Evidence map›Paper›PMID 37208888›Full record

ArticleJNMA; journal of the Nepal Medical Association2023

Severe COVID-19 among Admitted COVID-19 Patients in a Tertiary Care Centre: A Descriptive Cross-sectional Study.

Sapana Sedhain, Amrita Sinha, Neeta Kafle, Dipak Kumar Thakur

Abstract read
In one paragraph

Article in JNMA; journal of the Nepal Medical Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

4 authors.

Sapana SedhainDepartment of Pathology, Birat Medical College Teaching Hospital, Biratnagar, Morang, Nepal.
Amrita SinhaDepartment of Pathology, Birat Medical College Teaching Hospital, Biratnagar, Morang, Nepal.
Neeta KafleDepartment of Pathology, Birat Medical College Teaching Hospital, Biratnagar, Morang, Nepal.
Dipak Kumar ThakurDepartment of Urology, Birat Medical College Teaching Hospital, Biratnagar, Morang, Nepal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Severe COVID-19 patients experience elevated levels of serologic indicators of inflammation which can alter blood cell lineages and cause lymphopenia. The objective of this study was to find out the prevalence of severe COVID-19 among admitted COVID-19 patients in a tertiary care centre. Methods: A descriptive cross-sectional study was conducted in a tertiary care centre from 22 June 2021 to 30 September 2021 after obtaining ethical approval from the Institutional Review Committee (Reference number: IRC-PA-146/2077-78). Confirmed COVID-19 patients by reverse transcriptase polymerase chain reaction and admitted in the COVID block during the study period were included and those who were discharged on request or referred or unavailable blood tests were excluded. A convenience sampling method was used. Point estimate and 95% Confidence Interval were calculated. Results: Among 72 admitted COVID-19 patients, 63 (87.5%) (79.86-95.14, 95% Confidence Interval) patients had severe disease. The mean neutrophil to lymphocyte ratio and mean lymphocyte to C-reactive protein ratio were 11.60±8.15 and 25.55±20.96 respectively. Conclusions: The prevalence of severe COVID-19 was higher than in other studies done in similar settings. We suggest clinical parameter-based early categorisation of COVID-19 cases to utilize limited resources during the pandemic. Keywords: COVID-19; c-reactive protein; lymphocytes; severe acute respiratory syndrome coronavirus.

Indexed as

COVID-19C-Reactive ProteinCross-Sectional StudiesHospitalizationHumansTertiary Care CentersC-Reactive Protein

Identifiers

PMID37208888
PMCPMC10089021

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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.