Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India. Issue 3 (March 2022)

• Record Type: Journal Article
• Authors: Syed-Abdul, Shabbir; Babu, A. Shoban; Bellamkonda, Raja Shekhar; Itumalla, Ramaiah; Acharyulu, GVRK; Krishnamurthy, Surya; Ramana, Y. Venkat Santosh; Mogilicharla, Naresh; Malwade, Shwetambara; Li, Yu-Chuan
• Is Part Of: Journal of infection. Volume 84:Issue 3(2022)
• Page Start: 351
• Page End: 354
• Publication Date: 2022-03
• Subjects: Artificial intelligence -- Coronavirus -- COVID-19 -- Fungal infection -- India -- Mucormycosis; Infection -- Periodicals; Bacterial Infections -- Periodicals; Communicable Diseases -- Periodicals; Electronic journals; 616.905
• DOI: 10.1016/j.jinf.2021.12.016
• Languages: English
• ISSNs: 0163-4453

Abstract

Highlights: Mucormycosis is a rare but a life threatening fungal infection. India has been facing a public health challenge from COVID-19 related mucormycosis. Artificial intelligence based models show potential to predict high risk patients. Early detection can aid better management of COVID-19 related mucormycosis. Summary: Introduction: India 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. Methods: The 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. Results: The 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. Conclusion: The 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.