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Prediction of infected pancreatic necrosis in patients with acute necrotizing pancreatitis based on ensemble machine learning model
it is based on article Reviewed by:
Dr Reza Lankarani, General Surgeon
Founder | Surgical Pioneering Newsletter and Podcast Series
Editorial Board Member | Genesis Journal of Surgery and Medicine
- Online Publication Date: August 7, 2025
- DOI: 10.1186/s13017-025-00642-2(https://doi.org/10.1186/s13017-025-00642-2)
- Journal: World Journal of Emergency Surgery
Key Findings:
- Developed an ensemble machine learning (EL) model (LASSO + Random Forest + SVM) to predict infected pancreatic necrosis (IPN) in acute necrotizing pancreatitis (ANP) patients.
- Identified 31 risk factors (7 demographic, 14 laboratory, 10 radiological) via LASSO regression.
- EL model outperformed logistic regression (LR):
- Training AUC: 0.916 vs. 0.744; Validation AUC: 0.919 vs. 0.742.
- Accuracy: 92.6% (training), 91.5% (validation).
- Stable performance across IPN onset stages:
- AUC: 0.888 (7 days), 0.906 (7–14 days), 0.901 (14 days).
- External validation (78 patients): AUC 0.883.
- Fagan nomogram integrated for clinical utility.
Methods:
- Cohort: 1,073 ANP patients (349 IPN, 724 sterile necrosis SPN) from Xiangya Hospital (2011–2023).
- Data: Demographic, lab (e.g., PCT, RDW, albumin), and radiological scores (CTSI, MCTSI, EPIC).
- Validation: 7:3 training-validation split + external cohort (Third Xiangya Hospital).
Conclusions:
- EL model enables early IPN prediction, guiding timely antibiotics/surgery to reduce mortality (IPN mortality: 32–39%).
---
2. Critical Assessment
Strengths:
- Large Prospective Cohort: Robust sample size (n=1,073) with external validation (n=78).
- Innovative Methodology: First EL model integrating clinical, lab, and radiological data for IPN prediction.
- Clinical Utility: Fagan nomogram translates predictions into actionable probabilities.
- Temporal Analysis: Validated across IPN onset stages (7d to 14d), addressing disease heterogeneity.
- Rigorous Validation: Bootstrapping, calibration curves, and decision curve analysis (DCA) confirm reliability.
Weaknesses:
- Limited Generalizability: External cohort from same province (Hunan, China); lacks multi-center diversity.
- Exclusion Bias: Critically ill patients who died pre-surgery excluded, potentially underestimating IPN risk.
- Historical Bias: Data span 12 years (2011–2023); evolving treatments may affect model applicability.
- Black-Box Model: Limited explainability of EL predictions; no comparison with other ML algorithms (e.g., XGBoost).
- Incomplete Variables: Gut microbiota (a known IPN predictor) not included.
---
3. Comparison with Recent Studies
Key Insights:
- Sun et al. achieve highest AUC (0.92) by leveraging multi-modal data and ensemble ML.
- Temporal subgroup analysis addresses a gap in prior studies focused on early-phase prediction only.
- Rad-score innovation: Radiological subfeatures (e.g., mesenteric inflammation) enhance accuracy vs. total scores alone.
---
4. Expert Review & Future Directions
Significance:
This study sets a new benchmark for IPN prediction by synergizing ML with clinical-radiological data. The EL model’s stability across disease stages and Fagan nomogram integration offer immediate clinical value for risk stratification.
Impact:
- Potential to reduce mortality through early intervention (e.g., antibiotics/necrosectomy).
- Template for ML adoption in emergency surgery, moving beyond traditional LR models.
Future Research:
1. Multi-center validation across diverse populations to enhance generalizability.
2. Explainable AI (XAI): Unpack the "black box" for clinician trust (e.g., SHAP values).
3. Dynamic modeling: Incorporate real-time data (e.g., serial PCT measurements).
4. Microbiome integration: Explore gut microbiota as a predictive feature.
---
Summary for Patients and General Public
"This research developed an artificial intelligence (AI) tool that helps doctors predict dangerous infections in severe pancreatitis patients. By analyzing blood tests and scan results, the AI identifies high-risk patients earlier and more accurately than current methods. This allows timely treatment with antibiotics or surgery, potentially saving lives. While promising, the tool needs further testing in diverse hospitals before widespread use. For patients, this could mean faster, more precise care during critical illness."
---
Final Editorial Perspective:
Sun et al. deliver a methodologically robust, clinically relevant advancement in pancreatitis care. Despite limitations in generalizability and model transparency, this work pioneers ML-driven precision medicine for IPN. Future iterations addressing these gaps could redefine management paradigms for this high-mortality condition.
