Development and Validation of a Dynamic Nomogram for Predicting in-Hospital Mortality in Patients with Acute Pancreatitis: A Retrospective Cohort Study in the Intensive Care Unit

Kang Zou; Shu Huang; Wensen Ren; Huan Xu; Wei Zhang; Xiaomin Shi; Lei Shi; Xiaolin Zhong; Yan Peng; Muhan Lü; Xiaowei Tang

doi:10.2147/IJGM.S409812

Development and Validation of a Dynamic Nomogram for Predicting in-Hospital Mortality in Patients with Acute Pancreatitis: A Retrospective Cohort Study in the Intensive Care Unit

Int J Gen Med. 2023 Jun 17:16:2541-2553. doi: 10.2147/IJGM.S409812. eCollection 2023.

Authors

Kang Zou^#^{1

2}, Shu Huang^#^{3

4}, Wensen Ren^#^{1

2}, Huan Xu^{1

2}, Wei Zhang^{1

2}, Xiaomin Shi^{1

2}, Lei Shi^{1

2}, Xiaolin Zhong^{1

2}, Yan Peng^{1

2}, Muhan Lü^{1

2}, Xiaowei Tang^{1

2}

Affiliations

¹ Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
² Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, People's Republic of China.
³ Department of Gastroenterology, Lianshui County People' Hospital, Huaian, People's Republic of China.
⁴ Department of Gastroenterology, Lianshui People' Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: The aim of this study is to develop and validate a predictive model for the prediction of in-hospital mortality in patients with acute pancreatitis (AP) based on the intensive care database.

Patients and methods: We analyzed the data of patients with AP in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Then, patients from MIMIC-IV were divided into a development group and a validation group according to the ratio of 8:2, and eICU-CRD was assigned as an external validation group. Univariate logistic regression and least absolute shrinkage and selection operator regression were used for screening the best predictors, and multivariate logistic regression was used to establish a dynamic nomogram. We evaluated the discrimination, calibration, and clinical efficacy of the nomogram, and compared the performance of the nomogram with Acute Physiology and Chronic Health Evaluation II (APACHE-II) score and Bedside Index of Severity in AP (BISAP) score.

Results: A total of 1030 and 514 patients with AP in MIMIC-IV database and eICU-CRD were included in the study. After stepwise analysis, 8 out of a total of 37 variables were selected to construct the nomogram. The dynamic nomogram can be obtained by visiting https://model.sci-inn.com/KangZou/. The area under receiver operating characteristic curve (AUC) of the nomogram was 0.859, 0.871, and 0.847 in the development, internal, and external validation set respectively. The nomogram had a similar performance with APACHE-II (AUC = 0.841, p = 0.537) but performed better than BISAP (AUC = 0.690, p = 0.001) score in the validation group. Moreover, the calibration curve presented a satisfactory predictive accuracy, and the decision curve analysis suggested great clinical application value of the nomogram.

Conclusion: Based on the results of internal and external validation, the nomogram showed favorable discrimination, calibration, and clinical practicability in predicting the in-hospital mortality of patients with AP.

Keywords: MIMIC-IV; acute pancreatitis; dynamic nomogram; eICU-CRD; electronic intensive care unit collaborative research database; medical information mart for intensive care-IV.

Grants and funding

This study was funded by Natural Science Foundation of Sichuan Province (No. 2022NSFSC1378).