Dataset Information

Post-pancreatitis diabetes mellitus is common in chronic pancreatitis and is associated with adverse outcomes.

ABSTRACT:

Background

Post-pancreatitis diabetes mellitus (PPDM) is a common consequence of chronic pancreatitis (CP). We aimed to determine the incidence and predictors of PPDM after CP onset, as well as complications and antidiabetic therapy requirements, in a high-volume tertiary center.

Methods

We did a cohort study with retrospectively collected data from patients with definite CP seen at the Karolinska University Hospital between January 1999 and December 2020. Cause-specific Cox regression analysis was used to assess PPDM predictors. To estimate risk of complications and need for therapy the Fine-Gray subdistribution hazard model was employed, accounting for death as a competing risk.

Results

We identified 481 patients with CP. The cumulative incidence of PPDM was 5.1%, 13.2%, 27.5% and 38.9% at 5, 10, 15 and 20 years, respectively. Compared to CP patients without diabetes, patients with PPDM were predominantly male (55% vs. 75%), had more frequently alcoholic etiology (44% vs. 62%) and previous acute pancreatitis. The only independent predictor of PPDM was presence of pancreatic calcifications (aHR = 2.45, 95% CI 1.30-4.63). Patients with PPDM had higher rates of microangiopathy (aSHR = 1.59, 95% CI 1.02-2.52) and infection (aSHR = 4.53, 95% CI 2.60-9.09) compared to CP patients who had type 2 diabetes (T2DM). The rate of insulin use was three-fold higher, whereas metformin use rate was two-fold higher in the same comparison.

Conclusions

Patients with PPDM have a higher frequency of clinically significant complications and were more commonly prescribed insulin and metformin, suggesting a more aggressive phenotype than that of T2DM. Greater PPDM awareness is needed to optimize disease management.

SUBMITTER: Dugic A

PROVIDER: S-EPMC9892477 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Post-pancreatitis diabetes mellitus is common in chronic pancreatitis and is associated with adverse outcomes.

Dugic Ana A Hagström Hannes H Dahlman Ingrid I Rutkowski Wiktor W Daou Diana D Kulinski Paula P Löhr J-Matthias JM Vujasinovic Miroslav M

United European gastroenterology journal 20221201 1

<h4>Background</h4>Post-pancreatitis diabetes mellitus (PPDM) is a common consequence of chronic pancreatitis (CP). We aimed to determine the incidence and predictors of PPDM after CP onset, as well as complications and antidiabetic therapy requirements, in a high-volume tertiary center.<h4>Methods</h4>We did a cohort study with retrospectively collected data from patients with definite CP seen at the Karolinska University Hospital between January 1999 and December 2020. Cause-specific Cox regre ...[more]

PMID: 36454055

Similar Datasets

Project description:BackgroundTwenty-three percent of patients are diagnosed with diabetes mellitus after the first episode of acute pancreatitis. The incidence of post-acute pancreatitis diabetes mellitus is significantly higher than that of type 1 diabetes mellitus. Some studies have concluded that the all-cause mortality and worse prognosis of diabetes after pancreatitis are higher. We predicted that number of recurrences of pancreatitis would be significantly associated with the incidences of metabolic syndrome, abdominal obesity, and post-acute pancreatitis diabetes mellitus.MethodsPatients admitted to our hospital for hypertriglyceridemic acute pancreatitis from 2013-2021 were selected for a cross-sectional study. Statistical analysis methods were used to analyze the effect of recurrences on the long-term prognosis of patients with hypertriglyceridemic acute pancreatitis.ResultsIn this study, 101 patients with hypertriglyceridemic acute pancreatitis were included: 60 (59.41%) in the recurrent acute pancreatitis group and 41 (40.59%) in the only one episode of acute pancreatitis group. Among all hypertriglyceridemic acute pancreatitis patients, approximately 61.4% were diagnosed with abdominal obesity, 33.7% of patients are diagnosed with metabolic syndrome, 34.7% of patients are diagnosed with diabetes mellitus, and 21.8% of patients are diagnosed with post-acute pancreatitis diabetes mellitus. Recurrent acute pancreatitis were independent risk factors for post-acute pancreatitis diabetes mellitus in patients with hypertriglyceridemic acute pancreatitis (odds ratio [OR] = 3.964, 95% confidence interval [CI] = 1.230-12.774) and the risk of post-acute pancreatitis diabetes mellitus in patients with three or more recurrent episodes was 6.607 times higher than that in patients without recurrent episodes (OR = 6.607, 95% CI = 1.412-30.916).ConclusionsRecurrence is an independent risk factor for the development of post-acute pancreatitis diabetes mellitus and is significantly associated with the number of recurrences.

Project description:BackgroundDiabetes mellitus can occur after acute pancreatitis (AP), but the accurate quantitative methods to predict post-acute pancreatitis diabetes mellitus (PPDM-A) are lacking. This retrospective study aimed to establish a radiomics model based on contrast-enhanced computed tomography (CECT) for predicting PPDM-A.MethodsA total of 374 patients with first-episode AP were retrospectively enrolled from two tertiary referral centers. There were 224 patients in the training cohort, 56 in the internal validation cohort, and 94 in the external validation cohort, and there were 86, 22, and 27 patients with PPDM-A in these cohorts, respectively. The clinical characteristics were collected from the hospital information system. A total of 2,398 radiomics features, including shape-based features, first-order histogram features, high order textural features, and transformed features, were extracted from the arterial- and venous-phase CECT images. Intraclass correlation coefficients were used to assess the intraobserver reliability and interobserver agreement. Random forest-based recursive feature elimination, collinearity analysis, and least absolute shrinkage and selection operator (LASSO) were used for selecting the final features. Three classification methods [eXtreme Gradient Boosting (XGBoost), Adaptive Boosting, and Decision Tree] were used to build three models and performances of the three models were compared. Each of the three classification methods were used to establish the clinical model, radiomics model, and combined model for predicting PPDM-A, resulting in a total of nine classifiers. The predictive performances of the models were evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score.ResultsEleven radiomics features were selected after a reproducibility test and dimensionality reduction. Among the three classification methods, the XGBoost classifier showed better and more consistent performances. The AUC of the XGBoost's radiomics model to predict PPDM-A in the training, internal, and external cohorts was good (0.964, 0.901, and 0.857, respectively). The AUC of the XGBoost's combined model to predict PPDM-A in the training, internal, and external cohorts was good (0.980, 0.901, and 0.882, respectively). The AUC of the XGBoost's clinical model to predict PPDM-A in the training, internal, and external cohorts did not perform well (0.685, 0.733, and 0.619, respectively). In the external validation cohort, the AUC of the XGBoost's radiomics model was significantly higher than that of the clinical model (0.857 vs. 0.619, P<0.001), but there was no significant difference between the combined and radiomics models (0.882 vs. 0.857, P=0.317).ConclusionsThe radiomics model based on CECT performs well and can be used as an early quantitative method to predict the occurrence of PPDM-A.

Dataset Information

Post-pancreatitis diabetes mellitus is common in chronic pancreatitis and is associated with adverse outcomes.

Background

Methods

Results

Conclusions

Publications

Post-pancreatitis diabetes mellitus is common in chronic pancreatitis and is associated with adverse outcomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets