Original article / research
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2023 |
Month :
July
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Volume :
12 |
Issue :
3 |
Page :
BO11 - BO16 |
Full Version
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Predictors of Severity and Mortality among Hospitalised COVID-19 Patients during First Three Waves- A Retrospective Study
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Shailaja Alapaty, Suhas Dhulipala, Mangala N Sirsikar, Umalakshmi Annavarapu, Deepthi Mahendrakar, Alamelu Haran, Praveen Katare
1. Professor, Department of Biochemistry, Vydehi Institute of Medical Sciences and RC, Bangalore, Karnataka, India.
2. Sr. Resident, Department of Pulmonology, Vydehi Institute of Medical Sciences and RC, Bangalore, Karnataka, India.
3. Associate Professor, Department of Biochemistry, Vydehi Institute of Medical Sciences and RC, Bangalore, Karnataka, India.
4. Assistant Professor, Department of Biochemistry, Vydehi Institute of Medical Sciences and RC, Bangalore, Karnataka, India.
5. Assistant Professor, Department of Biochemistry, Vydehi Institute of Medical Sciences and RC, Bangalore, Karnataka, India.
6. Professor and Head, Department of Pulmonology, Vydehi Institute of Medical Sciences and RC, Bangalore, Karnataka, India.
7. Associate Professor, Department of Pulmonology, Vydehi Institute of Medical Sciences and RC, Bangalore, Karnataka, India.
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Correspondence
Address :
Shailaja Alapaty, Suhas Dhulipala, Mangala N Sirsikar, Umalakshmi Annavarapu, Deepthi Mahendrakar, Alamelu Haran, Praveen Katare,
Mangala N Sirsikar,
Vydehi Institute of Medical Sciences and Research Centre, #82, Nallurahalli, Whitefield, Bangalore-560066, Karnataka, India.
E-mail: mangalans81@gmail.com
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| | | ABSTRACT |  | : Introduction: Coronavirus disease 2019 (COVID-19) was was a global pandemic outbreak that emerged in three waves of varying severity. The inflammatory markers can be used to identify the transition of severity of the disease from a mild to severe or critical illness and also to predict the mortality. To improve clinical outcomes, continuous monitoring of these biomarkers in hospitalised patients is essential.
Aim: To compare the serum C-Reactive Protein (CRP), Lactate Dehydrogenase (LDH), serum ferritin and D-Dimer as a predictor of severity and mortality in hospitalised patients in different clinical categories during three waves of COVID-19 .
Materials and Methods: This retrospective study was conducted at Vydehi Institute of Medical Sciences and Research Centre, Karnataka. A total of 6189 patient’s data were retrieved from medical record section with confirmed cases of COVID-19 admitted during the three waves of COVID-19. The results of four inflammatory marker on the day of admission were analysed in mild, moderate, severe and critically ill patients and compared with disease severity, survival and death and also assessed the difference between first, second, and third wave of COVID-19 for sensitivity and specificity. Data was analysed by Chi-square test, Mann Whitney U test, ANOVA (Analysis of Variance), and Kruskal Wallis test and Receiver Operating Curve (ROC).
Results: The median age of patients in the mild category was found to be younger in all three waves compared to those in the severe and critically ill categories. In terms of gender distribution, males were more prevalent than females. Inflammatory markers were significantly increased in all three waves in the severe and critically ill categories. The median CRP (mg/dL) values were significantly increased in the critically ill in wave three compared to waves one and two. The median values of LDH IU/L significantly increased in critically ill patients in the second wave. The median values of ferritin increased significantly in wave one. The D-dimer values significantly increased in the critically ill category in wave three.
Conclusion: Before proceeding on to a specific diagnosis by RT-PCR, a combination of standard laboratory markers (CRP, LDH, and Ferritin D-Dimer) could accurately and reliably predict the diagnosis of COVID-19 with established sensitivity and specificity. |
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| Keywords
: Coronavirus disease-2019, Inflammation, Pandemic, Lactate dehydrogenase |
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| DOI and Others
: DOI: 10.7860/NJLM/2023/58855.2740
Date of Submission: Jul 03, 2022
Date of Peer Review: Oct 29, 2022
Date of Acceptance: Dec 26, 2022
Date of Publishing: Jul 01, 2023
AUTHOR DECLARATION:
• Financial or Other Competing Interests: None
• Was Ethics Committee Approval obtained for this study? Yes
• Was informed consent obtained from the subjects involved in the study? No
• For any images presented appropriate consent has been obtained from the subjects. No
PLAGIARISM CHECKING METHODS:
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ETYMOLOGY: Author Origin |
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INTRODUCTION |
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Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) was declared a pandemic by the World Health Organisation (WHO) on March 11, 2020 (1). COVID-19 presents with varying degree of clinical severity, ranging from asymptomatic to severe pneumonia, Acute Respiratory Distress Syndrome (ARDS), and even death (2). Development of inflammatory reactions in patients with COVID-19 occurs due to rapid viral replications of SARS-CoV-2 (3).
