS software version 23 (SPSS Inc., Chicago, IL).

Ethical consideration

The study was approved by the institutional ethical committee vide registration number-IEC/NDMC/2020/62 dated 31/12/2020. The study was a retrospective analysis with no intervention and without using unique patient identifiers.

   Results Top

This single-center retrospective study was conducted on 112 participants admitted with symptomatic COVID-19 infection in a COVID-19 designated center in New Delhi, India, between July and October 2020. The study was conducted by analyzing case records of patients admitted in non-ICU medical wards.

The mean age (years) of participating individuals was 52.46 ± 16.50. About 35.7% of the participants were above the age of 60 Years. About 58.0% of the participants were male and 42.0% of the participants were female. About 15.2% (n = 17) of the participants had a mild severe acute respiratory infection (SARI) but were admitted due to associated comorbidities. About 25.0% (n = 28) of the participants had moderate SARI. About 8.8% (n = 21) of the participants had severe SARI. About 41.1% (n = 46) of the participants had critical disease. Out of 46 critical patients, 19 (41.3%) had sepsis, 21 (45.7%) had septic shock, and 6 (18.8%) had sepsis with ARDS. We had 112 participants in our study, out of which 83 (74.1%) were discharged and 29 (25.9%) died during hospitalization [Table 1].
Table 1: Summary of outcomes

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Seventy (62.5%) patients had underlying comorbidities at the time of admission. Diabetes mellitus was the most frequently occurring comorbidity among the study participants at 62.5% (n = 70), followed by HTN, which was associated with 25.9% of patients (n = 29). About 3.6% (n = 4) of patients had a previous history of tuberculosis. History of bronchial asthma and chronic obstructive pulmonary disease (COPD) was reported by 7.1% (n = 8) of participants. About 8% (n = 9) and 6.2% (n = 7) of participants had coronary artery disease and chronic kidney disease as associated comorbidities, respectively, were as 3.6% (n = 4) and 6.2% (n = 7) were receiving treatment for hypothyroidism and anemia.

The laboratory, clinical, and radiological parameters were assessed at the time of admission [Table 2]. The mean (SD) of total leukocyte count (TLC) (x103/mm3) was 12.14 (6.45). The median (IQR) of TLC (x103/mm3) was 10.20 (7.22–17.22). The TLC (x103/mm3) ranged from 2.2 to 32.1. The mean (SD) of neutrophils (%) was 80.03 (12.23). The median (IQR) of neutrophils (%) was 82.50 (7390). The neutrophils (%) ranged from 32 to 96. A similar observation was made while studying the lymphocyte percentage in the differential count. The mean (SD) of lymphocytes (%) was 14.60 (9.49). The median (IQR) of lymphocytes (%) was 12.00 (7–20.55). The lymphocytes (%) ranged from 1.9 to 57. The mean (SD) of absolute lymphocyte count (ALC) was 1399.98/mm3 (740.10/mm3). The median (IQR) of ALC was 1292.00/mm3 (910.25–1854.75). The ALC ranged from 310 to 3956/mm3. About 23.2% of the participants had ALC: <800/mm3. The mean (SD) of platelet count (x103/mm3) was 219.46 (146.11). The median (IQR) of platelet count (x103/mm3) was 195.50 (138–270.25). The platelet count (x103/mm3) ranged from 19 to 970 × 103/mm3.
Table 2: Summary of routine investigations