Get full access to Reza Lankarani at lankarani.substack.com/subscribe
View all episodes
By Dr. Reza Lankaraniit is based on article Reviewed by:
Dr Reza Lankarani, General Surgeon
Founder | Surgical Pioneering Newsletter and Podcast Series
Editorial Board Member | Genesis Journal of Surgery and Medicine
- Online Publication Date: August 7, 2025
- DOI: 10.1186/s13017-025-00642-2(https://doi.org/10.1186/s13017-025-00642-2)
- Journal: World Journal of Emergency Surgery
Key Findings:
- Developed an ensemble machine learning (EL) model (LASSO + Random Forest + SVM) to predict infected pancreatic necrosis (IPN) in acute necrotizing pancreatitis (ANP) patients.
- Identified 31 risk factors (7 demographic, 14 laboratory, 10 radiological) via LASSO regression.
- EL model outperformed logistic regression (LR):
- Training AUC: 0.916 vs. 0.744; Validation AUC: 0.919 vs. 0.742.
- Accuracy: 92.6% (training), 91.5% (validation).
- Stable performance across IPN onset stages:
- AUC: 0.888 (7 days), 0.906 (7–14 days), 0.901 (14 days).
- External validation (78 patients): AUC 0.883.
- Fagan nomogram integrated for clinical utility.
Methods:
- Cohort: 1,073 ANP patients (349 IPN, 724 sterile necrosis SPN) from Xiangya Hospital (2011–2023).
- Data: Demographic, lab (e.g., PCT, RDW, albumin), and radiological scores (CTSI, MCTSI, EPIC).
- Validation: 7:3 training-validation split + external cohort (Third Xiangya Hospital).
Conclusions:
- EL model enables early IPN prediction, guiding timely antibiotics/surgery to reduce mortality (IPN mortality: 32–39%).
---
2. Critical Assessment
Strengths:
- Large Prospective Cohort: Robust sample size (n=1,073) with external validation (n=78).
- Innovative Methodology: First EL model integrating clinical, lab, and radiological data for IPN prediction.
- Clinical Utility: Fagan nomogram translates predictions into actionable probabilities.
- Temporal Analysis: Validated across IPN onset stages (7d to 14d), addressing disease heterogeneity.
- Rigorous Validation: Bootstrapping, calibration curves, and decision curve analysis (DCA) confirm reliability.
Weaknesses:
- Limited Generalizability: External cohort from same province (Hunan, China); lacks multi-center diversity.
- Exclusion Bias: Critically ill patients who died pre-surgery excluded, potentially underestimating IPN risk.
- Historical Bias: Data span 12 years (2011–2023); evolving treatments may affect model applicability.
- Black-Box Model: Limited explainability of EL predictions; no comparison with other ML algorithms (e.g., XGBoost).
- Incomplete Variables: Gut microbiota (a known IPN predictor) not included.
---
3. Comparison with Recent Studies
Key Insights:
- Sun et al. achieve highest AUC (0.92) by leveraging multi-modal data and ensemble ML.
- Temporal subgroup analysis addresses a gap in prior studies focused on early-phase prediction only.
- Rad-score innovation: Radiological subfeatures (e.g., mesenteric inflammation) enhance accuracy vs. total scores alone.
---
4. Expert Review & Future Directions
Significance:
This study sets a new benchmark for IPN prediction by synergizing ML with clinical-radiological data. The EL model’s stability across disease stages and Fagan nomogram integration offer immediate clinical value for risk stratification.
Impact:
- Potential to reduce mortality through early intervention (e.g., antibiotics/necrosectomy).
- Template for ML adoption in emergency surgery, moving beyond traditional LR models.
Future Research:
1. Multi-center validation across diverse populations to enhance generalizability.
2. Explainable AI (XAI): Unpack the "black box" for clinician trust (e.g., SHAP values).
3. Dynamic modeling: Incorporate real-time data (e.g., serial PCT measurements).
4. Microbiome integration: Explore gut microbiota as a predictive feature.
---
Summary for Patients and General Public
"This research developed an artificial intelligence (AI) tool that helps doctors predict dangerous infections in severe pancreatitis patients. By analyzing blood tests and scan results, the AI identifies high-risk patients earlier and more accurately than current methods. This allows timely treatment with antibiotics or surgery, potentially saving lives. While promising, the tool needs further testing in diverse hospitals before widespread use. For patients, this could mean faster, more precise care during critical illness."
---
Final Editorial Perspective:
Sun et al. deliver a methodologically robust, clinically relevant advancement in pancreatitis care. Despite limitations in generalizability and model transparency, this work pioneers ML-driven precision medicine for IPN. Future iterations addressing these gaps could redefine management paradigms for this high-mortality condition.
Get full access to Reza Lankarani at lankarani.substack.com/subscribe