Progressive cellular destruction leads to accumulation of macrophages and monocytes, which induces the release of cytokines and chemokine resulting in hyperinflammatory state called cytokine storm (4),(5). Cytokine storms presents clinically with progressive deterioration characterised by coagulopathy, hypotension, and multiple end-organ dysfunction (6). A number of inflammatory biomarkers had the potential to monitor and determine COVID-19 infection severity and fatality. Patients with COVID-19 had previously been found to have elevated levels of inflammatory markers like CRP and ferritin, LDH as well as alterations like D-Dimer, but little was known regarding their relationship with disease severity among three waves of COVID-19 pandemic (7). Computed Tomography (CT) imaging and chest X-ray, also plays an important role in assessing the severity of the disease but has got its own limitations of cost, in ability to use during high oxygen requirement or ventilator support (8).
Improved therapeutic outcomes may be associated to anti-inflammatory treatments designed to reduce these cytokine levels. The first COVID-19 infections in India were reported on 30th January, 2020 in three places in Kerala, and they persisted until September 2020 (9). A second wave begins in March 2021, which was much more severe than the first, with limited vaccines, hospital beds, oxygen supports and other medical services in parts of the country and it lasted till December 2021 (10). The third wave in India started in the third week of November 2021 and peaked in Jan 2022 (11). The similarities and differences between the characteristics of the three waves remain largely unknown, change in variants pathogenesis and transmission among three waves was essential.
Thus ongoing pandemic would benefit from the most efficient distribution of limited human and technical resource such as oxygen cylinder, ICU admission, treatment modalites and vaccines if routine laboratory parameters were analysed and established to assess the severity of the disease among three waves. Early identification of patients who were predicted to deteriorate was crucial for the appropriate utilisation of the available medical resources in each waves of pandemic. At present many literature is available only on altered inflammatory markers in first and second wave of COVID-19 (12),(13). Few studies have been conducted in the emergency unit regarding the study of early routine biomarkers for the prediction of morbidity and mortality of COVID-19 patients (14),(15),(16),(17). But, there was no article available in Indian setup, comparing the alteration in inflammatory markers in all three waves of COVID-19.
Hence, present study was conducted to understand the utility of inflammatory markers in each wave and also could be identified as selective biomarkers during different clinical staging. Present study was undertaken to establish the link between the trend of proinflammatory cytokines, such as CRP, LDH, ferritin, and D-Dimer and the overall outcomes in patients with COVID-19 in terms of survival and mortality in three waves.
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Material and Methods |
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A retrospective study was conducted at a tertiary care centre for the care of COVID-19, Vydehi Institute of Medical Sciences and Research Centre, Karnataka, India. A comparative data analysis was done with COVID-19 patients who were hospitalised during the first wave from 01-03-2020 to 01-05-2020, the second wave from 1-10-2020 onwards till 01-05-2021 and third wave from 01-11-2021 to 01-03-2022. The study protocol was approved by the Ethics Committee (VIEC: /2021/APP/004, EC reg NO: ECR/747/Inst/KA/2015/RR-18), and a waiver for consent was granted from all the study subjects.
Inclusion criteria: The patients aged ≥18 years of both gender who were hospitalised with a positive SARS-CoV-2 Polymerase Chain Reaction (PCR) assay of nasal, pharyngeal, or lower respiratory tract samples were included.
Exclusion criteria: Patients with co-morbid conditions such as heart attacks, trauma, infections, burns, chronic inflammatory diseases like lupus, vasculitis, rheumatoid arthritis and inflammatory bowel diseases were excluded from the study. Patients with suspected COVID-19 infection without laboratory confirmation by RT-PCR and those who presented to the hospital with symptoms consistent with COVID-19 but did not required hospitalisation, were also excluded.
Data collection: A thorough computerised search from the medical record section was conducted based on accessibility, demographic information, and results of laboratory inflammatory results like serum CRP, LDH, Ferritin, and D-Dimer levels within 24 hours of hospitalisation, clinical state, requirement for oxygen, and ventilatory support. The data was retrieved in all three waves and sorted. Patients with only serum CRP data were collected in wave one (N=1729), wave two (N=458), and wave three (N = 52). LDH wave one N (1398). wave two (N=448), wave three has (N=11). Ferritin wave one (N=1683) wave two (N=410), wave three-nil data. D-Dimer wave one (N=1702), wave two (N=25) and wave three (N=52).
