ArticleCureus2023
Role of Hematological Parameters in the Grading of COVID-19 and a Model to Predict the Outcome in Inpatients.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Human coronaviruses, identified in the 1960s, are known culprits of respiratory infections. Classified into alpha, beta, gamma, and delta subgroups, these viruses have the capacity to transition from animal reservoirs to causing severe respiratory ailments in humans. Notable outbreaks like the 2003 severe acute respiratory distress syndrome (SARS) epidemic and the ongoing coronavirus disease 2019 (COVID-19) pandemic underscore the recurring emergence of novel coronaviruses with severe human infection potential. COVID-19, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly become a leading global cause of severe acute respiratory syndrome. Immune system disruptions and cytokine imbalances contribute to severe cases, necessitating early diagnosis and precise severity assessment. Methodology This retrospective cross-sectional study encompassed 211 COVID-19 patients admitted to AIIMS Patna from May to July 2020. Clinical and hematological parameters, including neutrophils, eosinophils, basophils, lymphocytes, monocytes, red and white blood cell counts, platelet count, C-reactive protein (CRP), serum ferritin, and d-dimer, were meticulously recorded. Patients were categorized into non-severe and severe groups using the National Early Warning Score (NEWS) 2. Results Our findings underscore the pivotal role of hematological markers in gauging COVID-19 severity. Notably, markers such as neutrophil-to-lymphocyte ratio (NLR), derived NLR, lymphocyte monocyte ratio, platelet lymphocyte ratio, d-dimer, CRP, and serum ferritin exhibited notable elevation in severe cases. Survival analysis further established the predictive potential of these markers in assessing disease progression and mortality risk. We advocate for the integration of these markers into existing severity assessment frameworks to foster objective clinical evaluations. Conclusion In conclusion, our study unravels the intricate connection between COVID-19 severity and hematological parameters. We emphasize the early warning capabilities of NLR, derived NLR, platelet lymphocyte ratio, and other markers in predicting disease progression. This research underscores the imperative need to incorporate hematological markers into the evaluation of COVID-19 severity, thereby providing invaluable insights for enhancing clinical practice and patient outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.