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The mean (SD) of s. creatinine (mg/dL) was 1.83 (2.09). The median (IQR) of s. creatinine (mg/dL) was 1.10 (0.9–1.8). The s. creatinine (mg/dL) ranged from 0.3 to 10. The mean (SD) of total bilirubin (mg/dL) was 1.04 (0.82). The median (IQR) of total bilirubin (mg/dL) was 0.80 (0.7–1). The total bilirubin (mg/dL) ranged from 0.5 to 7.7. The mean (SD) of random blood sugar (RBS) (mg/dL) was 200.83 (131.83). The median (IQR) of RBS (mg/dL) was 158.50 (102.25–238.75). The RBS (mg/dL) ranged from 28 to 550. All the above variables were not normally distributed (Shapiro–Wilk Test: P ≤ 0.001). Serum procalcitonin levels were available for 61 patients, and the mean (SD) of s. procalcitonin (ng/mL) was 0.69 (1.27). The median (IQR) of s. procalcitonin (ng/mL) was 0.04 (0.02–0.2). The s. procalcitonin (ng/dL) ranged from 0 to 4. On day 1 of admission, serum lactate dehydrogenase (LDH) was available for 93 patients, and the mean (SD) of s. LDH (mg/dL) was 322.78 (239.00). The median (IQR) of s. LDH (mg/dL) was 239.00 (132–445). The s. LDH (mg/dL) ranged from 22 to 1382. About 51.6% of the participants had s. LDH: ≤240 mg/dL. About 48.4% of the participants had s. LDH: >240 mg/dL. The mean (SD) of s. ferritin (mg/dL) (n = 81) was 403.77 (381.28). The median (IQR) of s. ferritin (mg/dL) was 234.00 (112–554). The s. ferritin (mg/dL) ranged from 24.29 to 2000. About 97.5% of the participants had s. ferritin: ≤1000 mg/dL. About 2.5% of the participants had s. ferritin: >1000 mg/dL [Table 3]. About 69.2% of the participants had X-ray findings suggestive of ground-glass opacities.
Table 3: Summary of inflammatory markers on day of admission

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When baseline parameters were compared against the grade of SARI significant difference (P < 0.05) was present in the following parameters: age >60 years, diabetes mellitus TLC (x103/mm3), neutrophils (%), lymphocytes (%), absolute neutrophil count (ANC) (/mm3), neutrophil–lymphocyte ratio (NLR), platelet count (x103/mm3), s. creatinine (mg/dL), blood urea (mg/dL), total bilirubin (mg/dL), direct bilirubin (mg/dL), serum glutamic-oxaloacetic transaminase (SGOT) (IU/L), serum glutamic-pyruvic transaminase (SGPT) (IU/L), alkaline phosphatase (ALP) (IU/L), s. procalcitonin (ng/dL), s. LDH (mg/dL), s. LDH, s. ferritin (mg/dL), ECG, and X-ray. Highest values of the above parameters were present in patients with sepsis and septic shock.

The following variables were significantly associated (P < 0.05) with the variable “Sepsis Severity”: TLC (x103/mm3), neutrophils (%), and ANC (/mm3), had the highest values in patients with septic shock. We studied the correlation between demographic parameters, SARI grade, and sepsis severity with predetermined outcomes. We found that, TLC (x103/mm3), neutrophils (%), lymphocytes (%), ANC (/mm3), s. creatinine (mg/dL), blood urea (mg/dL), s. Na (mEq/L), total bilirubin (mg/dL), direct bilirubin (mg/dL), SGOT (IU/L), SGPT (IU/L), ALP (IU/L), RBS (mg/dL), s. procalcitonin (ng/dL), S. LDH (mg/dL), S. LDH, s. ferritin (mg/dL), were significantly (P < 0.5) associated with adverse outcomes, with the median being highest in the group which died during a hospital stay. There was a significant difference between the two groups in terms of platelet count (x103/mm3) and absolute leucocyte count, with values being higher in patients who were discharged.

There was a significant difference in patient outcome in terms of the distribution of SARI Grade (χ2 = 49.937, P ≤ 0.001). Patients who died during hospitalization were categorized as critical at the time of admission. There was a significant difference between the various groups in terms of the distribution of sepsis (χ2 = 49.768, P ≤ 0.001). The proportion of sepsis was significantly higher in those who died during hospitalization. There was a significant difference between the survivors and nonsurvivors groups in terms of the distribution of sepsis severity (χ2 = 12.770, P = 0.001). The patients who died showed a higher proportion of septic shock and ARDS as compared to those with only sepsis at the time of presentation.

