Systemic immune dysregulation and local barrier failure as predictors of septic shock in acute appendicitis: a large-scale multicenter interpretable machine learning study
- 1Department of Ultrasound, Nanjing Gaochun People’s Hospital , ,
Abstract
Background: The transition from localized acute appendicitis to systemic septic shock is driven by complex immune and metabolic dysregulation combined with anatomical barrier failure. Accurately predicting early hemodynamic deterioration remains clinically challenging. This study aimed to develop an interpretable multimodal machine learning model by integrating systemic immunological profiles with local sonographic characteristics.
Methods: This multicenter cohort study included 3000 patients with acute appendicitis. Following rigorous data reduction using the least absolute shrinkage and selection operator regression, eight supervised machine learning algorithms were constructed. The optimal predictive algorithm was comprehensively evaluated across training, internal testing, and independent external validation cohorts. The SHapley Additive exPlanations framework was utilized to provide transparent model interpretability, and the final algorithm was deployed as a dynamic digital calculator.
Results: Six independent predictors were identified, including the Systemic Immune Inflammation Index, C-reactive protein to albumin ratio, glucose to lymphocyte ratio, Prognostic Nutritional Index, periappendiceal fluid, and the sonographic loss of the submucosal echogenic layer. The Random Forest model demonstrated the highest discriminative performance among the evaluated algorithms, achieving an area under the curve of 0.914. The model’s discrimination, calibration, and decision-analytic performance were consistently evaluated in the internal testing and external validation cohorts. SHAP analysis indicated that systemic inflammatory indices and sonographic features reflecting local disease severity made important contributions to the model’s predicted risk of septic shock. These findings should be interpreted as predictive associations rather than evidence of biological causality or synergy.
Conclusion: This multicenter study developed and externally validated an interpretable machine learning model for early risk stratification of septic shock. The publicly accessible calculator may support individualized risk assessment; however, prospective impact evaluation is required to determine whether its clinical implementation facilitates earlier resuscitation or improves patient outcomes.
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BibTeX
@article{chen2026systemic,
title = {{Systemic immune dysregulation and local barrier failure as predictors of septic shock in acute appendicitis: a large-scale multicenter interpretable machine learning study}},
author = {Yunfeng Chen and Yunyun Rui},
journal = {Frontiers in cellular and infection microbiology},
year = {2026},
month = oct,
doi = {10.3389/fcimb.2026.1935756},
url = {https://europepmc.org/article/PMC/PMC13719369},
note = {CC BY},
}
RIS
TY - JOUR
TI - Systemic immune dysregulation and local barrier failure as predictors of septic shock in acute appendicitis: a large-scale multicenter interpretable machine learning study
AU - Yunfeng Chen
AU - Yunyun Rui
PY - 2026
DA - 2026/10/16
JO - Frontiers in cellular and infection microbiology
DO - 10.3389/fcimb.2026.1935756
UR - https://europepmc.org/article/PMC/PMC13719369
AB - Background: The transition from localized acute appendicitis to systemic septic shock is driven by complex immune and metabolic dysregulation combined with anatomical barrier failure. Accurately predicting early hemodynamic deterioration remains clinically challenging. This study aimed to develop an interpretable multimodal machine learning model by integrating systemic immunological profiles with local sonographic characteristics. Methods: This multicenter cohort study included 3000 patients with acute appendicitis. Following rigorous data reduction using the least absolute shrinkage and selection operator regression, eight supervised machine learning algorithms were constructed. The optimal predictive algorithm was comprehensively evaluated across training, internal testing, and independent external validation cohorts. The SHapley Additive exPlanations framework was utilized to provide transparent model interpretability, and the final algorithm was deployed as a dynamic digital calculator. Results: Six independent predictors were identified, including the Systemic Immune Inflammation Index, C-reactive protein to albumin ratio, glucose to lymphocyte ratio, Prognostic Nutritional Index, periappendiceal fluid, and the sonographic loss of the submucosal echogenic layer. The Random Forest model demonstrated the highest discriminative performance among the evaluated algorithms, achieving an area under the curve of 0.914. The model’s discrimination, calibration, and decision-analytic performance were consistently evaluated in the internal testing and external validation cohorts. SHAP analysis indicated that systemic inflammatory indices and sonographic features reflecting local disease severity made important contributions to the model’s predicted risk of septic shock. These findings should be interpreted as predictive associations rather than evidence of biological causality or synergy. Conclusion: This multicenter study developed and externally validated an interpretable machine learning model for early risk stratification of septic shock. The publicly accessible calculator may support individualized risk assessment; however, prospective impact evaluation is required to determine whether its clinical implementation facilitates earlier resuscitation or improves patient outcomes.
SN - 2235-2988
ER -