The disease severity at the time of hospitalisation was classified based on the new guidelines for COVID-19 disease severity by National Institute of Health issued by the Government of India as described below (18). Inflammatory marker values were recorded in mild, moderate, severe and critically ill categories (19) and compared in all the 3 waves.
Mild: Individuals who had any of the various signs and symptoms of COVID-19 (e.g., fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell) but who do not have shortness of breath, dyspnoea, or abnormal chest imaging.
Moderate: Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and who have an oxygen saturation (SpO2) of 94% on room air at sea level.
Severe: Individuals with a SpO2 of 94% on room air at sea level, a PaO2/FiO2 [oxygen in arterial blood (PaO2) to the fraction of the oxygen in the inspired air (FiO2)]. ratio of 300 mmHg, a respiratory rate greater than 30 breaths per minute, or lung infiltrates greater than 50%. Critically ill-Individuals who had respiratory failure, septic shock, and/or multiple organ dysfunction (19).
CRP was measured using the Nephalometric method in the Beckman image 800.range for CRP is 0.07-0.75 mg/dL. A Beckman dxc- 860 chemistry analyser was used to measure LDH and ferritin. LDH-100-190 IU/L below 60 years, above 60 years, 110-210 IU/L. Ferritin-20-250 ng/mL, D-Dimer analysed by ACL elite PRO coagulation analyser with reference range of < 255 ng/mL (20).
Statistical Analysis
Data entry was done in a Microsoft Excel 2007 spreadsheet. Data analysis was carried out by IBM Statistical Package for Social Sciences version 25.0 software. All p-values <0.05 were deemed statistically significant. Data was presented as either median or interquartile range for continuous, or frequency and percentages for categorical variables. The normality of the data was checked through the Shapiro-Wilk test. A non parametric test, i.e., the Mann Whitney U test, was used to compare differences between two independent groups when the data was not normally distributed. The Chi-square test, or Fisher’s exact test, was applied to the frequency distribution accordingly. A Receiver Operating Characteristic (ROC) curve was drawn to identify the optimal cut-off points of biomarkers to know their prognostic value. The Kruskal-Walli test was performed separately for waves 1, 2, and 3 to compare the median values of age, sex, and CRP/LDH/Ferritin/D-Dimer between the different severity grades. The Mann Whitney test was performed as a post hoc test to check the intragroup significance.
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Results |
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In all the three waves median age of patients in mild category found to be younger compared to moderate, severe and critically ill. According to genderwise distribution, male patients were more compared to females but this difference was not statistically significant. The median and IQR value of CRP (mg/dL) increased significantly in all three waves from mild to critically ill category. In wave one 1619 (93.63%) were survived and 110 (6.3%) had mortality. In wave two 392 (85.58%) survived and 66 (14.4%) had mortality. In wave three CRP (mg/dL) significantly increased in critically ill above 8.15 (2.92, 25.90)compared to other two waves and with no mortality (Table/Fig 1).
In wave one, AUC of 0.69 with cut-off points of CRP 1.25 mg/dL (12.5 mg/L) suggest it as a better marker of mortality in critically ill category (sensitivity-71.3%, specificity-59.8%). In wave two: AUC of 0.67 with cut-off points of CRP 4.16 mg/dL (41.6 mg/L) suggests it as a better marker of mortality in severe category (sensitivity-70.10%, specificity-56%). In wave three: AUC of 0.764 with cut-off points of CRP 1.92 mg/dL (19.2 mg/L) suggests it as a better marker of mortality in severe category (sensitivity-75%, specificity-42.1%) (Table/Fig 2).
The median age of patients in the mild category was found to be younger in all three waves compared to those in the moderately severe and critically ill categories. Males outnumbered females in terms of gender distribution, but the difference was not statistically significant. LDH (IU/L) median and IQR values increased significantly across all three waves, from mild to critically ill. In wave one, LDH (IU/L)significantly increased in critically ill patients above 380 (280.5, 511.5); 1272 (90.98%) patients survived, and 126 (9.01%) patients died. The number of severely and critically ill patients increased significantly in wave two 253.5 (197.2, 321.2) and 426.5 (299.5, 613.2), respectively, with 376 (83.92%) patients surviving and 72 (16.07%) dying. In wave three, LDH (IU/L) significantly increased in critically ill patients above 369 (207,522) with no mortality (Table/Fig 3).
