Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review

Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study

Preterm birth (PTB), defined as delivery before 37 weeks, affects 15 million infants annually, accounting for 11% of live births and over 35% of neonatal deaths. While advanced maternal age (≥ 35 years) is a known risk factor, PTB risk in women under 35 is underexplored. This study aimed to develop a machine learning-based model for PTB prediction in women under 35. A retrospective cohort of 2606 cases (2019-2022) equally split between full-term and preterm births was analyzed. Logistic Regression, LightGBM, Gradient Boosting Decision Tree (GBDT), and XGBoost models were evaluated. External validation was conducted using 803 independent cases (2023). Model performance was assessed using area under the curve (AUC), accuracy, sensitivity, and specificity. SHAP (SHapley Additive exPlanations) values were used to interpret model predictions. The XGBoost model demonstrated superior performance with an AUC of 0.893 (95% CI: 0.860-0.925) on the validation set. In comparison, Logistic Regression, LightGBM, and GBDT achieved AUCs of 0.872, 0.840, and 0.879, respectively. External validation of the XGBoost model yielded an AUC of 0.91 (95% CI: 0.889-0.931). SHAP analysis highlighted seven key predictors: alkaline phosphatase (ALP), alpha-fetoprotein (AFP), hemoglobin (HGB), urea (UREA), lymphocyte count (Lym1), sodium (Na), and red cell distribution width coefficient of variation (RDWCV). The XGBoost model provides accurate PTB risk prediction and key insights for early intervention in women under 35, supporting its potential clinical utility.

Predicting preterm birth using machine learning methods

Preterm birth is a significant public health concern, given its correlation with neonatal mortality and morbidity. The aetiology of preterm birth is complex and multifactorial. The objective of this study was to develop and compare machine learning models for predicting the risk of preterm birth. Data were collected from 50 patients in a maternity ward, with an analysis performed based on the timing of delivery (preterm vs. term). The applicability of XGBoost, CatBoost, logistic regression, support vector machines (SVM), and decision trees for predicting preterm delivery was evaluated through training. The linear SVM with boosted parameters demonstrated the highest performance, achieving an accuracy of 82%, precision of 83%, recall of 86%, and an F1-score of 84%. The logistic regression model, also boosted, demonstrated comparable performance to the linear SVM, with similar accuracy (80%), precision (82%), recall (82%), and F1-score (82%). The performance of other models, including decision trees and more complex algorithms, was inferior, which is likely attributable to the limited dataset and the number of parameters involved. In particular, machine learning models, most notably the linear SVM, can be effectively employed to assess the risk of preterm birth. The findings indicate that the linear SVM model exhibits the greatest efficacy among the tested models.

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study

OBJECTIVES

To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites.

DESIGN

Case-cohort design within a prospective cohort study.

SETTING

Cambridge, UK.

POPULATION OR SAMPLE

A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB.

METHODS

An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB.

MAIN OUTCOME MEASURES

sPTB and sETB.

RESULTS

We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860).

CONCLUSIONS

We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.

Cervical elastography: finding a novel predictor for improving the prediction of preterm birth in uncomplicated twin pregnancies

OBJECTIVE

This study set out to investigate a novel ultrasound parameter using cervical elastosonography for improving the prediction of spontaneous preterm birth (sPTB) in twin pregnancies.

STUDY DESIGN

The study was comprised of 106 twin pregnancies from October 2020 to January 2022 in Beijing Obstetrics and Gynecology Hospital. They were divided into two groups according to gestational age (GA) at delivery (delivery < 35 weeks and delivery ≥ 35 weeks). There were five elastographic parameters: Elasticity Contrast Index (ECI), Cervical Hardness Ratio (CHR), Closed Internal cervical ostium Strain rate (CIS); External cervical ostium strain rate (ES), CIS/ES ratio and Cervical Length (CL). All of the clinical and ultrasonic indicators with P < 0.1 were considered candidate indicators via univariate logistic regression. Based on the extracted unified combination of clinical indicators, the combinations of permutation with the candidate ultrasound indicators were performed step by step in multivariable logistic regression. The best ultrasound indicator with the lowest Akaike Information Criterion (AIC) and the highest Areas Under the receiver operating characteristic Curve (AUC) was chosen for establishing the prediction score.

