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Prediction of Multiple Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine
Fumin Xu1, Xiao Chen2, Chenwenya Li3
1Department of Gastroenterology, Daping Hospital, Army Medical University, Chongqing 400042, China.
Background:
Multiple organ failure (MOF) may lead to an increased mortality rate of moderately severe (MSAP) or severe acute pancreatitis (SAP). This study is aimed to use machine learning to predict the risk of MOF in the course of disease.
Methods:
Clinical and laboratory features with significant differences between patients with and without MOF were screened out by univariate analysis. Prediction models were developed for selected features through six machine learning methods. The models were internally validated with a five-fold cross-validation, and a series of optimal feature subsets were generated in corresponding models. A test set was used to evaluate the predictive performance of the six models.
Results:
305 (68%) of 455 patients with MSAP or SAP developed MOF. Eighteen features with significant differences between the group with MOF and without it in the training and validation set were used for modeling. Interleukin-6 levels, creatinine levels, and the kinetic time were the three most important features in the optimal feature subsets selected by K-fold cross-validation. The adaptive boosting algorithm (AdaBoost) showed the best predictive performance with the highest AUC value (0.826; 95% confidence interval: 0.740 to 0.888). The sensitivity of AdaBoost (80.49%) and specificity of logistic regression analysis (93.33%) were the best scores among the six models in the test set.
Conclusions:
A predictive model of MOF complicated by MSAP or SAP was successfully developed based on machine learning. The predictive performance was evaluated by a test set, for which AdaBoost showed a satisfactory predictive performance. The study is registered with the China Clinical Trial Registry (Identifier: ChiCTR1800016079).
Insights
Machine learning effectively predicts multiple organ failure (MOF) risk in acute pancreatitis patients. The AdaBoost algorithm demonstrated strong predictive performance, aiding early intervention and improving outcomes.
Area of Science:
- Medical informatics
- Computational biology
- Clinical prediction modeling
Background:
- Multiple organ failure (MOF) significantly increases mortality in moderately severe (MSAP) and severe acute pancreatitis (SAP).
- Accurate prediction of MOF risk is crucial for timely clinical intervention.
Purpose of the Study:
- To develop and validate machine learning models for predicting MOF risk in MSAP or SAP patients.
- To identify key clinical and laboratory features associated with MOF development.
Main Methods:
- Univariate analysis identified significant clinical and laboratory features differentiating MOF and non-MOF groups.
- Six machine learning algorithms were employed to build predictive models.
- Models were internally validated using five-fold cross-validation and externally evaluated on a test set.
Main Results:
- 305 out of 455 patients (68%) developed MOF.
- Interleukin-6 levels, creatinine, and kinetic time were identified as key predictors.
- The Adaptive Boosting (AdaBoost) algorithm achieved the highest predictive performance (AUC=0.826).
Conclusions:
- A machine learning-based predictive model for MOF in acute pancreatitis was successfully developed.
- AdaBoost demonstrated satisfactory predictive performance, offering a valuable tool for clinical decision-making.
- The model's performance was validated on an independent test set.
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Acute pancreatitis is characterized by rapid inflammation of the pancreas, often caused by factors like gallstone blockage or excessive alcohol consumption. Chronic pancreatitis, on the other hand, is a slow, progressive inflammation that may result from long-term alcohol abuse, obstructions in the pancreatic duct, or genetic factors.
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Assessment: