Evidence map›Paper›PMID 34975140›Full record

SynthesisIndian journal of pharmacology

COVID-19-associated rhino-orbital-cerebral mucormycosis: A systematic review, meta-analysis, and meta-regression analysis.

Anusuya Bhattacharyya, Phulen Sarma, Hardeep Kaur, Subodh Kumar, Jaimini Bhattacharyya, Manisha Prajapat, Ajay Prakash, Saurabh Sharma, Dibbanti Harikrishna Reddy, Prasad Thota and 3 more

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Indian journal of pharmacology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. COVID-19 associated mold infections: Review of COVID-19 associated pulmonary aspergillosis and mucormycosis.Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi · 2023
    Review
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  17. In Response to: Corticosteroids in Non-severe COVID-19: Finding Window of Opportunity.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2022
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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

13 authors.

Anusuya BhattacharyyaDepartment of Ophthalmology, Government Medical College and Hospital, sector 32, Chandigarh, India.
Phulen SarmaDepartment of Pharmacology, PGIMER, Chandigarh, India.
Hardeep KaurDepartment of Pharmacology, PGIMER, Chandigarh, India.
Subodh KumarDepartment of Pharmacology, PGIMER, Chandigarh, India.
Jaimini BhattacharyyaDepartment of Management Studies, IIT Madras, Chennai, Tamil Nadu, India.
Manisha PrajapatDepartment of Pharmacology, PGIMER, Chandigarh, India.
Ajay PrakashDepartment of Pharmacology, PGIMER, Chandigarh, India.
Saurabh SharmaDepartment of Pharmacology, PGIMER, Chandigarh, India.
Dibbanti Harikrishna ReddyDepartment of Pharmacology, Central University of Punjab, Bathinda, Punjab, India.
Prasad ThotaScientist, Indian Pharmacopoeia Commission, Ghaziabad, Uttar Pradesh, India.
Seema BansalDepartment of Pharmacology, PGIMER, Chandigarh, India.
Bhaswati Sharma GautamMicrobiology, B.P. Civil hospital, Nawgaon, Assam, India.
Bikash MedhiDepartment of Pharmacology, PGIMER, Chandigarh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTill now, no meta-analysis is available to address the clinical profile, risk factors, different interventions, and outcomes among COVID-19-associated rhino-orbito-cerebral mucormycosis (C-ROCM) cases. MATERIALS AND

methodsEight literature databases were screened using appropriate keywords from November 1, 2019, to June 30, 2021. The objectives were to analyze the clinical and microbiological profile, risk factor/comorbidity, intervention, and outcome. "R-metafor package" was used for analysis.

resultsA total of 23 studies were included. The mean age of presentation of C-ROCM was 54.6 years. The most common presentation was ptosis (72.7%), lid edema (60.6%), proptosis (60.6%), ophthalmoplegia (57.3%), loss of vision (53.7%), facial edema (34.7%), and nasal-blockage (11.8%). Evidence of intracranial spread was seen in 42.8% of cases. Rhizopus was the most common fungus (57.1%) isolated in fungal culture. Among C-ROCM patients, diabetes was the commonest comorbid condition, and the use of corticosteroids related to COVID-19 treatment was the most common risk factor (85.75%). Compared to controlled diabetics, C-ROCM was significantly higher among uncontrolled diabetics (odds ratio [OR] 0.15, 95% confidence interval [C.I.] 0.041-0.544, P = 0.0010). However, no significant association was seen between C-ROCM and COVID-19 severity (OR 0.930, 95% C.I. 0.212-4.087, P = 0.923). For treatment, amphotericin-B was the most common antifungal drug used which was followed by surgical options. However, mortality was high (prevalence 0.344, 95% C.I. 0.205-0.403) despite treatment.

conclusionAlthough local rhino-orbito symptoms were the first to appear, rapid intracranial extension was seen in a significant number of C-ROCM cases. Uncontrolled diabetes and excessive use of corticosteroid were the most common risk factors present among the C-ROCM cases. High index clinical suspicion is imperative (specifically among COVID-19 patients with diabetes), and routine screening may be helpful.

Indexed as

Amphotericin BAntifungal AgentsBrain DiseasesCOVID-19HumansMucormycosisNose DiseasesOrbital DiseasesRegression AnalysisRisk FactorsSARS-CoV-2Amphotericin BAntifungal AgentscerebralCOVID-19COVID-19–associated mucormycosismucormycosisorbitalrhinorhino-orbito-cerebral mucormycosisSARS CoV-2

Identifiers

PMID34975140
PMCPMC8764981

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.