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Risk factors and early clinical outcomes of acute pancreatitis: A retrospective case series from Kyrgyzstan
Kudaibergen Osmonaliev1, Shafee Ur Rehman2, Edilbek Kudayarov3
1Faculty of Medicine, Ala-Too International University, Bishkek, Kyrgyzstan/ National Surgical Center, Bishkek, Kyrgyzstan.
Insights
High serum amylase, elevated white blood cell count, and body mass index (BMI) can predict severe acute pancreatitis (AP). These accessible markers aid early risk stratification for better patient outcomes.
Area of Science:
- Gastroenterology
- Clinical Medicine
- Data Science in Healthcare
Background:
- Acute pancreatitis (AP) is a frequent gastrointestinal emergency with unpredictable severity.
- Early identification of patients at risk for severe AP is crucial for timely intervention.
- Reliable prognostic markers for AP severity are limited, especially in Central Asian settings.
Purpose of the Study:
- To identify early clinical, laboratory, and demographic predictors of AP severity.
- To enhance early risk stratification and guide prompt clinical management using statistical and machine learning.
- To address the need for improved prognostic markers in AP.
Main Methods:
- Retrospective analysis of 40 AP patients in Kyrgyzstan (2022-2024).
- Evaluation of admission data including demographics, symptoms, and lab values against revised Atlanta criteria.
- Application of statistical analyses (correlation, logistic regression) and machine learning for predictor identification.
Main Results:
- Serum amylase >500 U/L, WBC count >15×10^9/L, and BMI ≥30 were significant predictors of severe AP.
- Obesity (BMI ≥30) correlated with longer hospital stays (median 12 vs. 7 days).
- Machine learning confirmed serum amylase, WBC count, and BMI as key predictors.
Conclusions:
- Serum amylase, WBC count, and BMI are practical markers for early AP severity prediction.
- Integrating these markers into initial assessments can improve risk stratification and clinical decision-making.
- Further multicenter studies are recommended to validate findings and refine AP prediction models.
Background:
Acute pancreatitis (AP) is a common gastrointestinal emergency characterized by unpredictable severity. Early identification of patients at risk for severe disease is essential for timely intervention and improved outcomes, yet reliable prognostic markers remain limited, particularly in Central Asian clinical settings.
Objective:
To identify early clinical, laboratory, and demographic predictors of acute pancreatitis (AP) severity using statistical and machine learning approaches, to improve early risk stratification and guide prompt clinical management.
Methods:
This retrospective case series analysed 40 patients diagnosed with AP between 2022 and 2024 at tertiary hospitals in Bishkek, Kyrgyzstan. Admission data, including demographic characteristics, clinical symptoms, and laboratory values, were collected and evaluated using the revised Atlanta criteria. Statistical analyses included correlation testing, subgroup comparisons, and logistic regression, while a machine-learning-based feature importance analysis was used to identify key predictors of severe AP.
Results:
Several variables were significantly associated with severe AP. Serum amylase >500 U/L (OR = 5.2, p 7lt; 0.001), WBC count >15×109/L (OR = 4.7, p <0.001), and BMI ≥30 (OR = 3.4, p = 0.003) emerged as strong predictors of severity. A strong correlation was observed between total bilirubin and jaundice (r = 0.62, p < 0.001). Obese patients had longer hospital stays compared with non-obese patients (median 12 vs. 7 days; p = 0.021). Machine learning analysis confirmed serum amylase, WBC count, and BMI as the most influential predictors.
Conclusion:
Serum amylase, WBC count and BMI are practical, easily accessible markers that can support early prediction of AP severity. Incorporating these indicators into initial assessment protocols may enhance risk stratification and optimize clinical decision-making. Prospective multicentre studies are needed to validate these findings and refine AP severity prediction models.
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