Evidence map›Paper›PMID 37351008›Full record

ArticleInternational journal of general medicine2023

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 and 1 more

Open access · goldAbstract read
In one paragraph

Article in International journal of general medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Kang Zou *Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Shu Huang *Department of Gastroenterology, Lianshui County People' Hospital, Huaian, People's Republic of China.
Wensen Ren *Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Huan XuDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Wei ZhangDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Xiaomin ShiDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Lei ShiDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Xiaolin ZhongDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Yan PengDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Muhan LüDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Xiaowei TangDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.ORCID 0000-0001-6064-0526
Affiliated Hospital of Southwest Medical University · CNHuaian First People’s Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

acute pancreatitisdynamic nomogrameICU-CRDelectronic intensive care unit collaborative research databasemedical information mart for intensive care-IVMIMIC-IV

Identifiers

PMID37351008
PMCPMC10284301
OpenAlexW4380985456

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.