Relationship between blood glucose levels and length of hospital stay in patients with acute pancreatitis: An analysis of MIMIC-III database

Eliminating COVID-19 as the immediate culprit for igniting pancreatitis

The impact of severe acute respiratory syndrome coronavirus 2 infection on the potential development of pancreatitis is a subject of ongoing debate within academic discourse. Establishing a causal link between COVID-19 and pancreatitis may not be fully supported by relying only on retrospective studies or case reports. This study examined the relationship between COVID-19 phenotypes and pancreatitis by Mendelian randomization (MR) method. The identification of instrumental variables (single nucleotide polymorphisms) that exhibit a robust association with the COVID-19 phenotypes was accomplished through a meticulous process of rigorous screening procedures. We included acute pancreatitis and chronic pancreatitis (CP) as the outcomes in the MR analysis, even though no definitive studies exist between COVID-19 and CP. A direct causal relationship between genetically predicted COVID-19 phenotypes and pancreatitis risk cannot be established. There is an ongoing debate over the designation of COVID-19 as a definitive cause of pancreatitis.

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

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.