Evidence map›Paper›PMID 39749096›Full record

ArticleCureus2024

The Relationship Between Predisposing Risk Factors and COVID-19: An Observational Study.

Ramnivas Vishnoi, Manish Gaba, Naveen Kumar, Ankita Pandey, Arun Dewan

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ramnivas VishnoiInternal Medicine, Max Smart Super Speciality Hospital, New Delhi, IND.
Manish GabaInternal Medicine, Max Smart Super Speciality Hospital, New Delhi, IND.
Naveen KumarInternal Medicine, Max Smart Super Speciality Hospital, New Delhi, IND.
Ankita PandeyInternal Medicine, Max Smart Super Speciality Hospital, New Delhi, IND.
Arun DewanInternal Medicine, Max Smart Super Speciality Hospital, New Delhi, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Numerous risk factors have been identified for developing severe COVID-19, including sociodemographic variables and concomitant diseases. Individuals with underlying comorbidities such as diabetes, hypertension, asthma, and coronary artery disease are at a greater risk of severe illness and death. This study aimed to observe the association between risk factors and the severity of COVID-19. Methodology A single-center, hospital-based, prospective, observational study was conducted at Max Smart Super Speciality Hospital in Saket, Delhi from October 2020 to December 2021. A total of 1,454 patients admitted under our care in the Department of Internal Medicine were included in this study. Patients were divided into the following three groups: patients without comorbidities, patients with a single comorbidity, and patients with multiple comorbidities. The risk factors under evaluation were age >50 years, obesity, diabetes, hypertension, chronic kidney disease (CKD), heart disease, chronic liver disease (CLD), and immunocompromised status (human immunodeficiency virus, post-transplant, malignancy undergoing chemotherapy). Results In this study, 28.1% (n = 408) of patients did not have comorbidities, 30.1% (n = 438) of patients had a single comorbidity, and 41.8% (n = 608) of patients had multiple comorbidities. Regarding risk factors, 62% (n = 872) of patients were aged >50 years, 7.4% (n = 108) were obese, 30.7% (n = 447) had diabetes, 33% (n = 480) were hypertensive, 1.2% (n = 18) had CKD, 6.8% (n = 99) had heart disease, 0.3% (n = 4) had CLD, and 5.5% (n = 80) were immunocompromised. A statistically significant association was found between increasing age and worsening severity of COVID-19 (p = 0.0001), male gender (p = 0.0001), presence of comorbidities, including diabetes, hypertension, obesity, CKD, CLD, heart disease (p = 0.0001). Patients in the immunocompromised group did not have a statistically significant association with disease severity. A statistically significant association was found between mortality and severity of COVID-19. Overall, 16.7% (n = 48) of the patients in the no comorbidity group, 35.4% (n = 102) in the single comorbidity group, and 47.9% (n = 138) in the multiple comorbidity group (p = 0.0001) presented with severe disease on admission. Conclusions The study shows that the severity of the disease increased as the number of risk factors increased. This information can help us take early and active measures in these groups of patients with multiple comorbid illnesses.

Indexed as

comorbiditiescovid-19indiarisk factorsociodemographic variables

Identifiers

PMID39749096
PMCPMC11694842

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.