Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

Abstract

Acute fatty liver of pregnancy (AFLP) is a rare but potentially life-threatening hepatic disorder that leads to considerable maternal and fetal mortality. To explore the risk factors for maternal and fetal mortality in AFLP and develop new predictive models, through this retrospective study, we analyzed the demographic characteristics, clinical symptoms, and laboratory findings of 106 patients with AFLP who were admitted to Shandong Provincial Hospital. Risk factors for maternal and fetal mortality were analyzed by univariate and multivariate logistic regression analysis. The new models based on the multivariate logistic regression analysis and the model for end-stage liver disease (MELD) were tested in AFLP. The receiver operating characteristic curve (ROC) was applied to compare the predictive efficiency, sensitivity, and specificity of the two models. Prenatal nausea (p = 0.037), prolonged prothrombin time (p = 0.003), and elevated serum creatinine (p = 0.003) were independent risk factors for maternal mortality. The ROC curve showed that the area under the curve (AUC) of the MELD was 0.948, with a sensitivity of 100% and a specificity of 83.3%. The AUC of the new model for maternal mortality was 0.926, with a sensitivity of 90% and a specificity of 94.8%. Hepatic encephalopathy (p = 0.016) and thrombocytopenia (p = 0.001) were independent risk factors for fetal mortality. Using the ROC curve, the AUC of the MELD was 0.694, yielding a sensitivity of 68.8% and a specificity of 64.4%. The AUC of the new model for fetal mortality was 0.893, yielding a sensitivity of 100% and a specificity of 73.3%. Both the new predictive model for maternal mortality and the MELD showed good predictive efficacy for maternal mortality in patients with AFLP (AUC = 0.926 and 0.948, respectively), and the new predictive model for fetal mortality was superior to the MELD in predicting fetal mortality (AUC = 0.893 and 0.694, respectively). The two new predictive models were more readily available, less expensive, and easier to implement clinically, especially in low-income countries.