|Year : 2023 | Volume
| Issue : 2 | Page : 176-182
Variation in biochemical parameters in COVID-19 patients admitted at a tertiary care dedicated COVID hospital: A prospective study
Visesh Kumar1, Mala Mahto1, Sushil Kumar1, Akash Bansal1, Alok Ranjan2, Shamshad Ahmad2, Ayan Banerjee1
1 Department of Biochemistry, All India Institute of Medical Sciences, Patna, Bihar, India
2 Department of Community and Family Medicine, All India Institute of Medical Sciences, Patna, Bihar, India
|Date of Submission||15-Feb-2022|
|Date of Decision||26-May-2022|
|Date of Acceptance||03-Jun-2022|
|Date of Web Publication||4-Apr-2023|
Department of Biochemistry, All India Institute of Medical Sciences, Phulwarisharif, Patna - 801 507, Bihar
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Introduction: COVID-19 infection has a myriad of presentation. Rural India and other developing nations are relatively resource poor, not having access to modern specialized investigations. In this study, we tried to evaluate only biochemical parameters in predicting the severity of the infection. The aim of this study was to find a cost-effective means to predict the clinical course at the time of admission and thereby to reduce mortality and, if possible, morbidity by timely intervention. Materials and Methods: All COVID-19-positive cases admitted at our hospital from March 21 to December 31, 2020, were recruited in this study. The same acted as sham control at recovery. Results: We observed a significant difference in biochemical parameters at the time of admission and discharge, between mild/moderate disease and severe disease. We found slightly deranged liver function tests at admission, which becomes normal at the time of discharge. Urea, C-reactive protein (CRP), procalcitonin, lactate dehydrogenase, and ferritin concentrations in severe/critical patients were significantly higher than that in the mild/moderate group. Receiver operating characteristic curves were plotted to predict the severity on the basis of biochemical parameters independently, of the patients based on these values. Conclusion: We proposed cutoff values of certain biochemical parameters, which will help in judging the severity of the infection at admission. We developed a predictive model with a significant predictive capability for CRP and ferritin values, using normal available biochemical parameters, routinely done in resource-poor centers. Clinicians working in resource-poor situations will be benefitted by having an idea of the severity of the disease. Timely intervention will reduce mortality and severe morbidity.
| Abstract in French|| |
Introduction: L'infection au COVID19 a une myriade de présentations. L'Inde rurale et d'autres pays en développement sont relativement pauvres en ressources, non avoir accès aux enquêtes spécialisées modernes. Dans cette étude, nous avons essayé d'évaluer uniquement les paramètres biochimiques pour prédire la gravité de l'infection. Le but de cette étude était de trouver un moyen rentable de prédire l'évolution clinique au moment de l'admission et ainsi de réduire la mortalité et, si possible, la morbidité par une intervention rapide. Matériels et méthodes: Tous les cas positifs au COVID19 admis à notre hospitalisés du 21 mars au 31 décembre 2020, ont été recrutés dans cette étude. La même chose a agi comme un contrôle factice lors de la récupération. Résultats: Nous avons observé une différence significative dans les paramètres biochimiques au moment de l'admission et de la sortie, entre une maladie légère/modérée et une maladie grave. Nous avons trouvé des tests de la fonction hépatique légèrement dérangés à l'admission, qui deviennent normaux au moment de la sortie. Urée, protéine Créactive (CRP, les concentrations de procalcitonine, de lactate déshydrogénase et de ferritine chez les patients sévères/critiques étaient significativement plus élevées que chez les patients légers/modérés groupe. Les courbes caractéristiques de fonctionnement du récepteur ont été tracées pour prédire la gravité sur la base de paramètres biochimiques indépendamment, deles patients en fonction de ces valeurs. Conclusion: Nous avons proposé des valeurs seuils de certains paramètres biochimiques, qui permettront de juger de la gravité de l'infection à l'admission. Nous avons développé un modèle prédictif avec une capacité prédictive significative pour les valeurs de CRP et de ferritine, en utilisant les paramètres biochimiques normaux disponibles, systématiquement effectués dans les centres pauvres en ressources. Les cliniciens travaillant dans des situations où les ressources sont limitées bénéficier d'avoir une idée de la gravité de la maladie. Une intervention rapide réduira la mortalité et la morbidité grave.
