Development of a Machine Learning Model to Predict Risk of Development of COVID-19-Associated Mucormycosis

Abstract

Aim: The study aimed to identify quantitative parameters that increase the risk of rhino-orbito-cerebral mucormycosis, and subsequently developed a machine learning model that can anticipate susceptibility to developing this condition. Methods: Clinicopathological data from 124 patients were used to quantify their association with COVID-19-associated mucormycosis (CAM) and subsequently develop a machine learning model to predict its likelihood. Results: Diabetes mellitus, noninvasive ventilation and hypertension were found to have statistically significant associations with radiologically confirmed CAM cases. Conclusion: Machine learning models can be used to accurately predict the likelihood of development of CAM, and this methodology can be used in creating prediction algorithms of a wide variety of infections and complications.

Plain language summary

Fungal infections caused by the Mucorales order of fungi usually target patients with a weakened immune system. They are usually also associated with abnormal blood sugar states, such as in diabetic patients. Recent work during the COVID-19 outbreak suggested that excessive steroid use and diabetes may be behind the rise in fungal infections caused by Mucorales, known as mucormycosis, in India, but little work has been done to see whether we can predict the risk of mucormycosis. This study found that these fungal infections need not necessarily be caused by Mucorales’ species, but by a wide variety of fungi that target patients with weak immune systems. Secondly, we found that diabetes, breathing-assisting devices and high blood pressure states had associations with COVID-19-associated fungal infections. Finally, we were able to develop a machine learning model that showed high accuracy when predicting the risk of development of these fungal infections.

Keywords: :

• COVID-19-associated mucormycosis
• machine learning
• mucormycosis
• rhino-orbito-cerebral mucormycosis

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.tandfonline.com/doi/suppl/10.2217/fmb-2023-0190

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human experimental investigations. In addition, informed consent has been obtained from the participants involved.