Predicting intracranial involvement: Unveiling perineural spread in COVID-19-associated mucormycosis, a novel phenomenon

Abstract

Abstract This study aimed to investigate the risk factors associated with intracranial involvement in COVID-19-associated mucormycosis (CAM) and to develop a nomogram model for predicting the risk of intracranial involvement, with a specific focus on perineural spread. An ambispective analysis was conducted on 275 CAM patients who received comprehensive treatment. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors, and a nomogram was created based on the results of the multivariable analysis. The performance of the nomogram was evaluated using a receiver operating characteristic (ROC) curve, and the discriminatory capacity was assessed using the area under the curve (AUC). The model's calibration was assessed through a calibration curve and the Hosmer Lemeshow test. In the results, the multivariable logistic regression analysis revealed that age (OR: 1.23, 95% CI 1.06–3.79), HbA1c (OR: 7.168, 95% CI 1.724–25.788), perineural spread (OR: 6.3, 95% CI 1.281–19.874), and the disease stage were independent risk factors for intracranial involvement in CAM. The developed nomogram demonstrated good discriminative capacity with an AUC of 0.821 (95% CI 0.713–0.909) as indicated by the ROC curve. The calibration curve showed that the nomogram was well-calibrated, and the Hosmer Lemeshow test yielded a P-value of 0.992, indicating a good fit for the model. In conclusion, this study found that CAM particularly exhibits perineural spread, which is a predictive factor for intracranial involvement. A nomogram model incorporating age, HbA1c, disease stage, and perineural spread was successfully developed for predicting intracranial involvement in CAM patients in both in-patient and out-patient settings.