Mots-clés: COVID19, ferritine, lactate déshydrogénase, urée
Keywords: COVID-19, ferritin, lactate dehydrogenase, urea
|How to cite this article:|
Kumar V, Mahto M, Kumar S, Bansal A, Ranjan A, Ahmad S, Banerjee A. Variation in biochemical parameters in COVID-19 patients admitted at a tertiary care dedicated COVID hospital: A prospective study. Ann Afr Med 2023;22:176-82
|How to cite this URL:|
Kumar V, Mahto M, Kumar S, Bansal A, Ranjan A, Ahmad S, Banerjee A. Variation in biochemical parameters in COVID-19 patients admitted at a tertiary care dedicated COVID hospital: A prospective study. Ann Afr Med [serial online] 2023 [cited 2023 Jun 6];22:176-82. Available from: https://www.annalsafrmed.org/text.asp?2023/22/2/176/373568
| Introduction|| |
After the confirmation of the first case of COVID-19 from Wuhan, China, the disease has spread globally to almost all the countries in the world. It has been declared pandemic by the World Health Organization (WHO) dated April 6, 2020. The virus which causes the coronavirus disease 2019 (COVID-19) was named SARS-CoV-2 by the International Committee of Taxonomy of Viruses. This pandemic has created a major burden on economy and patients mortality and morbidity not only in India but also globally.
Coronaviruses are positive-sense single-stranded ribonucleic acid (RNA) viruses. The RNA genome of SARS-CoV-2 has about 30,000 nucleotides, encoding for 29 proteins. Gold standard diagnosis of COVID-19 is done by molecular identification of the SARS-CoV-2 using nucleic acid amplification tests such as the reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR). Since India and many developing countries are not equipped with sufficient laboratory and human resource capacity to perform massive molecular identification, it leads to delays between testing and confirmation. Even after confirmation, clinical presentation is unpredictable. Till date, no single parameter is good enough to predict mortality caused by this virus. Patient history, hematological and biochemical laboratory parameters, and imaging are the mainstays for monitoring patient status.
Major concern about SARS-CoV-2 is that this disease is rapidly deteriorating the health condition of high-risk groups such as elderly and immune compromised people, so timely detection and monitoring of disease progression is much needed. Rural India as well as many developing countries also does not have the access to various specialized investigations such as interleukin-6 (IL-6), ferritin, and C-reactive protein (CRP). Hence, in this study, we focused on the variation in various biochemical parameters in COVID-19 patients and correlated accordingly. In this, we tried to evaluate the variations of the biochemical parameters in COVID-19-infected patients at admission and during discharge. We also tried to create a model to predict the values of IL-6, ferritin, and CRP from routine biochemical investigations along with the age and gender of the patients.
There are few studies done worldwide analyzing only the biochemical parameters and very few have been reported from India. Hence, the present study aimed to evaluate variation in various biochemical parameters in COVID-19 patients at active infection and with when they have clinically recovered.
| Materials and Methods|| |
All patients admitted at our hospital from March 21 to December 31, 2020, were recruited in this study. Severity of the disease was classified as diagnosed positive cases of COVID-19 through RT-PCR with clinical picture: mild (PaO2/FIO2 = 101–200 mmHg), moderate (PaO2/FIO2 = 101–200 mmHg with PEEP ≥5 cm of H2O), and severe cases (PaO2/FIO2 ≤100 mmHg with PEEP ≥5 cm of H2O).
LFT, KFT, CRP, and lactate dehydrogenase (LDH) were measured on a BECKMAN COULTER AU680 machine as per the manufacturer's protocol.
Ferritin and IL-6 were measured by chemiluminescent method on SIEMENS ADVIA CENTAUR as per the manufacturer's protocol. Patients who had expired were excluded from the study.
