Evidence map›Paper›PMID 36313915›Full record

ReviewEJIFCC2022

Laboratory Features of Hospitalised Patients with COVID-19 in Jersey, UK.

Sergio Gama, Julie Bellamy, Nadia Couvert, Effie Liakopoulou

Abstract readReview
In one paragraph

Review in EJIFCC, 2022. 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
–field-weighted citation impact
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.

Sergio GamaDepartment of Blood Sciences, Jersey General Hospital, St. Helier, Jersey, UK.
Julie BellamyDepartment of Blood Sciences, Jersey General Hospital, St. Helier, Jersey, UK.
Nadia CouvertDepartment of Blood Sciences, Jersey General Hospital, St. Helier, Jersey, UK.
Effie LiakopoulouDepartment of Blood Sciences, Jersey General Hospital, St. Helier, Jersey, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is an acute respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To date, more than 550 million cases and 6 million deaths have been reported worldwide. This study investigated the laboratory features in hospitalised patients with COVID-19 and determined risk factors for in-hospital mortality. This retrospective observational study included laboratory results of confirmed cases of hospitalised patients with SARS-CoV-2 infection in Jersey (UK) between March-December 2020 (subject to inclusion criteria), and a control group. Furthermore, COVID-19 patients were split into two sub-groups, based on outcome (non-survivors vs. survivors). Logistic regression was used to determine risk factors for in-hospital mortality. A total of 81 COVID-19 cases and 100 controls were included in this study. In the COVID-19 group, 59.3% of subjects were male, and the overall mortality was 33.3%. The main laboratory changes were the following: 95.1% of patients presented with raised C-reactive protein (p<0.001), 85% showed increased fibrinogen (p<0.001), 70% had prolonged prothrombin time (p=0.014), 51.9% suffered from lymphopenia (p<0.001), 42% had elevated gamma glutamyl transferase (p=0.011) and 35.8% demonstrated raised creatinine concentration (p=0.002). Non-survivors were older than survivors (median age: 82 vs. 74 years, p=0.003) with substantial lymphopenia (p=0.018), high creatinine level (p=0.009), and leukocytosis (p=0.018). Increased in-hospital mortality risk was 6.7-fold in patients presenting with a lymphocyte count <0.85 x10

Indexed as

coronavirus 2 (SARS-CoV-2)COVID-19laboratory featuresmortality risksevere acute respiratory syndrome

Identifiers

PMID36313915
PMCPMC9562481

What OpenQuestion holds

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Registered trials

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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.