Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites

Li, Bu-Ren; Zhuo, Ying; Jiang, Ying-Ying; Zhang, Shi-Yan

doi:10.1038/s41598-024-63944-6

Cited by 3 publications

References 31 publications

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Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections

Zhou,

Zhang,

Deng

et al. 2024

Sci Rep

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The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning techniques, such as random forest analysis, can significantly improve diagnostic accuracy. We conducted a retrospective analysis of clinical and laboratory data from 214 patients with acute respiratory infections, collected between October 2022 and October 2023 at the Second Hospital of Nanping. The study population was categorized into three groups: COVID-19 positive ( n = 52), MP positive ( n = 140), and co-infected ( n = 22). Key biomarkers, including C-reactive protein (CRP), procalcitonin (PCT), interleukin- 6 (IL-6), and white blood cell (WBC) counts, were evaluated. Correlation analyses were conducted to assess relationships between biomarkers within each group. The random forest analysis was applied to evaluate the discriminative power of these biomarkers. The random forest model demonstrated high classification performance, with area under the ROC curve (AUC) scores of 0.86 (95% CI: 0.70–0.97) for COVID-19, 0.79 (95% CI: 0.64–0.92) for MP, 0.69 (95% CI: 0.50–0.87) for co-infections, and 0.90 (95% CI: 0.83–0.95) for the micro-average ROC. Additionally, the precision-recall curve for the random forest classifier showed a micro-average AUC of 0.80 (95% CI: 0.69–0.91). Confusion matrices highlighted the model’s accuracy (0.77) and biomarker relationships. The SHAP feature importance analysis indicated that age (0.27), CRP (0.25), IL6 (0.14), and PCT (0.14) were the most significant predictors. The integration of computational methods, particularly random forest analysis, in evaluating clinical and biomarker data presents a promising approach for enhancing diagnostic processes for infectious diseases. Our findings support the use of specific biomarkers in differentiating between COVID-19 and MP, potentially leading to more targeted and effective diagnostic strategies. This study underscores the potential of machine learning techniques in improving disease classification in the era of precision medicine.

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Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections

Zhou,

Zhang,

Deng

et al. 2024

Sci Rep

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Precision diagnostics of COVID-19 and Mycoplasma pneumoniae through random forest and biomarkers integration

Zhou,

Zhang,

Deng

et al. 2024

Preprint

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The COVID-19 pandemic has highlighted the critical need for precise diagnostic methods to distinguish between similar respiratory infections such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning techniques like Random Forest analysis can significantly improve diagnostic accuracy. We conducted a retrospective analysis of clinical and laboratory data from 214 acute respiratory infections patients, categorized into three groups: COVID-19 positive (n = 52), MP-positive (n = 140), and co-infected with both positive (n = 22). Key biomarkers including C-reactive protein (CRP), procalcitonin (PCT), interleukin 6 (IL-6), and white blood cell (WBC) counts were evaluated. The Random Forest algorithm was applied to analyze the discriminative power of these biomarkers. Our analysis revealed significant correlations between age, PCT, IL-6, CRP, and the patient groups. The Random Forest model demonstrated high classification performance with area under the ROC curve (AUC) scores of 0.91 for COVID-19, 0.90 for MP, and 0.65 for co-infections. Feature importance analysis indicated that age, CRP, IL6, and PCT were the most significant predictors. Confusion matrices and Spearman correlation heatmaps highlighted the model's accuracy and the relationships between different biomarkers. The integration of computational methods, particularly Random Forest analysis, in evaluating clinical and biomarker data presents a promising approach for enhancing diagnostic processes for infectious diseases. Our findings support the use of specific biomarkers in differentiating between COVID-19 and MP, potentially leading to more targeted and effective diagnostic strategies. This study underscores the potential of machine learning techniques in improving disease classification in the era of precision medicine.

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Aloe-emodin plus TIENAM ameliorate cecal ligation and puncture-induced sepsis in mice by attenuating inflammation and modulating microbiota

Su,

Deng,

et al. 2024

Front. Microbiol.

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Despite the high sepsis-associated mortality, effective and specific treatments remain limited. Using conventional antibiotics as TIENAM (imipenem and cilastatin sodium for injection, TIE) is challenging due to increasing bacterial resistance, diminishing their efficacy and leading to adverse effects. We previously found that aloe-emodin (AE) exerts therapeutic effects on sepsis by reducing systemic inflammation and regulating the gut microbiota. Here, we investigated whether administering AE and TIE post-sepsis onset, using a cecal ligation and puncture (CLP)-induced sepsis model, extends survival and improves physiological functions. Survival rates, inflammatory cytokines, tissue damage, immune cell populations, ascitic fluid microbiota, and key signaling pathways were assessed. Combining AE and TIE significantly enhanced survival rates, and reduced inflammation and bacterial load in septic mice, indicating potent antimicrobial properties. Moreover, substantial improvements in survival rates of AE + TIE-treated mice (10% to 60%) within 168 h were observed relative to the CLP group. This combination therapy also effectively modulated inflammatory marker (interleukin [IL]-6, IL-1β, and tumor necrosis factor [TNF]-α) levels and immune cell counts by decreasing those of B, NK, and TNFR2+ Treg cells, while increasing that of CD8+ T cells; alleviated tissue damage; reduced bacterial load in the peritoneal cavity; and suppressed the NF-κB signaling pathway. We also observed a significantly altered peritoneal cavity microbiota composition post-treatment, characterized by reduced pathogenic bacteria (Bacteroides) abundance. Our findings underscore the potential of AE + TIE in treating sepsis, and encourage further research and possible clinical implementations to surmount the limitations of TIE and amplify the therapeutic potential of AE.

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Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites

Cited by 3 publications

References 31 publications

Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections

Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections

Precision diagnostics of COVID-19 and Mycoplasma pneumoniae through random forest and biomarkers integration

Aloe-emodin plus TIENAM ameliorate cecal ligation and puncture-induced sepsis in mice by attenuating inflammation and modulating microbiota

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