Evidence map›Paper›PMID 35198321›Full record

ArticleCureus2022

Radiological Lung Sequelae of Severe COVID-19: A Retrospective Observational Study From a Dedicated COVID Centre of Eastern India.

Deependra Rai, Subhash Kumar, Sanjay Pandey, Harsh Vardhan

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

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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

2 citing papers in PubMed.

  1. Review
  2. Antifibrotic in interstitial lung diseases: When, where, and how long?Lung India : official organ of Indian Chest Society
    Article
4 · The record

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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.

Deependra RaiPulmonary Medicine, All India Institute of Medical Sciences Patna, Patna, IND.
Subhash KumarRadiodiagnosis, All India Institute of Medical Sciences Patna, Patna, IND.
Sanjay PandeyPhysical Medicine and Rehabilitation, All India Institute of Medical Sciences Patna, Patna, IND.
Harsh VardhanNephrology, All India Institute of Medical Sciences Patna, Patna, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe pulmonary sequelae of severe COVID-19 infection are yet to be fully defined. The authors undertook this study to find out the proportion of severe COVID-19 patients having fibrosis-like lung sequelae during a medium-term follow-up period. MATERIALS AND

methodsThis was a retrospective observational study from a dedicated COVID centre of Eastern India. Severe COVID-19 patients who had undergone chest computerized tomography (CT) during the acute phase of illness and at least one follow-up CT with a gap of minimum two months between the two scans were included in the study.

resultA total of 39 patients who had recovered from severe COVID-19 pneumonia and presented to the pulmonary medicine OPD in the months of July and August 2021 were included. Patients with pre-existing lung disease (n-4), mild to moderate (n-11), and due to unavailability of CT scan (n-2) were excluded. A total of 22 patients (thirteen males, nine females) were thus included for analysis. Follow-up scans were performed with a mean of 2.5 months after the onset of the disease. Out of 22 patients, only one patient's follow-up scan was normal. Predominant fibrotic-like features were present in six (27.2%) patients, though some evidence of fibrosis-like changes were seen in 20 out of 22 (90.9%) patients. The remaining 15 (68.2%) patients with abnormal scans had predominant non-fibrotic changes like ground-glass opacities (GGOs), consolidation, cavity, or nodule. The most common presenting symptoms at the follow-up examination were dyspnoea (81.8%), cough (54.1%) followed by fatigue in 40.9% of patients.

conclusionThis study concluded that most of the severe COVID-19 patients have some residual radiological findings during medium-term follow-up. Fibrotic-like lesions are present in almost all patients but most of them get resolved with time. True fibrotic features like honeycombing are rarely seen as residual lung sequelae.

Indexed as

covid-19 indiact scan chestpost-covid sequelaepulmonary fibrosis'severe acute respiratory syndrome coronavirus 2

Identifiers

PMID35198321
PMCPMC8855736

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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.