Neutrophils (%), NLR, lymphocytes (%), ANC (/mm3), s. procalcitonin (ng/dL), TLC (x103/mm3), SGPT (IU/L), direct bilirubin (mg/dL), blood urea (mg/dL), s. LDH (mg/dL), total bilirubin (mg/dL), SGOT (IU/L), s. ferritin (mg/dL), s. creatinine (mg/dL), ALP (IU/L), platelet count (x103/mm3), ALC (/mm3), RBS (mg/dL), S. Na (mEq/L) significantly predicted adverse outcome in our study.[Table 4] and [Table 5]. Although diabetes mellitus was the most common associated chronic illness, there was no significant association of diabetes mellitus with adverse outcomes in our study.
Table 4: Association between outcome and parameters

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Table 5: Receiver operating characteristic curve analysis showing diagnostic performance of various parameters

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   Discussion Top

The COVID-19 pandemic has devastated our globalized world, resulting in the epoch-defining upheaval of human civilization on every front. A novel form of acute respiratory illness caused by severe acute respiratory illness coronavirus-2 (SARS-CoV-2) was first reported from Wuhan city of China in January of the year 2020.[7],[8] COVID-19 was declared a global pandemic by WHO on March 11, 2020, and the interim guidance report of the agency outlined the clinical features, disease severity, and management strategy for the novel coronavirus. The spectrum of symptomatic manifestation shows a wide range, from mild to critical. As we continue to grapple with this dreaded epidemic, researchers around the world are shedding the light of the determinants of disease pathophysiology and severity. The critical disease is seen in up to 5% of patients with viral sepsis being a significant cause of morbidity in COVID-19 disease.[3],[9] Demographic features such as race, gender, and socioeconomic profile are risk factors assigned to severe disease and poor outcomes.[10]

Clinical practice across the globe concurs that dysregulated immune response including direct viral cytotoxicity and endothelial inflammation resulting in multiorgan dysfunction and inflammation can be attributed to SARS-CoV-2. Moreover, these patients full fill the sepsis-3 criteria for sepsis and septic shock.[3],[11],[12] As sepsis requires a time-sensitive multidisciplinary approach the importance of studies aimed at exploring sepsis as a determinant of serve disease cannot be overemphasized.[13]

This retrospective study was carried out with 112 participants in a COVID-19-designated hospital in Delhi, to study the prevalence and impact of sepsis in COVID-19 patients. At presentation, the mean age of participants was 52.46 ± 16.50 years with a median IQR of 55 years. About 35.7% were above 60 years of age. In a study by Wang D et al., the median age at presentation was 56 years with an IQR of 46–68 years. The same study has demonstrated that increasing age is a predictor of mortality in COVID-19.[13] In the present study, the mean (SD) of age (years) in the discharged group was 52.36 (16.79). The mean (SD) age (Years) in the group of patients who died was 52.72 (15.90). There was no significant difference between the groups in terms of age (years) and gender with P > 0.05. According to WHO interim guidance, COVID-19 illness is classified as mild, moderate, and critical. It further classifies critical COVID-19 into three categories: sepsis, septic shock, and ARDS.[2] We used the SOFA score as a diagnostic marker for sepsis based on its ability to detect multiorgan dysfuction.[14] The prevalence of viral sepsis in community-acquired pneumonia ranges between 40% and 50%.[11],[15] In our study, the group fulfilling the criteria of critical COVID-19 formed the largest cohort, at 41.1% (n = 46), followed by moderate COVID-19 at 25% (n = 28). In the present study, the prevalence of sepsis as determined by SOFA score was 41.1% (n = 46). Out of 46 patients who were admitted with the critical disease 19 patients fulfilled the criteria of sepsis, whereas 21 were diagnosed with septic shock. Six patients fulfilled the criteria for ARDS.

About 62.5% of our study population was suffering from underlying comorbid conditions. Diabetes mellitus was the most frequently occurring comorbidity in the study group at 37.5% followed by hypertension,[15] however, no significant correlation was found with adverse outcomes. According to the global tuberculosis report, at 26%, India is one of the 8 nations accounting for two third of the global tuberculosis burden.[16] Hence, we expected to see a significant prevalence of COVID-19 among tuberculosis patients. However, we found only four patients with active tuberculosis. Similarly, there was no significant association between COVID-19 and other chronic respiratory illnesses such as COPD and asthma. Our study population had two PLWHA patients with poor compliance to treatment. In both these patients, the predominant findings were consistent with sepsis with no radiological or clinical signs of respiratory involvement. A daily wages laborer admitted to our ward for severe COVID-19 and generalized tonic–clonic seizure simultaneously tested positive for P. Vivax malaria.

