Evidence map›Paper›PMID 38947593›Full record

ArticleCureus2024

Exploring the Clinical Features, Management of Hypertension, and Predictors of Severity in Hospitalized Hypertensive COVID-19 Patients.

Narendar Kumar, Syed Azhar Syed Sulaiman, Furqan K Hashmi, Ahmed Noor, Rabbiya Ahmad, Ali Qureshi, Faheem Jhatial, Siti Maisharah Sheikh Ghadzi

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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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

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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

8 authors.

Narendar KumarClinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, MYS.
Syed Azhar Syed SulaimanClinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, MYS.
Furqan K HashmiPharmacy, University of the Punjab, Lahore, PAK.
Ahmed NoorCardiology, Indus Hospital & Health Network, Karachi, PAK.
Rabbiya AhmadClinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, MYS.
Ali QureshiClinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, MYS.
Faheem JhatialPharmacy Services, Indus Hospital & Health Network, Karachi, PAK.
Siti Maisharah Sheikh GhadziClinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, MYS.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Hypertension significantly contributes to the severity, prolonged hospitalization, the need for intensive care, and mortality of COVID-19 patients. However, the data is still evolving. This study investigated the predictors of severity among hypertensive COVID-19 patients. Methodology This cohort study included 333 hospitalized hypertensive COVID-19 patients at the Indus Hospital, Karachi, Pakistan, from April 2021 to October 2021. The study evaluated the clinical features, antihypertensive therapy, and predictors of severity. A multivariable binary logistic regression model was used to determine severity predictors using IBM SPSS Statistics for Windows, Version 27.0 (Released 2020; IBM Corp., Armonk, NY, USA). Results The majority of hypertensive COVID-19 patients were females (54.7%), aged <65 years (55.8%), and coexisted with diabetes mellitus (56.5%). The independent predictors of severity were male (aOR 2.65, 95% CI, 1.08-6.51; p < 0.033), fever (aOR 3.52, 95% CI, 1.24-9.92; p = 0.017), shortness of breath (aOR 4.49, 95% CI, 1.73-11.63; p = 0.002), oxygen saturation (<90%) (aOR 87.39, 95% CI, 19.15-398.75; p < 0.001), and D-dimer (>0.5 mcg/ml) (aOR 3.03, 95% CI, 1.19-7.71; p = 0.020). Conclusions Our study concluded that males with fever before admission, shortness of breath, lower oxygen saturation, and elevated D-dimer are the predictors of severity among hypertensive COVID-19 patients.

Indexed as

clinical featurecovid-19hypertensionsars-cov-2severity

Identifiers

PMID38947593
PMCPMC11214652

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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.