ABSTRACT: Acute pancreatitis in pregnancy (APIP) is a rare but dangerous complication. APIP has common symptoms with acute abdomen. Assessment of an acute abdomen is more complicated during pregnancy because the gravid uterus could mask most of symptomatic signs. It has been a challenge to diagnose APIP by physical examination or diagnostic imaging. Case studies on APIP are also limited for analysis on the risk factors associated with the disease. This retrospective study evaluated a series of risk factors from a relatively substantial number of APIP cases to determine early predictors or prognosis markers for APIP.Fifty-nine APIP patients together with 179 random normal pregnant women in Shengjing Affiliated Hospital of China Medical University were included for this retrospective study. Medical parameters of blood test in biochemistry and hematology were compared between 2 groups using t test. Multivariate logistic regression analysis was performed to investigate the relationship between various factors and APIP using Statistical Applied Software (SAS student version).Compared with normal pregnant women, APIP patients have elevated values in alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen, creatinine, C-reactive protein, direct bilirubin, fibrin degradation products, gamma-glutamyl transpeptidase (GGT), glucose, lipase, pH and decreased values in albumin, fibrinogen, high-density lipoprotein (HDL), hemoglobin, low-density lipoprotein cholesterol (LDL-D), and total proteins from their blood tests. In addition, APIP patients have decreased numbers in red cells but increased numbers in white blood cells and increased ratio of neutrophil/lymphocyte (N/L). Among these factors, N/LR, GGT, lipase, and HDL are significantly associated with APIP. This study suggests that the combination of those factors serve as a panel of indicators for early-onset prognosis of APIP.GGT, lipase, HDL, and N/LR can serve as a panel of factors to predict APIP. More case studies are important to further evaluate the predicting power of this panel factors in APIP.
