Type 2 diabetes, high blood pressure and acute pancreatitis

FEATURES OF THE COMORBID COURSE OF CHRONIC PANCREATITIS AND ARTERIAL HYPERTENSION

OBJECTIVE

The aim: To investigate the clinical features of the chronic pancreatitis (CP) clinical course in patients with concomitant arterial hypertension.

PATIENTS AND METHODS

Materials and methods: 100 patients with PD were investigated. In 60 patients, the course of CP and AH was combined - the main group, the comparison group - 40 patients with CP without concomitant pathology.

RESULTS

Results: In 52 patients (86.7%) with CP and AH abdominal pain was recorded versus 24 (60.0%) with CP (p<0.01). Correlation analysis revealed weak relationship between the intensity of pain acc. Visual analogue scale (VAS) of pain and the degree of steatorrhea (τ = 0.40, p <0.01), the degree of amilorrhea (τ = 0.39, p <0.01) and the average strength of the relationship with creatorrhoea (τ = 0.60 , p <0.01). Dyspepsia was revealed in CP and AH: flatulence in 55 (91.7%) compared with 26 (65.0%) with CP, diarrhea in 52 (86.7%) patients in the main group versus 23 (57.5%) in the comparison group, nausea in 52 (86.7%), vomiting in 45 (75.0%) in the main group versus 18 (45.0%) and 12 (30.0%) patients from the comparison group (p<0.01 in all comparisons). Asthenia is expressed in patients with CP and AH: weakness in 50 (83.3%) patients versus 6 (15.0%), psychoemotional lability in 44 (73.3%) versus 3 (7.5%), headache in 47 (78.3%) versus 6 (15.0%), sleep disorders in 45 (75.0%) compared with 1 (2.5%) patients with CP (p<0.01 in all comparisons).

CONCLUSION

Conclusions: The negative effect of concomitant hypertension on the clinical course of CP has been established. AH contributes to increased pain syndrome, dyspepsia, asthenia.

Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis

BACKGROUND

Multiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF.

METHODS

Patients with MSAP and SAP who were admitted from July 2014 to June 2017 were included. Firstly, parameters with significant differences between patients with MOF and without MOF were screened out by univariate analysis. Then, support vector machine (SVM), logistic regression analysis (LRA) and artificial neural networks (ANN) models were constructed based on these factors, and five-fold cross-validation was used to train each model.

RESULTS

A total of 263 patients were enrolled. Univariate analysis screened out sixteen parameters referring to blood volume, inflammatory, coagulation and renal function to construct machine-learning models. The predictive efficiency of the optimal combinations of features by SVM, LRA, and ANN was almost equal (AUC = 0.840, 0.832, and 0.834, respectively), as well as the Acute Physiology and Chronic Health Evaluation II score (AUC = 0.814, P > 0.05). The common important predictive factors were HCT, K-time, IL-6 and creatinine in three models.

CONCLUSIONS

Three machine-learning models can be efficient prognostic tools for predicting MOF in MSAP and SAP. ANN is recommended, which only needs four common parameters.