Pre-pregnancy body mass index combined with peripheral blood PLGF, DCN, LDH, and UA in a risk prediction model for pre-eclampsia.

Objective: This study analyzes the levels of peripheral blood placental growth factor (PLGF), body mass index (BMI), decorin (DCN), lactate dehydrogenase (LDH), uric acid (UA), and clinical indicators of patients with preeclampsia (PE), and establishes a predictive risk model of PE, which can provide a reference for early and effective prediction of PE. Methods: 81 cases of pregnant women with PE who had regular prenatal checkups and delivered in Jinshan Branch of Shanghai Sixth People's Hospital from June 2020 to December 2022 were analyzed, and 92 pregnant women with normal pregnancies who had their antenatal checkups and delivered at the hospital during the same period were selected as the control group. Clinical data and peripheral blood levels of PLGF, DCN, LDH, and UA were recorded, and the two groups were subjected to univariate screening and multifactorial logistic regression analysis. Based on the screening results, the diagnostic efficacy of PE was evaluated using the receiver operating characteristic (ROC) curve. Risk prediction nomogram model was constructed using R language. The Bootstrap method (self-sampling method) was used to validate and produce calibration plots; the decision curve analysis (DCA) was used to assess the clinical benefit rate of the model. Results: There were statistically significant differences in age, pre-pregnancy BMI, gestational weight gain, history of PE or family history, family history of hypertension, gestational diabetes mellitus, and history of renal disease between the two groups (P < 0.05). The results of multifactorial binary logistic stepwise regression revealed that peripheral blood levels of PLGF, DCN, LDH, UA, and pre-pregnancy BMI were independent influences on the occurrence of PE (P < 0.05). The area under the curve of PLGF, DCN, LDH, UA levels and pre-pregnancy BMI in the detection of PE was 0.952, with a sensitivity of 0.901 and a specificity of 0.913, which is better than a single clinical diagnostic indicator. The results of multifactor analysis were constructed as a nomogram model, and the mean absolute error of the calibration curve of the modeling set was 0.023, suggesting that the predictive probability of the model was generally compatible with the actual value. DCA showed the predictive model had a high net benefit in the range of 5% to 85%, suggesting that the model has clinical utility value. Conclusion: The occurrence of PE is related to the peripheral blood levels of PLGF, DCN, LDH, UA and pre-pregnancy BMI, and the combination of these indexes has a better clinical diagnostic value than a single index. The nomogram model constructed by using the above indicators can be used for the prediction of PE and has high predictive efficacy.

Serum lipoprotein and RBC rigidity index to predict cerebral infarction in patients with carotid artery stenosis.

OBJECTIVE: This study aims to determine the risk factors and to predict the occurrence of cerebral infarction in patients with carotid artery stenosis. METHODS: Two hundred and one subjects with carotid artery stenosis were retrospectively selected from Jinshan Branch of Shanghai Sixth People's Hospital, 115 cases of which with cerebral infarction and 86 without it. Clinical tests were performed including coagulation indices, fasting glucose, serum lipid, and blood rheology. Logistic regression analyses were used to identify the risk factors. Regression model was established, and receiver operating characteristic (ROC) curve was applied to analyze its diagnostic value. RESULTS: Our data indicated that apolipoprotein AI (OR = 0.051, 95% CI: 0.009-0.295), lipoprotein (a) (OR = 1.003, 95% CI: 1.001-1.005), and RBC rigidity index (OR = 0.383, 95% CI: 0.209-0.702) were independent risk factors. Area under the curve (AUC) of the regression model = 0.78, with the sensitivity of 73.9% (95% CI: 64.9%-81.7%) and specificity of 69.2% (95% CI: 52.4%-83.0%). Prediction probability was determined while logistic regression score >0.748 defaulted as high-risk status. High-risk ratios were 80% in progressive cerebral infarction and 72% in nonprogressive cerebral infarction (P > .05), respectively, while significant differences were found when both compared with controls (P < .001). CONCLUSIONS: We show herein that the regression model based on apolipoprotein AI, lipoprotein (a), and RBC IR is a promising tool to predict the occurrence of cerebral infarction in patients with carotid artery stenosis. However, identification of novel diagnostic markers for progressive cerebral infarction is still necessary.