A new predicting model of preeclampsia based on peripheral blood test value

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Introduction

Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.

Aim

To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.

Methodology

Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

Current state

ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.

Future challenges

To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.

Conclusion

The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Development and evaluation of a nomogram for adverse outcomes of preeclampsia in Chinese pregnant women

Objective

Preeclampsia, the main cause of maternal and perinatal deaths, is associated with several maternal complications and adverse perinatal outcomes. Some prediction models are uesd to evaluate adverse pregnancy outcomes. However, some of the current prediction models are mainly carried out in developed countries, and many problems are still exist. We, thus, developed and validated a nomogram to predict the risk of adverse pregnancy outcomes of preeclampsia in Chinese pregnant women.

Methods

The clinical data of 720 pregnant women with preeclampsia in seven medical institutions in Chongqing from January 1, 2010, to December 31, 2020, were analyzed retrospectively. The patients were divided into two groups: 180 cases (25%) with adverse outcomes and 540 cases (75%) without adverse outcomes. The indicators were identified via univariate analysis. Logistic regression analysis was used to establish the prediction model, which was displayed by a nomogram. The performance of the nomogram was evaluated in terms of the area under the receiver operating characteristic (ROC) curve, calibration, and clinical utility.

Results

Univariate analysis showed that 24 indicators were significantly different (P < 0.05). Logistic regression analysis showed that gestational age, 24 h urine protein qualitative, and TT were significantly different (P < 0.05). The area under the ROC curve was 0.781 (95% CI 0.737–0.825) in training set and 0.777 (95% CI 0.689–0.865) in test set. The calibration curve of the nomogram showed good agreement between prediction and observation. The analysis of the clinical decision curve showed that the nomogram is of practical significance.

Conclusion

Our study identified gestational age, 24 h urine protein qualitative, and TT as risk factors for adverse outcomes of preeclampsia in pregnant women, and constructed a nomogram that can easily predict and evaluate the risk of adverse pregnancy outcomes in women with preeclampsia.

Expression and clinical significance of lncRNA TCL6 in serum of patients with preeclampsia

Preeclampsia is a common syndrome in pregnancy and a leading cause of mortality of pregnant females and their infants. To investigate the diagnostic and prognostic utility of lncRNA T-cell leukemia/lymphoma 6 (TCL6) in patients with preeclampsia, 120 singleton pregnant females with preeclampsia and another 100 healthy pregnant control subjects were analyzed in the present study. The expression of lncRNA TCL6 in the serum of the included patients was detected. Receiver operating characteristic curve analysis was applied to evaluate the efficiency of lncRNA TCL6 in terms of preeclampsia diagnosis and grading. Kaplan-Meier analysis was adopted to assess the effect of lncRNA TCL6 expression on the rate of adverse pregnancy. Multivariate logistic regression was used to determine high-risk factors of adverse pregnancy. The results indicated that lncRNA TCL6 was significantly increased in the serum of patients with preeclampsia. Furthermore, TCL6 was elevated in subgroups of patients with early-onset or severe preeclampsia and with Haemolysis, Elevated Liver enzymes and Low Platelet count syndrome in comparison with other patients with preeclampsia. High expression of TCL6 in pregnant females corresponded to a higher rate of adverse pregnancy outcomes. Severe preeclampsia, early-onset preeclampsia and high TCL6 expression were identified as independent risk factors for adverse pregnancy outcomes. For each unit increase in TCL6 expression, a 9.5-fold increase of the risk of adverse maternal and fetal outcomes was determined. Collectively, high expression of lncRNA TCL6 may assist the diagnosis and grading of preeclampsia and may be adopted as an independent risk factor for adverse pregnancy outcomes.

Changes in serum TG levels during pregnancy and their association with postpartum hypertriglyceridemia: a population-based prospective cohort study

BACKGROUND

Blood lipid increases during gestation are considered a physiological adaption, and decrease after delivery. However, some adverse pregnancy outcomes are thought to be related to gestational lipid levels. Therefore, it is necessary to have a reference range for lipid changes during gestation. The present study aims to describe triglyceride (TG) changes during pregnancy and 42 days postpartum and to find cut-off points for TG levels during the first, second, and third trimesters.

METHODS

A total of 908 pregnant women were followed from recruitment to 42 days postpartum, and their serum lipids were collected at gestational weeks 6-8, 16, 24, and 36 and 42 days postpartum. The major outcome was postpartum hypertriglyceridemia. The association between gestational and postpartum TG levels was analysed by stepwise multiple linear regression. A two-stage approach including a linear mixed-effect model and linear or logistic regression was conducted to explore the contribution of the changes in TG over time in pregnancy to postpartum hypertriglyceridemia. Logistic regression was constructed to examine the association between gestational TG levels and postpartum hypertriglyceridemia. Cut-off points were calculated by receiver operating characteristic (ROC) curves.

RESULTS

There was a tendency for serum TG to increase with gestational age and decrease at 42 days postpartum. Prepregnancy overweight, obesity, and GDM intensified this elevation. Higher TG levels at gestational weeks 6-8, 16, 24, and 36 were positively associated with a higher risk of postpartum hypertriglyceridemia [OR 4.962, 95 % CI (3.007-8.189); OR 2.076, 95 % CI (1.303-3.309); OR 1.563, 95 % CI (1.092-2.236); and OR 1.534, 95 % CI (1.208-1.946), respectively]. The trend of the change in TG over time was positively associated with the TG level and risk of postpartum hypertriglyceridemia [OR 11.660, 95 % CI (6.018-22.591)]. Based on ROC curves, the cut-off points of serum TG levels were 1.93, 2.35, and 3.08 mmol/L at gestational weeks 16, 24, and 36, respectively. Stratified analysis of prepregnancy body mass index (pre-BMI) and GDM showed that higher gestational TG was a risk factor for postpartum hypertriglyceridemia in women with normal pre-BMI and without GDM.

CONCLUSIONS

Gestational TG and its elevation were risk and predictive factors of postpartum hypertriglyceridemia, especially in pregnant women with normal pre-BMI or without GDM.