In wave one, AUC of 0.63 with cut-off points of LDH 204IU/L suggests it as a better marker of mortality in critically ill category (sensitivity-73%, specificity-53.8%). In wave two, AUC 0.84 with cut-off points of LDH was 226.5IU/ better marker of mortality in severe category (sensitivity - 90%, specificity- 63.5%). In wave three, AUC=0.86 with cut-off points of LDH was 293.5 IU/L better marker of mortality in critically ill category (sensitivity-60%, specificity-11%) (Table/Fig 4).
In wave two, median age of patients in mild category found to be younger compared to moderate, severe and critically ill above 51 years to 57 years. Males outnumbered females in terms of gender distribution, but the difference was not statistically significant. The median and IQR value of ferritin (ng/mL) increased significantly in two waves from mild to critically ill category. In wave one and two median and IQR value of Ferritin (ng/mL) significantly increased in severe and critically ill above 339 (154.6, 586.2), 466.6 (218.9, 881.2), 325.8 (146, 577.2), 424.1 (197.1, 842.4) respectively.
In wave one, 1556 (92.5%) patients survived and 127 (7.5%) patients had mortality, with p-values <0.001. In wave two, 350 (85.4%) patients survived and while 60 (14.6%) died (Table/Fig 5).
In wave one, AUC of 0.67 with cut-off points of ferritin 187.6 (ng/mL) suggests it as a better marker of mortality in critically ill category (sensitivity-70%, specificity-57.3%), In wave two, AUC 0.74, 0.61 with cut-off points of Ferritin 183.9/301.5 (ng/mL) suggests it as a better marker of mortality in severe and critically ill category (sensitivity-80.1%/58.1, specificity - 54.2/55.1%) (Table/Fig 6).
The median age of patients in the mild category was found to be younger in all three waves compared to those in the moderately severe and critically ill categories. Males outnumbered females in terms of gender distribution, but the difference was not statistically significant. The median and IQR values of D-Dimer ng/mL increased significantly in all three waves, from mild to critically ill. In wave one, the median and IQR values of D-Dimer significantly increased in severe and critically ill patients with 287 (213, 587), 462 (300, 1094.5), and 1583 (93%) patients surviving, while 119 (6.99%) patients died. In wave two, the median and IQR values of D-Dimer (ng/mL) significantly increased in the severe and critically ill categories 408.5 (291,861), 584 (290.5, 973), respectively, and no mortality. In wave three, D-Dimer significantly increased in all clinical categories, significantly in the critically ill above 795 (233, 1200), and with no mortality (Table/Fig 7).
In wave one, AUC of 0.75 with cut-off points of D-Dimer 240.5 ng/mL suggests it as a better marker of mortality in critically ill category (sensitivity-85.1%, specificity-54.8%), In wave two, AUC (0.76) with cut-off points of D-Dimer 310 ng/mL suggest it as a better marker of mortality in critically ill category (sensitivity-66.7%, specificity- 62.5%), In wave three, AUC (0.45) with cut-off points of D-Dimer 302 ng/mL suggest it as a better marker of mortality in severe and critically ill category (sensitivity-90.2%, specificity-60.5%) (Table/Fig 8).
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Discussion |
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The COVID-19 pandemic had transformed into a worldwide disaster, marked by a significant incidence of complications, mortality, and even economic disruption. Accordingly, there is a critical need for affordable and feasible markers to streamline the diagnostic process and quantify the severity of the condition. The increased serum levels of CRP, LDH, D-dimer, and Ferritin at the time of admission within 24 hours in COVID-19 patients indicated of early ICU admission and categorisation of severity and requirement of an early treatment in order to avoid predicted mortality.
First retrospective study in India in which increased inflammatory markers such as serum levels of CRP, LDH, D-dimer, and Ferritin extensively analysed for age, sex mortality and survivor in clinical severity individually. As per data available more number of subjects were found in wave 1 compared to 2nd/3rd waves. The reason behind the difference between the two waves was not exactly known. The probable explanation suggests that a new variant of SARS-CoV-2 emerged in second and third wave with less virulence, better knowledge about disease state, early laboratory COVID-19 testing and availability of well equipped intensive care units with ventilators for critical severity, better treatment modality and finally initiation of vaccination campaign which provided immunity before third wave making COVID 19 infection less severe (21).