All analyses were carried out using SPSS, version 22 (SPSS Inc., Chicago, IL, USA). Categorical variables were presented as proportion. Continuous variables were presented as median and interquartile range values. Shapiro–Wilk test was used to verify the normal distribution of all continuous variables taking P < 0.05. Almost all the biochemical variables were not normally distributed; hence, nonparametric tests, such as Mann–Whitney U-test, were applied for the continuous variables and Chi-square test for the categorical variables. Spearman's correlation coefficient was used to measure association between severities of COVID-19 with biochemical variables with significance test for the correlation coefficient. Receiver operating characteristic (ROC) curve analysis was used to determine the optimum cutoff points of significantly correlated biochemical variables with severity of disease and area under curve with 95% confidence interval with significance value for each variable. The cutoff point was determined using the index of union method. A 2-tailed P < 0.05 was considered statistically significant.
| Results|| |
The study population included 202 hospitalized patients with confirmed COVID-19. Out of 202 total cases, 43 cases were of severe condition and 159 cases were of mild/moderate level. The mean age was 51.22 years (95% confidence interval [CI]: 49.1–53.3 years); there was no statistically significant difference in age (P = 0.42) between severe cases (52.8 years; 95% CI: 48.37–57.30) and mild/moderate cases (50.78 years; 95% CI: 48.37–53.19 years). There was no significant gender distribution between the two groups of disease severity (Chi-square at 1, df = 1.49; P = 0.22). The proportion of male cases was 76.7%.
From [Table 1], it is obvious that a highly significant difference was observed at admission in the biochemical variables such as urea, sodium, CRP, ferritin, LDH, and procalcitonin (PCT) between severe cases as compared to mild/moderate cases of COVID-19. Inflammatory parameters as well as acute phase reactants such as CRP, ferritin, and PCT are more elevated in severe cases compared to mild–moderate cases. Elevated urea, sodium, serum glutamic oxaloacetic transaminase (SGOT), and serum glutamic pyruvic transaminase (SGPT), may be, and are indicative that the patients belonging to the severe category have mildly deranged kidney and liver parameters.
|Table 1: Comparison of biochemical variables of mild/moderate and severe coronavirus disease 2019 cases at admission|
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From [Table 2], it is evident that there is a significant difference in the biochemical variables such as total protein, albumin, globulin, ferritin, and LDH at discharge between severe cases as compared to mild/moderate cases of COVID-19. All the patients were discharged based on the clinical recovery. At the time of discharge, values of some inflammatory parameters and acute phase reactants as well as well SGOT and SGPT have fallen as compared with the time of admission, which signifies better outcome. Patients belonging to the severe category were admitted to the hospital for a prolonged duration when compared to the mild/moderate category, which may explain the lower protein, albumin, and globulin levels in them, at the time of discharge. Even though these patients were discharged based on clinical recovery, elevated ferritin and LDH values may indicate that they have not recovered biochemically completely and these parameters may take longer time to attain its normal values respectively, more gradually.
|Table 2: Comparison of biochemical variables of mild/moderate and severe coronavirus disease 2019 cases at discharge|
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Further, we wanted to evaluate whether there exists any significant correlation between the biochemical parameters and severity of the disease, at the time of admission. [Table 3] presents the correlation coefficient of all biochemical variables with severity status of the patients at admission. Biochemical variables such as urea, sodium, CRP, ferritin, LDH, and PCT were found to be positively and significantly correlated with the severity of COVID-19. The positive correlation establishes the fact that disease severity is reflected by increased values of these parameters. These parameters will further help the clinicians to categorize the patients as mild/moderate or severe independently in resource poor situations.
