The Effect of Admission Serum Triglyceride Level on the Prediction of Severity of Acute Pancreatitis in Pregnancy

Assessment of early factors for identification or prediction severe acute pancreatitis in pregnancy

BACKGROUND

Acute pancreatitis in pregnancy (APIP) is a rare and serious condition, and severe APIP (SAPIP) can lead to pancreatic necrosis, abscess, multiple organ dysfunction, and other adverse maternal and infant outcomes. Therefore, early identification or prediction of SAPIP is important.

AIM

To assess factors for early identification or prediction of SAPIP.

METHODS

The clinical data of patients with APIP were retrospectively analyzed. Patients were classified with mild acute pancreatitis or severe acute pancreatitis, and the clinical characteristics and laboratory biochemical indexes were compared between the two groups. Logical regression and receiver operating characteristic curve analyses were performed to assess the efficacy of the factors for identification or prediction of SAPIP.

RESULTS

A total of 45 APIP patients were enrolled. Compared with the mild acute pancreatitis group, the severe acute pancreatitis group had significantly increased (P < 0.01) heart rate (HR), hemoglobin, neutrophil ratio (NEUT%), and neutrophil–lymphocyte ratio (NLR), while lymphocytes were significantly decreased (P < 0.01). Logical regression analysis showed that HR, NEUT%, NLR, and lymphocyte count differed significantly (P < 0.01) between the groups. These may be factors for early identification or prediction of SAPIP. The area under the curve of HR, NEUT%, NLR, and lymphocyte count in the receiver operating characteristic curve analysis was 0.748, 0.732, 0.821, and 0.774, respectively. The combined analysis showed that the area under the curve, sensitivity, and specificity were 0.869, 90.5%, and 70.8%, respectively.

CONCLUSION

HR, NEUT%, NLR, and lymphocyte count can be used for early identification or prediction of SAPIP, and the combination of the four factors is expected to improve identification or prediction of SAPIP.

Acute Pancreatitis in Pregnancy: A Propensity Score Matching Analysis and Dynamic Nomogram for Risk Assessment

BACKGROUND

Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6-18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment.

OBJECTIVE

The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish, and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients.

STUDY DESIGN

Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The dataset after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated.

RESULTS

A total of 111 APIP were included. In all APIP patients, hyperlipidemic pancreatitis was the most important reason. The levels of serum amylase, creatinine, albumin, triglyceride, high-density lipoprotein cholesterol, and apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, and the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability.

CONCLUSION

The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability.

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP). Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed. Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937-0.990) and 0.862 (95% CI 0.742-0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways. Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

Defined, low threshold for caesarean section and multidisciplinary team management improves fetal outcome from acute pancreatitis in pregnancy

BACKGROUND

Acute pancreatitis in pregnancy (APIP) is associated with increased maternal and fetal mortality.

OBJECTIVES

We sought to determine whether a low threshold for cesarean section (C-section) in severe acute pancreatitis (SAP) or Predict SAP improves maternal and fetal outcomes in patients with APIP.

METHODS

We identified patients with APIP at a single institution from a prospective database and studied fetal and maternal health in APIP before (2005-2014) and after (2015-2019) introduction of multidisciplinary team management with a defined, lowered threshold for C-section. The primary end point was fetal mortality comprising abortion and perinatal death. Risk factors associated with fetal mortality were analyzed by univariable and multivariable logistic regression analysis.

RESULTS

A total of 165 patients with APIP were eligible for analysis. There was a highly significant increase in patients undergoing C-section from 37 (30.8%) of 120 during 2005-2014 to 27 (60%) of 45 in 2015-2019 (P = 0.001), with a highly significant fall in fetal mortality from 37 (30.8%) of 120 to 3 (6.7%) of 45 between the same periods (P = 0.001), when maternal mortality fell from 6 to zero (P = 0.19). Maternal early systemic inflammatory response syndrome (SIRS) (odds ratio [OR] 6.98, 95% confidence interval [CI] 1.53, 30.80, P = 0.01) and SAP (OR 3.64, 95%CI 1.25, 10.60, P = 0.02) were two independent risk factors associated with fetal mortality.

CONCLUSIONS

Multidisciplinary collaboration and a defined, low threshold for C-section improve fetal outcomes in patients with APIP.

Acute pancreatitis in pregnancy: a 10-year, multi-center, retrospective study in Beijing

OBJECTIVE

Acute pancreatitis in pregnancy (APIP) is a rare and serious complication during pregnancy. It has acute onset and is difficult to diagnose and treat. The aim of the present study was to describe the etiology, clinical manifestations, and maternofetal outcomes of APIP.

METHODS

We retrospectively reviewed 32 pregnant women who were treated at three tertiary care hospitals in Beijing, China. The correlation between the causes of APIP, severity, laboratory indices, and outcomes was analyzed.

RESULTS

The most common causes of APIP were hypertriglyceridemia (56.2%,18/32) and gallstones (28.1%, 9/32). Hypertriglyceridemia-induced APIP was associated with a higher rate of severe acute pancreatitis (P = 0.025). Serum level of triglycerides showed a positive correlation with the severity of APIP (P = 0.039). The most frequent presentation of APIP was abdominal pain (93.7%, 30/32). There were no maternal or fetal deaths in our study. Apgar scores at 1 min, 5 min, and 10 min of the premature neonates was correlated with the severity of APIP of the mother (P = 0.022; 0.002; 0.002).

CONCLUSION

High level of triglycerides may serve as a useful marker of the severity of APIP. The severity of APIP was associated with higher risk of neonate asphyxia. Appropriate timing of termination of pregnancy is a key imperative for APIP patients.