Comparing the age-wise percentage distribution of cases during both 1st, 2nd and 3rd the waves, in all three waves more or less same and it was attributed that the percentage distribution of median age of the cases during the first wave and second was above 50 to 55 years in severe and critically ill, while in the third waves in critically ill above 60 years which was in line with study of Jain VK et al., and Han Y et al., in their study suggested older subjects were infected in the first wave and younger population in the second (22),(23). Present study was also in line with Singh S et al., suggested as compared to the first and third waves, the cases during the second wave of the pandemic presented at a younger age group (24).
The SARS-CoV-2 virus could affect both genders, but many studies done across the world showed that older males were more (> 50%) susceptible. Probable mechanism for male susceptibility was that alteration in innate immunity due to hormonal response elements like putative Androgen Response Elements (AREs), Oestrogen Response Elements (ORE). Men probably smoke more frequently and drink more heavily than women, which was also linked to chronic diseases. Women also typically have better immune systems than men [24-28]. In present study in all three waves male subjects were more compared to females, finding were in line with above studies.
In the present study, inflammatory markers such as CRP, LDH, D-Dimer and Ferritin were found to be significantly higher in severe and critically ill patients in all three waves. This demonstrates that patients with severe/ critical illness who were hospitalised in the first wave had more severe disease compared with the other two waves.
Study conducted by Pan F et al., concluded that inflammatory parameter CRP >77.35 mg/L, LDH >481 U/L, and D-dimer >3.06 mg/L were increased in patients in severe and critically ill clinical category and associated with poor prognosis, multiorgan failure and death which was similar to the present study (29). Thus, elevated CRP was linked with both severity and mortality in COVID-19 patients, suggestive of prominent hyperinflamation. Serum ferritin was an acute-phase protein and LDH which could be used as a prognostic marker for tissue damage it was suggested that hyperferritinemia in COVID-19 patients was most likely due to the cytokine storm. Thus elevated ferritin levels had been associated not only to inflammation, but also to direct cellular damage and more organ dysfunction. Since LDH was expressed in lung tissue (isozyme 3), patients with severe COVID-19 infections could be predicted to release higher levels of LDH in the circulation, as a severe form of interstitial pneumonia (30).
Ceci FM et al., (31) in their study where results were analysed by ANOVA, data revealed that LDH, Ferritin, CRP, and D-dimer levels were markedly elevated within 24 hrs of admission in emergency department linked with development of a worst course similar results observed in present study. Tan C et al., the AUC scores suggest that elevations in a combination of blood parameters (LDH, MGB, CPR, Ferritin and D-dimer) at the emergency section level could lead to severe (ICU and/or death) health outcomes (32).
CRP had an AUC of 0.69, 0.77, and 0.764. LDH (0.63, 0.84, 0.85) and ferritin (0.67, 0.61) (Table/Fig 2),(Table/Fig 4),(Table/Fig 6),(Table/Fig 8) were about 0.7, which was similar to the study done by Mardani R et al., who reported an AUC of 0.8 for CRP and LDH, which was in line with the present study (33). The results suggested that CRP and LDH had the highest sensitivity and specificity in the critically ill and severe categories. A cut-off value of 301.5 ng/mL for ferritin in a two waves was close to the value (304 ng/mL) reported by Tular O et al., (34). For LDH, the cut-off value was found to be 204.5-293.5 IU/L; this value was similar to the result (277 mg/dL) reported by Li C et al., (35). For CRP, a cut-off value of 1.25 to 2.53 mg/dL was observed to be concomitant with a specificity of 0.77 and a sensitivity of 0.56, these findings were in line with those reported by Cheng B et al., (36).
Guan WJ et al., reported in their study that severe patients had a significantly higher level of D-dimer than non severe patients after analysing 1,099 patients with laboratory confirmed COVID-19 from over 550 hospitals in China (37). D-Dimer levels were significantly higher in the critically and severely ill. COVID-19 patients. It was found that elevated D-dimer at admission was a risk factor for the death of adult patients (38). Similar results were found in the study. The underlying mechanism was unknown in the relationship between elevated D-dimer and COVID-19 mortality. Thrombus formation and mortality were caused by a hyperinflammtory state with cytokine strom. Thus, if D-dimer measurements and monitoring were performed, COVID-19 mortality could be predicted.
More patients with COVID-19 were hospitalised in the first wave, with high mortality rate compared to a lower mortality rate, observed in the second and third wave. Contou D et al., (39), concluded that the difference in mortality between the first and other two waves was attributed to delay testing, a poor understanding about the infection, increased pathogenicity of COVID-19, a limitation of oxygen supply, Intensive Care Unit (ICU) admission, and needing mechanical ventilation failed non invasive respiratory support.
