Early Biochemical Markers in Predicting the Clinical Outcome of COVID-19 Patients Admitted in Tertiary Care Hospital

Prasad, Shrishtidhar; Patel, Suprava; Behera, Ajoy Kumar; Naik, Gitismita; Shah, Seema; Nanda, Rachita; Mohapatra, Eli

doi:10.1055/s-0042-1742631

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…These findings echo patterns seen in other states of India, such as Delhi, Bengaluru, and Madhya Pradesh, where early fatalities after hospital admission have been observed [ 19 - 21 ]. Supporting evidence from a study by Prasad et al (2022) in a tertiary care hospital in Chhattisgarh indicates that nearly 46% of non-survivors passed away within five days of admission [ 22 ]. These findings collectively underscore the critical importance of early detection, prompt medical attention, and appropriate management protocols for COVID-19 patients, especially during the initial days of hospitalization.…”

Section: Discussionmentioning

confidence: 91%

An Analysis of COVID-19 Mortality in Chhattisgarh: A Two-Year Study on Demographics, Trends, and Impacts

Agrawal¹,

Gahwai²,

Dayama³

et al. 2023

Cureus

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Background The COVID-19 pandemic, caused by the novel coronavirus, has had a profound impact on global health, significantly affecting demographics worldwide. As India's twelfth most affected state, Chhattisgarh has experienced substantial COVID-19-related fatalities. This study aims to analyze the temporal, geographical, and demographic distribution and trends in COVID-19 mortality reported by the Department of Health and Family Welfare, Chhattisgarh, spanning from the onset of the pandemic until March 2022 (two years). Methods Data about all COVID-19 deaths recorded between March 2020 and March 2022 were collected from the State Surveillance Unit, Department of Health and Family Welfare, Chhattisgarh, and subsequently compiled in a Microsoft Excel sheet (Microsoft, Redmond, Washinton) for analysis. Results A comprehensive dataset of 14,038 deaths was examined during the study period. Of these, 24.5% (3446), 72.2% (10141), and 3.3% (451) occurred in 2020, 2021, and 2022, respectively. The top five districts in Chhattisgarh with the highest COVID-19 mortality rates were identified as follows: 1) Raipur (23.5%), 2) Durg (13.4%), 3) Bilaspur (8.9%), 4) Raigarh (7.05%), and 5) Janjgir Champa (6.25%). The mean age of the deceased individuals was determined to be 55.44 years, with a standard deviation of 15.14 years. Furthermore, the impact of the pandemic was found to affect males compared to females in Chhattisgarh disproportionately. Conclusion Over the two-year study period, three distinct waves of COVID-19 were observed, with the second wave being the most devastating, particularly for the elderly population. Understanding the demographic characteristics and trends in COVID-19 mortality is crucial for implementing targeted public health measures and interventions to mitigate the impact of future infectious disease outbreaks.

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Section: Discussionmentioning

confidence: 91%

An Analysis of COVID-19 Mortality in Chhattisgarh: A Two-Year Study on Demographics, Trends, and Impacts

Agrawal¹,

Gahwai²,

Dayama³

et al. 2023

Cureus

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“…[ 44 45 46 ] In a study done in the tertiary care hospital in Chhattisgarh by Prasad S et al , (2022), nearly 46% of non-survivors succumbed within five days of admission. [ 47 ]…”

Section: Discussionmentioning

confidence: 99%

The impact of Covid-19 in Chhattisgarh, India: A descriptive study

Agrawal,

Galhotra,

Gahwai

et al. 2024

Journal of Family Medicine and Primary Care

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Background: India is among the most severely affected countries in the ongoing Covid-19 pandemic, second to the USA. Until July 2022, there were 43 million confirmed Covid-19 cases, with 0.53 million Covid-19 deaths in India. Chhattisgarh, the twelfth-highest affected state by Covid-19 disease, has had an enormous mortality toll, with 14,046 deaths by 15th July 2022. This study is a descriptive analysis of the Covid-19 deaths across Chhattisgarh from September 2021 to February 2022 (the third pandemic wave). Materials and Methods: All Covid-19 deaths that occurred between September 2021 to February 2022 were line-listed, and family members of all who died in the hospital were interviewed. Results: 356 Covid-19 deaths were included in the present study. The top three districts which observed maximum deaths (63.48%) were Raipur (27.53%), Durg (15.45%), and Bilaspur (7.87%). Around 50% of the deceased were of age 65 years or above. The male deaths were twice as compared to the female deaths. The RT-PCR was the most used testing method, followed by RAT. Most of the admissions (58%) were in a government facility. Among the deceased, 60% had one or two associated comorbidities. Hypertension and Diabetes were the most common comorbidity identified. Almost 90% of patients were symptomatic. Breathlessness was the most common symptom. Half of the deceased were not vaccinated. The median expenditure on Covid-19 management was ₹ 50,000 (€ 565.13). Conclusion: The deaths in the study were urban-centric. Higher Covid-19 mortality was observed in unvaccinated older men (≥65 years) with one or more comorbidity.

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“…(A) CAR values in non-survivor vs. survivor COVID-19 patients[20,21,23,26,27,[29][30][31][32]34,[43][44][45][46][47]50]. (B) Subgroup analysis according to country of origin between non-survivor vs. survivor COVID-19 patients [20,21,23,26,27,[29][30][31][32]34,[43][44][45][46][47]50[21,23,27,30,31].…”

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confidence: 99%

C-Reactive Protein-to-Albumin Ratio and Clinical Outcomes in COVID-19 Patients: A Systematic Review and Meta-Analysis

et al. 2022

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C-reactive protein-to-albumin ratio (CAR) is an independent risk factor in cardiovascular, cerebrovascular, and infectious diseases. Through this study, we investigated the CAR values with respect to the severity and mortality of COVID-19 patients. We performed a systematic review and meta-analysis to retrieve studies that evaluated CAR values upon hospital admission in relation to the severity or mortality of COVID-19 patients. We adopted a random-effect model to calculate the pooled mean difference (MD) and their 95% confidence intervals (CI). Quality assessment was appraised using a Newcastle–Ottawa scale and publication bias was assessed using the Begg-test and funnel plot. We equally performed a subgroup analysis using study location and a sensitivity analysis only with studies with low risk of bias. We analyzed 32 studies (n = 12445). Severe COVID-19 patients had higher on-admission CAR values than non-severe COVID-19 patients (MD: 1.69; 95% CI: 1.35–2.03; p < 0.001; I2 = 89%). Non-survivor patients with COVID-19 had higher CAR values than survivor patients (MD: 2.59; 95% CI: 1.95–3.23; p < 0.001; I2 = 92%). In sensitivity analysis, the relationship remained with a decreasing of heterogeneity for severity (MD: 1.22; 95% CI: 1.03–1.40; p < 0.001; I2 = 13%) and for mortality (MD: 2.99; 95% CI: 2.47–3.51; p < 0.001; I2 = 0%). High CAR values were found in COVID-19 patients who developed severe disease or died.

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Early Biochemical Markers in Predicting the Clinical Outcome of COVID-19 Patients Admitted in Tertiary Care Hospital

Cited by 5 publications

References 29 publications

An Analysis of COVID-19 Mortality in Chhattisgarh: A Two-Year Study on Demographics, Trends, and Impacts

An Analysis of COVID-19 Mortality in Chhattisgarh: A Two-Year Study on Demographics, Trends, and Impacts

The impact of Covid-19 in Chhattisgarh, India: A descriptive study

C-Reactive Protein-to-Albumin Ratio and Clinical Outcomes in COVID-19 Patients: A Systematic Review and Meta-Analysis

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