Development and validation of a nomogram for predicting survival in patients with acute pancreatitis

doi:10.5847/wjem.j.1920-8642.2023.022

Abstract

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.

Key words: Acute pancreatitis, Risk factor, Prognosis

Xiao-guang Zhu, Jia-mei Jiang, Yong-xia Li, Jing Gao, Wei Wu, Qi-ming Feng. Development and validation of a nomogram for predicting survival in patients with acute pancreatitis[J]. World Journal of Emergency Medicine, 2023, 14(1): 44-48.

Figures/Tables 2

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Variables	Univariate regression		Multivariate Cox regression
Variables	HR, 95% CI	P-value	HR, 95% CI	P-value
Age, years	1.09, 1.07-1.12	<0.001	1.06, 1.03-1.08	<0.001
WBC, ×10⁹/L	1.08, 1.05-1.12	<0.001	1.03, 1.00-1.06	0.046
Creatinine, mg/dL	1.52, 1.32-1.74	<0.001
BUN, mg/dL	1.03, 1.02-1.04	<0.001
SBP, mmHg	0.97, 0.96-0.98	<0.001	0.99, 0.97-1.00	0.015
DBP, mmHg	0.95, 0.94-0.97	<0.001
Lactate, mmol/L	1.24, 1.13-1.36	<0.001	1.10, 1.01-1.20	0.023
SAPS-II	1.09, 1.07-1.11	<0.001	1.04, 1.02-1.06	<0.001