We compared the grade of SARI in the study population with demographic and biochemical parameters. The Chi-square test was used to determine the association between the grade of SARI. Moderate grade SARI had the highest proportion of patients above the age of 60 years irrespective of gender. Diabetes mellitus was the most common comorbidity found in patients categorized with severe SARI and the difference was statistically significant when compared to mild and moderate SARI (P = 0.016). The variables TLC, ANC, and NLR were not normally distributed in the three subgroups of SARI. Thus, nonparametric tests (Kruskal–Wallis test) were used to make group comparisons. There was a significant difference between the four subgroups of SARI (mild, moderate, severe, and critical) in terms of TLC (x103/mm3) (χ2 = 20.413, P ≤ 0.001), with the median TLC (x103/mm3) being highest in critical patients. A similar pattern was observed in absolute neutrophil count of the study population. Highest ANC count was present in the subgroup with critical disease and the difference in comparison to other grades of SARI was statistically significant (P < 0.001). There was a significant difference between the four subgroups of SARI in terms of NLR (χ2 = 26.617, P ≤ 0.001), with the median NLR being highest in the critical SARI subgroup. S. creatinine (mg/dL), blood urea (mg/dL), total bilirubin (mg/dL), direct bilirubin (mg/dL), SGOT (IU/L), SGPT (IU/L), ALP (IU/L), s. procalcitonin (ng/dL), s. LDH (mg/dL), s. LDH, and s. ferritin (mg/dL) were the laboratory variable significantly associated with the grade of SARI (P < 0.05) with the highest values being observed in the subgroup with critical SARI. These findings are congruent with international studies which found that patients of SARS-CoV-2 with sepsis were comparatively older and had a higher prevalence of comorbid illness.[17],[18]

Out of 112 patients included in the study, 29 patients (25.9%) died during the hospital stay. Critical illness was present in 28 of these patients while the remaining one patient had severe disease. Out of 46 patients categorized as critical, death was reported in 28 cases. Mortality was highest in patients with ARDS at 100% (n = 6), followed by patients with septic shock at 76.2% (n = 16). The difference in mortality rate between patients with sepsis, septic shock, and ARDS was statistically significant (<0.001). Fisher's exact test was used to explore the association between the severity of sepsis and mortality.

The best parameter for the outcome for prediction of outcome (death vs. discharge) in terms of area under the receiver operating characteristic and sensitivity was neutrophil%. At a cutoff of neutrophils (%) ≥85, it predicted death with a sensitivity of 86% and a specificity of 70%. Similarly, ALC (/mm3) ≤936, predicted death with a sensitivity of 62%, and a specificity of 82%. Procalcitonin level was the best parameter in terms of sensitivity and positive predictive value. At a cutoff value of ≥1 ng/dL ≥1, serum procalcitonin predicted adverse outcomes with a sensitivity of 54% and a specificity of 95%.

The study focused on patient data from a single medical ward. Data from surgical wards, ICU, and obstetrics would have increased the scope of our study. History and clinical as well as laboratory parameters were not available for patients who died within a short period of arrival in the COVID-19 emergency. As interaction with immediate family was limited, accessing patients' previous health records during treatment was difficult. The prohibitory lockdowns also lead to delays in the access of health care.

Even as the world is being inoculated at an unprecedented speed against COVID-19, the challenges posed by newer variants such as the delta strain are unprecedented. 20A/S: 478K or the B.1.617.2 (Delta) variant was identified in India in December 2020 and has since plunged India into the second wave of the pandemic. The Delta variant had a secondary attack rate of 13.6% and is associated with a higher risk of hospitalization.[19] While dealing with the clinical spectrum of this novel virus, the health-care delivery system has witnessed the critical impact of crippling lockdown which made accessing health care an arduous task, especially for elderly and critically ill patients. Therefore, health research specially aimed at determinants of disease severity such as immunopathogensis, virus characteristic, and health-care delivery find resonance around the medical fraternity.

