Evidence map›Paper›PMID 35233327›Full record

ArticleCureus2022

Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence.

Pranav Ajmera, Amit Kharat, Satvik Dhirawani, Sanjay M Khaladkar, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare, Snehal Rathi

Abstract read
In one paragraph

Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Pranav AjmeraRadiology, Dr. DY Patil Medical College, Hospital and Research Centre, Pune, IND.
Amit KharatMusculoskeletal Radiology, Dr. DY Patil Medical College, Hospital and Research Centre, Pune, IND.
Satvik DhirawaniRadiology, Dr. DY Patil Medical College, Hospital and Research Centre, Pune, IND.
Sanjay M KhaladkarRadiodiagnosis, Dr. DY Patil Medical College, Hospital and Research Centre, Pune, IND.
Viraj KulkarniData Science, DeepTek Medical Imaging Pvt. Ltd., Pune, IND.
Vinay DuddalwarRadiology and Biomedical Imaging, Keck School of Medicine, University of Southern California, Los Angeles, USA.
Purnachandra LamghareRadiology, Dr. DY Patil Medical College, Hospital and Research Centre, Pune, IND.
Snehal RathiRadiology, Mahatma Gandhi Mission Institute of Health Sciences, Mumbai, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS). Methodology This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis. Results The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis. Conclusions This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.

Indexed as

artificial intelligencecovid-19ct chestimagingradiology

Identifiers

PMID35233327
PMCPMC8881892

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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.