Evidence map›Paper›PMID 35027793›Full record

ArticleIndian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine2021

Comparison of Clinical Characteristics and Outcome of Critically Ill Patients Admitted to Tertiary Care Intensive Care Units in India during the Peak Months of First and Second Waves of COVID-19 Pandemic: A Retrospective Analysis.

Sukhyanti Kerai, Rahil Singh, Shanta Dutta, Aayushi Mahajan, Munisha Agarwal

Open access · diamondAbstract read
In one paragraph

Article in Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 26% of its field
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

7 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Analysis of Predictors and Outcomes of COVID-19 Patients Requiring ICU Admission from COVID-19 Registry, India.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2023
    Article
  6. Review
  7. Is It a Wave or a Tsunami? That is the Question.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2021
    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

5 authors at 1 institution in 1 country.

Sukhyanti KeraiDepartment of Anaesthesiology and Critical Care, Maulana Azad Medical College, New Delhi, India.ORCID https://orcid.org/0000-0002-7771-0786
Rahil SinghDepartment of Anaesthesiology and Critical Care, Maulana Azad Medical College, New Delhi, India.ORCID https://orcid.org/0000-0002-3941-1398
Shanta DuttaDepartment of Anaesthesiology and Intensive Care, Maulana Azad Medical College, New Delhi, India.ORCID https://orcid.org/0000-0002-6371-9595
Aayushi MahajanDepartment of Anaesthesiology and Intensive Care, Maulana Azad Medical College, New Delhi, India.ORCID https://orcid.org/0000-0002-5903-1157
Munisha AgarwalDepartment of Anaesthesiology and Intensive Care, Maulana Azad Medical College, New Delhi, India.ORCID https://orcid.org/0000-0003-2958-6810
Maulana Azad Medical College · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronavirus disease-2019 (COVID-19) continues to pose serious challenges to healthcare systems globally with the disease progressing over time in crest-trough pattern of waves. We compared the patient characteristics and outcomes of critically ill patients admitted during the first and second waves of COVID-19 pandemic. MATERIALS AND

methodsWe did a retrospective analysis of medical records of critically ill patients admitted to intensive care unit (ICU) at the peak period of both waves. The data on demographics, symptoms, treatment received, and outcomes of patients were recorded.

resultsCompared to first wave, significantly more females, younger age group, and those without underlying comorbidities required ICU admission during the second wave. The treatments received during both periods were similar except for preferential use of methylprednisolone over dexamethasone and proclivity of bilevel positive airway pressure (BiPAP) ventilation over high-flow nasal cannula (HFNC). There was no significant difference in the duration of ICU stay and mortality of patients. During the first wave, the factors associated with nonsurvival of patients were advanced age, comorbidities, severe disease, and a lesser number of days on HFNC. All these factors along with higher Sequential Organ Failure Assessment (SOFA) score were observed to be linked with patient nonsurvival during the second wave.

conclusionIn India, the second wave of COVID-19 significantly influenced ICU demographics with a predominance of females and young adults requiring critical care. During both time periods, patients received similar treatment except for the propensity to use methylprednisolone and BiPAP as opposed to dexamethasone and HFNC in second wave. No significant difference in ICU mortality was noted. HOW TO CITE THIS ARTICLE: Kerai S, Singh R, Dutta S, Mahajan A, Agarwal M. Comparison of Clinical Characteristics and Outcome of Critically Ill Patients Admitted to Tertiary Care Intensive Care Units in India during the Peak Months of First and Second Waves of COVID-19 Pandemic: A Retrospective Analysis. Indian J Crit Care Med 2021;25(12):1349-1356.

Indexed as

Coronavirus disease-2019Coronavirus disease-2019 in IndiaCoronavirus disease-2019 intensive care unitMortalityPredictorsSeverity

Identifiers

PMID35027793
PMCPMC8693101
OpenAlexW3216389666

What OpenQuestion holds

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LicenceCC BY-NC
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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.