In the first wave, the treatment recommended was steroids as part of the recovery trial and increased length of hospital stay (39). Because steroids, remdesivir, and convalescent plasma were used substantially more frequently in the second and third wave than in the first, mortality in the second wave was lower than in the first. In the second wave, all our patients had already had steroids administered before ICU admission; in third wave of COVID-19 with the initiation of vaccination in India hospitalisation and mortality observed only in severe and critically ill. Mild moderate cases treated at outpatient basis.
To summarise hyperinflammatory state, cytokine strom and mortality in COVID-19 with laboratory biomarkers well correlated with elderly aged subjects, male gender, and higher mortality in wave one. The outcomes were worse in first wave compared to second wave, followed by third wave.
Limitation(s)
Study had few limitations, such as the serum CRP, LDH, Ferritin and D-Dimer level were measured within the first 24 hours of hospitalisation, length of stay, treatment modality and iimmunisation factors were not considered.
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Conclusion |
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Inflammatory markers elevated in all three waves, based on sensitivity and specificity CRP remained as good marker of severity and mortality in all three waves suggestive of hyper inflammatory state, LDH and ferritin marker of severity in wave two suggestive sudden surge of cytokine storm due to rapid transmission and multi organ damage. D-Dimer found to better marker of severity and mortality in wave one due to hypercoagulable state. Thus inflammatory parameters predict clinical severity and which guides the early treatment.
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| | 1. | WHO. WHO Director-General’s opening remarks at the media briefing on COVID-19 -11. (2020). Available online at: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19--11-march-2020
[ Google Scholar] | | 2. | Lew TW, Kwek TK, Tai D, Earnest A, Loo S, Singh K, et al. Acute respiratory distress syndrome in critically ill patients with severe acute respiratory syndrome. JAMA. 2003; 290:374-80. ?doi?https://doi.org/10.1001/jama.290.3.374#doi# ?pmid?12865379#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 3. | Mehta P, McCauley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ, et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet. 2020; 395(10229):1033-34. ?doi?https://doi.org/10.1016/S0140-6736(20)30628-0#doi# ?pmid?32192578#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 4. | Tay MZ, Poh CM, Renia L, MacAry PA, Ng LFP. The trinity of COVID-19: immunity, inflammation and intervention. Nat Rev Immunol. 2020:1-12. ?doi?https://doi.org/10.1038/s41577-020-0311-8#doi# ?pmid?32346093#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 5. | Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 2020; 8(4):420-22. ?doi?https://doi.org/10.1016/S2213-2600(20)30076-X#doi# ?pmid?32085846#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 6. | Lee DW, Gardner R, Porter DL, Louis CU, Ahmed N, Jensen M, et.al. Current concepts in the diagnosis and management of cytokine release syndrome. Blood. 2014;124(2):188-95. ?doi?https://doi.org/10.1182/blood-2014-05-552729#doi# ?pmid?24876563#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 7. | Kermali M, Khalsa RK, Pillai K, Ismail Z, Harky A. The role of biomarkers in diagnosis of COVID-19 - A systematic review. Life Sci. 2020 Aug 1;254:117788. doi: 10.1016/j.lfs.2020.117788. ?doi?https://doi.org/10.1016/j.lfs.2020.117788#doi# ?pmid?32475810#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 8. | World Health Organization. (2020). Use of chest imaging in COVID-19: a rapid advice guide, 11 June 2020. World Health Organization. https://apps.who.int/iris/handle/10665/332336
[ Google Scholar] | | 9. | Andrews MA, Areekal B, Rajesh KR, Krishnan J, Suryakala R. “First confirmed case of COVID-19 infection in India: A case report”. Indian Journal of Medical Research. 2020; 151 (5): 490-92.?doi?https://doi.org/10.4103/ijmr.IJMR_2131_20#doi# ?pmid?32611918#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 10. | Michael Safi (21 April 2021). “India’s shocking surge in Covid cases follows baffling decline”. The Guardian. Retrieved 29 April 2021 URL: https://www.theguardian. com/world/2021/apr/21/india-shocking-surge-in-covid-cases-follows-baffling-decline.
