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Necrotizing fasciitis (NF) is a severe and life-threatening soft tissue infection that requires timely and accurate diagnosis to improve patient outcomes. The early diagnosis of NF remains challenging due to its similarity to other subcutaneous soft tissue infections like cellulitis. This study aims to employ machine learning techniques to differentiate NF from cellulitis and enhance the diagnostic accuracy of NF by developing a modified LRINEC (MLRINEC) score. These modifications aimed to improve the sensitivity and specificity of NF diagnosis. The study utilized three machine learning classifiers—Logistic Regression, decision tree, and Random Forest—to assess their effectiveness in distinguishing between NF and cellulitis cases. The MLRINEC score was developed by incorporating six key blood test parameters: creatinine, hemoglobin, platelet count, sodium, white blood cell count, and C-reactive protein using laboratory data from Maha Sarakham Hospital in Northeastern Thailand. Our findings indicate that the decision tree classifier demonstrated superior performance, achieving the highest recall, particularly in accurately identifying NF cases. A feature importance analysis revealed that hemoglobin levels and white blood cell counts were the most critical factors influencing the model's predictions. The platelet count (PT), C-reactive protein (CRP), and creatinine (CT) also played important roles, while sodium levels (NA) were the least influential. The MLRINEC score demonstrates high accuracy in classifying NF and cellulitis patients, paving the way for improved diagnostic protocols in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Vascular pythiosis, caused by Pythium insidiosum, is a life-threatening disease with high mortality rate in patients with residual disease post-surgery. This study evaluated the effectiveness of a combination therapy of surgery, azithromycin, doxycycline, and itraconazole., Methods: In this open-label, Phase II multicenter trial, 51 patients were enrolled. Patients were categorized based on residual disease post-surgery (unresectable lesions, incomplete resection, or persistent symptoms). Patients with residual disease received azithromycin (500 mg daily), doxycycline (100 mg twice daily), and itraconazole (200 mg thrice daily) until beta-d-glucan (BDG) levels were negative (< 80 pg/ml) for three months. Those without residual disease received the same regimen for six months. Outcomes included all-cause mortality at six months, adverse events, changes in BDG levels over time, and factors associated with residual disease and mortality., Results: At six months, the all-cause mortality rate was 15.7%. Mortality in patients with residual disease was 31.5% compared to 6.25% for those without (p=0.04). Lesions above the popliteal artery were a significant predictor of residual disease (incidence rate ratio [IRR] 3.20, 95% confidence interval [CI] 1.08-11.70). BDG levels decreased over time (odds ratio [OR] 0.82, 95%CI 0.77-0.88 per week, p<0.001), but remained higher in the residual disease group (OR 4.29, 95% CI 1.55-11.92)., Conclusions: The combination therapy of surgery, azithromycin, doxycycline, and itraconazole improves survival in patients with vascular pythiosis, including those with residual disease. This regimen is well-tolerated and should be considered a standard of care, with further research needed for long-term outcomes., (© The Author(s) 2025. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)