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Published on: June 29, 2013
Prediction of pre-eclampsia complicated by fetal growth restriction and its perinatal outcome based on an artificial
Ke-Hua Huang1, Feng-Yi Chen2, Zhao-Zhen Liu1
1Department of Obstetrics and Gynecology, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Insights
Artificial neural networks (ANNs) can predict pre-eclampsia (PE) with fetal growth restriction (FGR) and adverse outcomes using maternal blood markers. This approach offers improved early detection and intervention for high-risk pregnancies.
Area of Science:
- Maternal-fetal medicine
- Artificial intelligence in healthcare
- Biomarker discovery
Background:
- Pre-eclampsia (PE) complicated by fetal growth restriction (FGR) significantly increases perinatal mortality, preterm birth, and neonatal asphyxia.
- Current ultrasound methods for FGR detection have limitations, including ignoring soft tissues and susceptibility to errors from section surface selection and fetal position.
- Early detection of FGR is challenging, often leading to delayed interventions.
Purpose of the Study:
- To assess the predictive value of maternal peripheral blood parameters and clinical indicators for PE complicated by FGR.
- To evaluate the effectiveness of an artificial neural network (ANN) model in predicting adverse perinatal outcomes in these high-risk pregnancies.
- To explore laboratory and clinical indicators for early prediction of FGR in PE cases.
Main Methods:
- A retrospective case-control study analyzed the correlation between maternal peripheral blood parameters and perinatal outcomes in pregnant patients with PE complicated by FGR.
- An artificial neural network (ANN) was constructed using 15 identified factors to predict PE complicated by FGR and adverse perinatal outcomes.
- Key factors included maternal age, BMI, inflammatory markers (neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio), coagulation parameters, lipid profiles, platelet indices, uric acid, lactate dehydrogenase, and total bile acids.
Main Results:
- The ANN model demonstrated significant accuracy in predicting PE complicated by FGR (84.3% accuracy, 97.7% sensitivity, 78% specificity).
- The model also showed good predictive capabilities for adverse outcomes: preterm births (76.3%), FGR severity (81.9%), premature rupture of membranes (80.3%), postpartum hemorrhage (80.1%), and fetal distress (77.6%).
- Fifteen maternal factors were identified as significantly correlated with PE complicated by FGR.
Conclusions:
- An artificial neural network model utilizing maternal peripheral blood parameters provides a valuable tool for predicting the occurrence of PE complicated by FGR.
- The ANN model effectively predicts adverse perinatal outcomes associated with PE and FGR, including the severity of FGR and preterm births.
- This AI-driven approach holds promise for earlier detection and improved management of high-risk pregnancies complicated by PE and FGR.
Abstract:
Objective: Pre-eclampsia (PE) complicated by fetal growth restriction (FGR) increases both perinatal mortality and the incidence of preterm birth and neonatal asphyxia. Because ultrasound measurements are bone markers, soft tissues, such as fetal fat and muscle, are ignored, and the selection of section surface and the influence of fetal position can lead to estimation errors. The early detection of FGR is not easy, resulting in a relative delay in intervention. It is assumed that FGR complicated with PE can be predicted by laboratory and clinical indicators. The present study adopts an artificial neural network (ANN) to assess the effect and predictive value of changes in maternal peripheral blood parameters and clinical indicators on the perinatal outcomes in patients with PE complicated by FGR. Methods: This study used a retrospective case-control approach. The correlation between maternal peripheral blood parameters and perinatal outcomes in pregnant patients with PE complicated by FGR was retrospectively analyzed, and an ANN was constructed to assess the value of the changes in maternal blood parameters in predicting the occurrence of PE complicated by FGR and adverse perinatal outcomes. Results: A total of 15 factors-maternal age, pre-pregnancy body mass index, inflammatory markers (neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio), coagulation parameters (prothrombin time and thrombin time), lipid parameters (high-density lipoprotein, low-density lipoprotein, and triglyceride counts), platelet parameters (mean platelet volume and plateletcrit), uric acid, lactate dehydrogenase, and total bile acids-were correlated with PE complicated by FGR. A total of six ANNs were constructed with the adoption of these parameters. The accuracy, sensitivity, and specificity of predicting the occurrence of the following diseases and adverse outcomes were respectively as follows: 84.3%, 97.7%, and 78% for PE complicated by FGR; 76.3%, 97.3%, and 68% for provider-initiated preterm births,; 81.9%, 97.2%, and 51% for predicting the severity of FGR; 80.3%, 92.9%, and 79% for premature rupture of membranes; 80.1%, 92.3%, and 79% for postpartum hemorrhage; and 77.6%, 92.3%, and 76% for fetal distress. Conclusion: An ANN model based on maternal peripheral blood parameters has a good predictive value for the occurrence of PE complicated by FGR and its adverse perinatal outcomes, such as the severity of FGR and preterm births in these patients.