[ Google Scholar] | | 11. | The Indian Express. Third Covid wave inevitable, didn’t foresee current ferocity:
[ Google Scholar] | | 12. | Scientific Advisor to PM. [accessed on May 6, 2021]. Available from: https:// indianexpress.com/article/coronavirus/india-coronavirus-pandemicthird-wave-hospitals-oxygen-shortage-7303234/
[ Google Scholar] | | 13. | Asghar MS, Yasmin F, Haris A, Nadeem A, Taweesedt PT, Surani S, et al. Comparison of first and second waves of COVID-19 through severity markers in ICU patients of a developing country. J Community Hosp Intern Med Perspect. 2021;11(5):576-84. ?doi?https://doi.org/10.1080/20009666.2021.1949793#doi# ?pmid?34567444#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 14. | Ziuzia-Januszewska L, Januszewski M, Sosnowska-Nowak J, Janiszewski M, Dobrzy ´nski P, Jakimiuk AA, et al. COVID-19 Severity and Mortality in Two Pandemic Waves in Poland and Predictors of Poor Outcomes of SARS-CoV-2 Infection in Hospitalized Young Adults. Viruses. 2022;14:1700. ?doi?https://doi.org/10.3390/v14081700#doi# ?pmid?36016322#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 15. | Gorgojo-Galindo Ó, Martín-Fernández M, Peñarrubia-Ponce MJ, Álvarez FJ, Ortega-Loubon C, Gonzalo-Benito H, et al. Predictive Modeling of Poor Outcome in Severe COVID-19: A Single-Center Observational Study Based on Clinical, Cytokine and Laboratory Profiles. J. Clin. Med. 2021;10(22):5431. ?doi?https://doi.org/10.3390/jcm10225431#doi# ?pmid?34830714#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 16. | Garrafa E, Vezzoli, M, Ravanelli M, Farina D, Borghesi A, Calza S, et al. Early prediction of in-hospital death of COVID-19 patients: A machine-learning model based on age, blood analyses, and chest X-ray score. eLife. 2021;10:70640. ?doi?https://doi.org/10.7554/eLife.70640#doi# ?pmid?34661530#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 17. | Zheng D, Andonian D, Wojcik S. Identifying possible surrogate markers of COVID19 as a supplement to diagnostic testing in Upstate New York. Am. J. Emerg. Med. 2021;50:625-30. ?doi?https://doi.org/10.1016/j.ajem.2021.09.036#doi# ?pmid?34879477#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 18. | Avendaño-Ortiz J, Lozano-Rodríguez R, Martín-Quirós A, Terrón V, Maroun-Eid C. The immune checkpoints storm in COVID-19: Role as severity markers at emergency department admission. Clin. Transl. Med. 2021;11(10):573. ?doi?https://doi.org/10.1002/ctm2.573#doi# ?pmid?34709745#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 19. | World Health Organization. Coronavirus disease (COVID-19) outbreak, https:// www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (accessed 9 March 2021).
[ Google Scholar] | | 20. | Centers for Disease Control and Prevention. Underlying medical conditions associated with higher risk for severe COVID-19: information for healthcare professionals. 2022. Available at: https://www.cdc.gov/coronavirus/2019-ncov/ hcp/clinical-care/underlyingconditions.html. Accessed September 16, 2022
[ Google Scholar] | | 21. | Huang I, Pranata R, Lim MA, Oehadian A, Alisjahbana B. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis. Ther Adv Respir Dis. 2020;14:175346662093717521 November-December 1995 https://doi.org/10.1111/j.1537-2995.1995. tb03571 ?doi?https://doi.org/10.1177/1753466620937175#doi# ?pmid?32615866#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 22. | Outer S. Hoogenboom, Antoine Pham, Harnadar Anand, Roman Fleysher, Alexandra Buczek, et al. First and second COVID-19 waves in Japan: a comparison of disease severity and characteristics J Infect. 2021;82(4): 84-23.
[ Google Scholar] | | 23. | Jain VK, Iyengar KP, Vaishya R Differences between first wave and second wave of COVID-19 in India Diabetes Metab Syndr. 2021;15(3):1047-48. ?doi?https://doi.org/10.1016/j.dsx.2021.05.009#doi# ?pmid?33992554#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 24. | Han Y, Luo Z, Zhai W, Zheng Y, Liu H, Wang Y, et al. Comparison of the clinical manifestations between different age groups of patients with overseas imported COVID-19. PLoS ONE. 2020; 15(12): e0243347 ?doi?https://doi.org/10.1371/journal.pone.0243347#doi# ?pmid?33275609#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 25. | Singh S, Sharma A, Gupta A, Joshi M, Aggarwal A, Soni N, et al. Demographic comparison of the first, second and third waves of COVID 19 in a tertiary care hospital at Jaipur, India. Lung India. 2022;39(6):525-31. ?doi?https://doi.org/10.4103/lungindia.lungindia_265_22#doi# ?pmid?36629231#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 26. | Y Wenham C, Smith J, Morgan R. Gender and COVID-19 Working Group. COVID- 19: the gendered impacts of the outbreak. Lancet. 2020; 395(10227):846-48. ?doi?https://doi.org/10.1016/S0140-6736(20)30526-2#doi# ?pmid?32151325#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 27. | BMJ Global Health Sex, gender and COVID-19:Disaggregated data and health disparities Available from: https://blogs.bmj.com/bmjgh/2020/03/24/sex-gender-and-covid-19-disaggregated-data-and-health-disparities/ Last accessed on 2021 May 22.