|Table 3: Correlation coefficient and P value between items and disease severity|
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Next, we wanted to determine a specific cutoff value of the biochemical parameters which can be used to predict the severity of the disease category independent of the clinical categorization. For this, we plotted a ROC curve for all the parameters which were significantly different between the two groups. The ROC curves of significantly associated biochemical variables were determined and are listed in [Table 4]. The ROC curves for urea (area under the curve [AUC] = 0.645, 95% CI: 0.56–0.73; P = 0.004; [Figure 1]a) suggested that the best cutoff point was ≥38 units with a sensitivity of 60.9% and a specificity of 60.8%. The ROC curve for sodium (AUC = 0.610, 95% CI: 0.52–0.70; P = 0.032; [Figure 1]b) suggested the best cutoff points ≥135.5 mEq/L with a sensitivity of 60.5% and a specificity of 51.6%. The ROC curve for CRP (AUC = 0.60, 95% CI: 0.51–0.696; P = 0.047; [Figure 1]c) suggested the best cutoff point ≥69 mg/L with a sensitivity of 60% and a specificity of 54.4%. The ROC curve for ferritin (AUC = 0.645, 95% CI: 0.55–0.734; P = 0.004; [Figure 1]d) suggested the best cutoff point ≥556 ng/ml with a sensitivity of 65% and a specificity of 55.6%. The ROC curve for LDH (AUC = 0.655, 95% CI: 0.56–0.75, P = 0.002. [Figure 1]e) suggested the best cutoff point ≥816 U/L with a sensitivity of 65.1% and a specificity of 53.5%. The ROC curve of PCT (AUC = 0.63 95% CI: 0.53–0.73, P = 0.015; [Figure 1]f) suggested the best cutoff point ≥0.47 ng/ml with a sensitivity of 63.2% and a specificity of 60.6%. The ROC curve for Chloride (AUC = 0.596, 95% CI: 0.51–0.659, P = 0.051; [Figure 1]g) suggested the best cutoff point ≥100 mEq/L with a sensitivity of 58.1% and a specificity of 61.1%.
|Table 4: Receiver operating characteristic curves for cutoff values of significant biochemical variables|
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|Figure 1: (a) ROC curve of urea at baseline. (b) ROC curve of sodium at baseline. (c) ROC curve of CRP at baseline. (d) ROC curve of ferritin at baseline. (e) ROC curve of LDH at baseline. (f) ROC curve of PCT at baseline. (g) ROC curve of chloride at baseline. ROC: Receiver operating characteristic, CRP: C-reactive protein, LDH: Lactate dehydrogenase, PCT: Procalcitonin|
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There are various hospitals which do not have access to specialized investigations. Hence, we tried to develop a predictive model for CRP, ferritin, and IL-6 at baseline using the routine tested parameters, age, and gender. [Table 5] and [Table 6] show prediction of model of CRP and ferritin at baseline of COVID-19 patients.
|Table 5: Prediction of C-reactive protein at baseline of coronavirus disease 2019 patients|
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|Table 6: Prediction of Ferritin at baseline of coronavirus disease 2019 patients|
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Model: F (5, 180) = 7.42; P < 0.001: R2 = 0.1708 (SEX = 1 for Male and 0 for Female in the above formula).
CRP = 479 + 31.43(SEX) 131.57 (AG) +0.52 (UREA) 13.64 (PHOS) 2.45 (CHLORIDE)
(Sex = 1 for Male and 0 for Female in the above formula). R2 = 0.1708
FERRITIN = 1145.33 + 215.06 (SEVERITY) +1.948 (SGPT) +6.25 (UREA) 121.26 (TP) 66.48 (CREATININE)
(Severity value is 1 for severe cases and 0 for mild/moderate cases in the above formula). R2 = 0.1933
Both the models predictive value is highly significant at P < 0.001.
| Discussion|| |
We have tested almost all biochemical parameters that are done in our department and analyzed them. Most of the patients were of mild to moderate severity and had better recovery. However, it is important to identify severely ill patients earlier so that timely management can be initiated and mortality can be prevented in some cases. We observed almost 202 COVID-19 patients having clinical features such as fever, fatigue, and cough as observed in other studies. In this study, we observed the difference in biochemical parameters at the time of admission between the patients who were admitted in normal wards (mild/ moderate category) and in intensive care unit (severe category), but largely there was no significant difference in those parameters during discharge, which were significantly different at the time of admission, further establishing the fact the criteria set for discharge were robust and well thought of. A study done by Zhang C et al. showed that 2%–11% of patients with COVID-19 had liver comorbidities and 14%–53% of cases had abnormal alanine transaminase and aspartate transaminase levels during progression of COVID-19 disease. This study also showed that liver damage in mild cases of COVID-19 is often transient. Our study also corroborated their findings as there is mild elevation in SGOT/SGPT in mild/moderate and severe category, respectively, at admission and it decreases with time at the time of discharge. There was a difference in the values of SGPT ,though not statistically significant (P < 0.06) at admission between the mild/moderate and the severe category of the patients stating deranged liver function tests at admission, more in case of severe category which recovers fully at the time of discharge of the patients. Another study done by Xu Wei et al. had shown that urea and creatinine are increased in COVID-19 patients. Our study revealed that urea was significantly increased in severe variety of cases at the time of admission, but at the time of discharge, both urea and creatinine were normal. Almost all biochemical parameters are in the same range at the time of discharge as there is no significant difference between mild/moderate and severe cases except for total protein, ferritin, and LDH.
