Development and validation of a nomogram for predicting survival in patients with acute pancreatitis
10.5847/wjem.j.1920-8642.2023.022
- Author:
Xiao-guang Zhu
1
,
2
Author Information
1. Department of Emergency Medicine, Shanghai Jiao Tong University Affiliated Sixth People&rsquo
2. s Hospital, Shanghai 200233, China
- Publication Type:Journal Article
- Keywords:
Acute pancreatitis;
Risk factor;
Prognosis
- From:
World Journal of Emergency Medicine
2023;14(1):44-48
- CountryChina
- Language:English
-
Abstract:
BACKGROUND: Acute pancreatitis (AP) is a complex and heterogeneous disease. We aimed to design and validate a prognostic nomogram for improving the prediction of short-term survival in patients with AP.
METHODS: The clinical data of 632 patients with AP were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The nomogram for the prediction of 30-day, 60-day and 90-day survival was developed by incorporating the risk factors identified by multivariate Cox analyses.
RESULTS: Multivariate Cox proportional hazard model analysis showed that age (hazard ratio [HR]=1.06, 95% confidence interval [95% CI] 1.03-1.08, P<0.001), white blood cell count (HR=1.03, 95% CI 1.00-1.06, P=0.046), systolic blood pressure (HR=0.99, 95% CI 0.97-1.00, P=0.015), serum lactate level (HR=1.10, 95% CI 1.01-1.20, P=0.023), and Simplified Acute Physiology Score II (HR=1.04, 95% CI 1.02-1.06, P<0.001) were independent predictors of 90-day mortality in patients with AP. A prognostic nomogram model for 30-day, 60-day, and 90-day survival based on these variables was built. Receiver operating characteristic (ROC) curve analysis demonstrated that the nomogram had good accuracy for predicting 30-day, 60-day, and 90-day survival (area under the ROC curve: 0.796, 0.812, and 0.854, respectively; bootstrap-corrected C-index value: 0.782, 0.799, and 0.846, respectively).
CONCLUSION: The nomogram-based prognostic model was able to accurately predict 30-day, 60-day, and 90-day survival outcomes and thus may be of value for risk stratification and clinical decision-making for critically ill patients with AP.
