โก Quick Summary
This study provides a comprehensive overview of the global epidemiology of nocardiosis, an often overlooked opportunistic infection, analyzing 9,750 reported cases and employing machine learning to develop a model that predicts mortality rates in affected patients. The findings reveal a 19.8% overall mortality rate, with higher risks associated with disseminated infections.
๐ Key Details
- ๐ Dataset: 9,750 reported cases of nocardiosis
- ๐ Regions: Predominantly North America and Asia
- โ๏ธ Technology: Nine machine learning algorithms, with a focus on stochastic gradient boosting (SGBT)
- ๐ Performance: SGBT outperformed other models in mortality prediction
๐ Key Takeaways
- ๐ Rising Incidence: The number of nocardiosis cases has significantly increased since 2000.
- ๐ Mortality Rates: Overall mortality rate stands at 19.8%, with disseminated infections at 31.7%.
- ๐จโโ๏ธ Risk Factors: Advanced age, male gender, and underlying diseases increase mortality risk.
- ๐ Machine Learning: The SGBT model effectively predicts mortality, aiding early identification of high-risk patients.
- ๐ Regional Variations: The distribution of Nocardia species varies significantly across different regions.
- ๐งช Clinical Implications: Findings can inform personalized treatment plans for patients with nocardiosis.
๐ Background
Nocardiosis is a rare but serious opportunistic infection caused by the Nocardia species, primarily affecting immunocompromised individuals. Despite its increasing incidence, it remains underdiagnosed and often misidentified, leading to significant morbidity and mortality. Understanding its epidemiology and risk factors is crucial for improving patient outcomes and guiding clinical practices.
๐๏ธ Study
This study conducted a thorough literature review using the PubMed and Web of Science databases, identifying 9,750 cases of nocardiosis reported up to August 2024. The researchers aimed to analyze the global epidemiology of the infection and develop a machine learning model to predict mortality rates among patients, utilizing various algorithms to enhance predictive accuracy.
๐ Results
The study found that the mean age of patients was 50.4 years, with a male predominance of 64.7%. The overall all-cause mortality rate was 19.8%, while disseminated infections were linked to a higher mortality rate of 31.7%. The SGBT model demonstrated superior performance in mortality prediction, effectively identifying high-risk patients across both training and test datasets.
๐ Impact and Implications
The findings of this study have significant implications for clinical practice. By highlighting the epidemiology and risk factors associated with nocardiosis, healthcare providers can better identify at-risk populations and implement timely interventions. The development of an interpretable machine learning model not only aids in mortality prediction but also paves the way for personalized treatment strategies, ultimately improving patient care and outcomes.
๐ฎ Conclusion
This study underscores the importance of recognizing nocardiosis as a serious opportunistic infection with rising incidence and notable mortality rates. The integration of machine learning into clinical practice offers a promising avenue for enhancing patient management and outcomes. Continued research and awareness are essential to combat this often-missed infection and improve healthcare responses.
๐ฌ Your comments
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The global epidemiology, risk factors, and mortality prediction of nocardiosis: an easily missed opportunistic infection.
Abstract
This study was to comprehensively investigate the epidemiology of nocardiosis worldwide and develop an interpretable machine learning (ML) model to predict mortality in patients with nocardiosis. The PubMed and Web of Science databases were searched for the literature review using the keywords: “Nocardia” or “nocardiosis” through 31 August 2024, 9,750 cases of nocardiosis were reported. Nine ML algorithms were employed to predict the mortality in patients with nocardiosis. A total of 9,750 reported cases were identified and included. Most cases were from North America and Asia. The mean age of patients was 50.4โยฑโ19.3, with a male predominance (64.7%). The overall all-cause mortality rate was 19.8%, although disseminated infections were associated with a higher mortality rate of 31.7%. Since 2000, the number of reported nocardiosis cases has increased markedly, while the all-cause mortality rate has decreased significantly and stabilized. The distribution of Nocardia species exhibited regional variation. Advanced age, male, underlying diseases, disseminated infections, infection type, clinical features, and use of corticosteroids or immunosuppressants had a higher risk of all-cause mortality [Odds Ratio (ORs)โ=โ1.35-2.63, Pโ<โ0.05]. The stochastic gradient boosting (SGBT) model outperformed eight other machine learning models, accurately predicting mortality in patients with nocardiosis across both training and test datasets. This study provides a comprehensive overview of the global epidemiology and species distribution of nocardiosis, highlighting distinct regional patterns. An interpretable ML model was developed and validated that helps clinicians identify high-risk patients early and provides a basis for developing personalized treatment plans.
Author: [‘Du B’, ‘Song Z’, ‘Ren Z’, ‘Tang D’, ‘Shen J’, ‘Yao J’, ‘Qiu X’, ‘Xu S’, ‘Yuan M’, ‘Liu Z’, ‘Li Z’]
Journal: Sci Rep
Citation: Du B, et al. The global epidemiology, risk factors, and mortality prediction of nocardiosis: an easily missed opportunistic infection. The global epidemiology, risk factors, and mortality prediction of nocardiosis: an easily missed opportunistic infection. 2025; 15:42090. doi: 10.1038/s41598-025-26244-1