   Conclusion Top

COVID-19 has become synonymous with respiratory failure, but COVID-19-induced sepsis is an important determinant of disease severity. It precipitates multiorgan dysfunction resulting in adverse outcomes. Identification of sepsis and associated critical disease at the point of initial contact is vital for the optimum management of patient. Association of sepsis with age and comorbidities should be the focal point of our initial patient assessment, especially in the current scenario where timely access to health care may not be assessable to vulnerable populations. The study did not receive any funding and there were no conflicts of interest.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

   References Top

World Health Organization. Coronavirus Disease (COVID-19) Weekly Epidemiological Update and Weekly Operational Update. Available from: [Last accessed on 2021 Jun 13].  Back to cited text no. 1
World Health Organization. Novel Corona Virus (2019-n CoV) Technical Guidance. Available from: [Last accessed on 2021 Jun 13].  Back to cited text no. 2
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020;395:1054-62.  Back to cited text no. 3
Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016;315:801-10.  Back to cited text no. 4
Prescott HC, Langa KM, Iwashyna TJ. Readmission diagnoses after hospitalization for severe sepsis and other acute medical conditions. JAMA 2015;313:1055-7.  Back to cited text no. 5
Li H, Liu L, Zhang D, Xu J, Dai H, Tang N, et al. SARS-CoV-2 and viral sepsis: Observations and hypotheses. Lancet 2020;395:1517-20.  Back to cited text no. 6
World Health Organization. Director-General's Remark at Media Briefing on 2019-nCoV on 11 February 2020. Available from: [Last accessed on 2021 Jun 13].  Back to cited text no. 7
Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020;5:536-44.  Back to cited text no. 8
Wu Z, McGoogan JM. 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 2020;323:1239-42. doi:10.1001/jama.2020.2648.  Back to cited text no. 9
Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med 2020;8:475-81. doi: 10.1016/S2213-2600(20)30079-5. Epub 2020 Feb 24. Erratum in: Lancet Respir Med. 2020 Apr;8(4):e26. PMID: 32105632; PMCID: PMC7102538.  Back to cited text no. 10
Zhou F, Wang Y, Liu Y, Liu X, Gu L, Zhang X, et al. Disease severity and clinical outcomes of community-acquired pneumonia caused by non-influenza respiratory viruses in adults: a multicentre prospective registry study from the CAP-China Network. Eur Respir J 2019;54:1802406. doi: 10.1183/13993003.02406-2018.  Back to cited text no. 11
Lin GL, McGinley JP, Drysdale SB, Pollard AJ. Epidemiology and immune pathogenesis of viral sepsis. Front Immunol 2018;9:2147.  Back to cited text no. 12
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323:1061-9.  Back to cited text no. 13
Ferreira FL, Bota DP, Bross A, Mélot C, Vincent JL. Serial evaluation of the SOFA Score to predict outcome in critically ill patients. JAMA 2001;286:1754-8.  Back to cited text no. 14
Ren C, Yao RQ, Ren D, Li JX, Li Y, Liu XY, et al. The clinical features and prognostic assessment of SARS-CoV-2 infection-induced sepsis among COVID-19 patients in Shenzhen, China. Front Med (Lausanne) 2020;7:570853.  Back to cited text no. 15
World Health Organization. Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2016. Geneva: World Health Organization; 2018. Available from: [Last accessed on 2020 Jul 20].  Back to cited text no. 16
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020;395:507-13.  Back to cited text no. 17
Alhazzani W, Møller MH, Arabi YM, Loeb M, Gong MN, Fan E, et al. Surviving sepsis campaign: Guidelines on the management of critically ill adults with coronavirus disease 2019 (COVID-19). Intensive Care Med 2020;46:854-87.  Back to cited text no. 18
Levin AT, Hanage WP, Owusu-Boaitey N, Cochran KB, Walsh SP, Meyerowitz-Katz G. Assessing the age specificity of infection fatality rates for COVID-19: Systematic review, meta-analysis, and public policy implications. Eur J Epidemiol 2020;35:1123-38.  Back to cited text no. 19


  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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