In conformity with Liu J's and Wan S's study, our study also found that levels of CRP were significantly associated with the severity of COVID-19. In addition, LDH, PCT, and ferritin were also related to disease severity in a significant way.
There are 5 isoenzymes of LDH in humans (LDH-1 in cardiomyocytes, LDH-2 in reticuloendothelial system, LDH-3 in pneumocytes, LDH-4 in kidneys and pancreas, and LDH-5 in liver and striated muscle). Multiple organ injury and decreased oxygenation with upregulation of the glycolytic pathway is one of the major causes of elevated LDH values apart from cardiac damage. There is cytokine-mediated tissue damage and LDH release in severe infections. Severe form of interstitial pneumonia with acute respiratory distress is the salient feature of COVID-19. As LDH isoenzymes 3 is present in lung tissue, greater release of this enzyme is natural in COVID-19 infection. In our study, there was a significant difference of LDH at admission and discharge between the mild/moderate and severe group of patients in consonance with the above-mentioned study. Even though there was a significant difference at discharge between the groups, the values of both the groups had fallen significantly when compared with that of the admission. The higher value at discharge in the severe category of patients is probably due to prolonged recovery time taken by these.
In our study, the PCT, LDH, and ferritin concentration in severe/critical patients was significantly higher than that in the mild/moderate group. CRP and ferritin are nonspecific acute-phase proteins and are increased in cases of inflammation. To the best of our knowledge, this study is one of the early to determine relationships between CRP, ferritin, LDH, PCT, and COVID-19. We have derived cutoff values for ferritin ≥556.4 ng/ml, LDH ≥816 U/l, PCT ≥0.47 ng/ml, CRP ≥69.9 mg/L, which can stratify patients category based on biochemical parameters only. These cutoff values can be applied to fresh infections and the severity of the disease can be predicted using these values. The values above this cutoff were more likely to develop severe disease. High ferritin levels may be due to secondary hemophagocytic lymphohistiocytosis (sHLH) and cytokine storm syndrome in a severe variety of COVID-19 patients. sHLH comprises an underrecognized, hyperinflammatory syndrome having a fulminant and fatal hypercytokinemia with multiorgan failure. In adults, sHLH is most commonly provoked by viral infections, and it occurs in 3·7%–4·3% of patients having sepsis.
As India is a resource-poor country, markers such as CRP, ferritin, IL-6, and PCT are not tested readily in rural regions. We tried to develop models which could be used to predict these values using simple routine biochemical parameters which are widely available. We tried to predict CRP and ferritin with the normal available biochemical parameters which are readily done in the periphery. Prediction of CRP was done with gender, AG ratio, urea, phosphorous, and chloride, while that of ferritin was done with SGPT, urea, total protein, and creatinine. These models can predict with a significance value of <0.001. These models can be used in rural set up not only in India but also other developing nations to predict CRP and ferritin values, which are important biochemical parameters used in disease stratification. We could not predict a successful model for IL-6 as the sample size was small for IL-6 to develop a predictive model.
| Conclusion|| |
This study will help in stratifying COVID-19 patients in limited resource centers. However, our study is with certain lacunae. The sample size should have been even much larger to predict the models. Moreover, the models could not be tested in patients. Another study with a much bigger sample size is required to further reinforce our findings and test the predicted models.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]