[ Google Scholar] | | 28. | Vahidy FS, Pan AP, Ahnstedt H, Munshi Y, Choi HA, Tiruneh Y, et al. Sex differences in susceptibility, severity, and outcomes of coronavirus disease 2019: Cross-sectional analysis from a diverse US metropolitan area. PLoS One. 2021;16(1):e0245556. ?doi?https://doi.org/10.1371/journal.pone.0245556#doi# ?pmid?33439908#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 29. | Pan F, Yang L, Li Y, Liang B, Li L, Ye T, et al. Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): a case-control study. Int J Med Sci. 2020;17(9):1281-92. ?doi?https://doi.org/10.7150/ijms.46614#doi# ?pmid?32547323#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 30. | Jin JM, Bai P, He W, Wu F, Liu XF, Han DM, et al. Gender differences in patients with COVID 19: Focus on severity and mortality. Front Public Health 2020; 8:152. ?doi?https://doi.org/10.3389/fpubh.2020.00152#doi# ?pmid?32411652#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 31. | Ceci FM, Fiore M, Gavaruzzi F, Angeloni A, Lucarelli M, Scagnolari C, et al. Early Routine Biomarkers of SARS-CoV-2 Morbidity and Mortality: Outcomes from an Emergency Section. Diagnostics. 2022;12(1):176. ?doi?https://doi.org/10.3390/diagnostics12010176#doi# ?pmid?35054342#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 32. | Tan C, Huang Y, Shi F, Tan K, Ma Q, Chen Y, et al. C-reactive protein correlates with computed tomographic fifindings and predicts severe COVID-19 early. J. Med Virol. 2020;92(7):856-62. ?doi?https://doi.org/10.1002/jmv.25871#doi# ?pmid?32281668#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 33. | Mardani R, Vasmehjani AA, Zali F, Gholami A, Nasab SDM, Kaghazian H, et al. Laboratory Parameters in Detection of COVID-19 Patients with Positive RT-PCR; a Diagnostic Accuracy Study. Arch Acad Emerg Med. 2020;8(1):e43.
[ Google Scholar] | | 34. | Tural Onur S, Altin S, Sokucu SN, Fikri BI, Barça T, Bolat E, et al. Could ferritin level be an indicator of COVID-19 disease mortality? J Med Virol. 2021;93(3):1672-77. ?doi?https://doi.org/10.1002/jmv.26543#doi# ?pmid?32965712#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 35. | Li C, Ye J, Chen Q, Hu W, Wang L, Fan Y, Lu Z, et al. Elevated Lactate Dehydrogenase (LDH) level as an independent risk factor for the severity and mortality of COVID-19. Aging (Albany NY) 2020 Aug 14;12(15):15670-681. ?doi?https://doi.org/10.18632/aging.103770#doi# ?pmid?32805722#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 36. | Cheng B, Hu J, Zuo X, Chen J, Li X, Chen Y, et al. Predictors of progression from moderate to severe coronavirus disease 2019: a retrospective cohort. Clin Microbiol Infect. 2020;26(10):1400-05. ?doi?https://doi.org/10.1016/j.cmi.2020.06.033#doi# ?pmid?32622952#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 37. | Guan WJ, Ni ZY, Hu Y. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708-20. ?doi?https://doi.org/10.1056/NEJMoa2002032#doi# ?pmid?32109013#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 38. | 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(10229):1054-62. ?doi?https://doi.org/10.1016/S0140-6736(20)30566-3#doi# ?pmid?32171076#pmid#
[ Google Scholar] [ CrossRef] [ PubMed] | | 39. | Contou D, Fraissé M, Pajot O, Tirolien JA, Mentec H, Plantefève G. Comparison between first and second wave among critically ill COVID-19 patients admitted to a French ICU: no prognostic improvement during the second wave? Crit Care. 2021 Jan 4;25(1):3. doi: 10.1186/s13054-020-03449-6.?doi?https://doi.org/10.1186/s13054-020-03449-6#doi# ?pmid?33397421#pmid# [ Google Scholar] [ CrossRef] [ PubMed]
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