Evidence map›Paper›PMID 34953910›Full record

ArticleThe Journal of infection2022

Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India.

Shabbir Syed-Abdul, A Shoban Babu, Raja Shekhar Bellamkonda, Ramaiah Itumalla, Gvrk Acharyulu, Surya Krishnamurthy, Y Venkat Santosh Ramana, Naresh Mogilicharla, Shwetambara Malwade, Yu-Chuan Li

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of infection, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 12 citations in OpenAlex.

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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

10 authors at 5 institutions in 3 countries.

Shabbir Syed-AbdulGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 5F., No. 172-1, Sec. 2, Keelung Rd. Da'an, Taipei 106, Taiwan; International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; School of Gerontology and Health Management, College of Nursing, Taipei Medical University, Taipei, Taiwan.
A Shoban BabuDepartment of ENT, Gandhi Medical College and Hospital, Musheerabad, Secunderabad, Telangana 500003, India. Electronic address: shobhanaachi@gmail.com.
Raja Shekhar BellamkondaSchool of Management Studies, University of Hyderabad, Hyderabad, Telangana, India.
Ramaiah ItumallaDepartment of Health Management, College of Public Health and Health Informatics, University of Hail, Hail, Saudi Arabia.
Gvrk AcharyuluSchool of Management Studies, University of Hyderabad, Hyderabad, Telangana, India.
Surya KrishnamurthyData Scientist, iQGateway, Bengaluru, Karnataka, India.
Y Venkat Santosh RamanaDepartment of Hospital Administration, Gandhi Hospital, Secunderabad, Telangana, India.
Naresh MogilicharlaDepartment of ENT, Gandhi Medical College and Hospital, Musheerabad, Secunderabad, Telangana 500003, India.
Shwetambara MalwadeInternational Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Yu-Chuan LiGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 5F., No. 172-1, Sec. 2, Keelung Rd. Da'an, Taipei 106, Taiwan; International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. Electronic address: jack@tmu.edu.tw.
Taipei Medical University · TWGandhi Medical College & Hospital · INUniversity of Hyderabad · INUniversity of Ha'il · SAYashoda Hospital · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIndia reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital.

methodsThe dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error.

resultsThe logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis.

conclusionThe developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.

Indexed as

COVID-19MucormycosisArtificial IntelligenceHospitals, PublicHumansIndiaSARS-CoV-2SurvivorsArtificial intelligenceCoronavirusCOVID-19Fungal infectionIndiaMucormycosis

Identifiers

PMID34953910
PMCPMC8694789
OpenAlexW4200574883

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.