RESULTS

Over 30% (36/106) of those who delivered before 35 weeks gestation. There were distinct differences in the clinical characteristics and cervical elastography parameters between the two groups. Seven major clinical variables were identified as a unified clinical indicator. CISmin as the best ultrasound elastography predictor indicated the lowest AIC and the highest AUC and outperformed alternative indicators significantly in the prediction of delivery before 35 weeks of gestation. Unfortunately, CLmin which was commonly used in clinical practice ranked far from all of the cervical elastography parameters and presented the highest AIC and the lowest AUC. A preliminary scoring rule was established and the ability to predict the risk of sPTB in twin pregnancies was improved (Accuracy: 0.896 vs 0.877; AIC: 81.494 vs 91.698; AUC: 0.923 vs 0.906).

CONCLUSIONS

The cervical elastosonography predictor such as CISmin might be a more useful indicator applied for enhancing the ability in predicting twin pregnancies preterm birth than CL. Furthermore, there would be more benefits for advancing clinical decision-making in actual clinical practice by using cervical elastosonography in the near future.

Evaluating the Efficacy of Cervical Tactile Ultrasound Technique as a Predictive Tool for Spontaneous Preterm Birth

Background

Premature cervical softening and shortening may be considered an early mechanical failure that predispose to preterm birth.

Purpose

This study aims to explore the applicability of an innovative cervical tactile ultrasound approach for predicting spontaneous preterm birth (sPTB).

Materials and Methods

Eligible participants were women with low-risk singleton pregnancies in their second trimester, enrolled in this prospective observational study. A Cervix Monitor (CM) device was designed with a vaginal probe comprising four tactile sensors and a single ultrasound transducer operating at 5 MHz. The probe enabled the application of controllable pressure to the external cervical surface, facilitating the acquisition of stress-strain data from both anterior and posterior cervical sectors. Gestational age at delivery was recorded and compared against cervical elasticity.

Results

CM examination data were analyzed for 127 women at 240/7 - 286/7 gestational weeks. sPTB was observed in 6.3% of the cases. The preterm group exhibited a lower average cervical stress-to-strain ratio (elasticity) of 0.70 ± 0.26 kPa/mm compared to the term group's 1.63 ± 0.65 kPa/mm with a p-value of 1.1 × 10-4. Diagnostic accuracy for predicting spontaneous preterm birth based solely on cervical elasticity data was found to be 95.0% (95% CI, 88.5 - 100.0).

Conclusion

These findings suggest that measuring cervical elasticity with the designed tactile ultrasound probe has the potential to predict spontaneous preterm birth in a cost-effective manner.

Current status of the position on labor progress prediction for contemporary pregnant women using Friedman curves: An updated review

AIM

Prediction of labor progression is important for maternal and fetal health, as improved accuracy can lead to more timely intervention and improved outcomes. This review aims to outline the importance of predicting the progression of spontaneous parturition, detail the various methods employed to enhance this prediction and provide recommendations for future research.

METHODS

We searched articles relating to labor progression and systematic review articles on Artificial Inteligence (AI) in childbirth management using PubMed. To supplement, Google Scholar was used to find recent guidelines and related documents.

RESULTS

Traditional methods like vaginal examinations, criticized for subjectivity and inaccuracy, are gradually being replaced by ultrasound, considered a more objective and accurate approach. Further advancements have been observed with machine learning and artificial intelligence techniques, which promise to surpass the accuracies of conventional methods. The Friedman curve, developed in 1954, is the standard for assessing labor progress, but its application to Asian women, in particular, remains controversial, and various studies have reported that the actual rate of labor was slower than that indicated by the Friedman curve.

CONCLUSION

There is a need to innovate methodologies for predicting delivery tailored to modern pregnant women, especially when they have different genetic and cultural backgrounds than their Western counterparts, such as Asians. Future research should develop predictive models of labor progression that aim to enhance medical intervention and improve the safety and well-being of both mother and child.

Establishment of a model for predicting preterm birth based on the machine learning algorithm

BACKGROUND

The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future.

METHODS

This was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis. In this study, a logical regression model, decision tree, Naive Bayes, support vector machine, and AdaBoost are used to construct the prediction model. Accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified.

RESULTS

A total of 5411 participants were included and were used for model construction. AdaBoost model has the best prediction ability among the five models. The accuracy of the model for the prediction of "non-preterm birth" was the highest, reaching 100%, and that of "preterm birth" was 72.73%.

CONCLUSIONS

By constructing a preterm birth prediction model based on electronic health records, we believe that machine algorithms have great potential for preterm birth identification. However, more relevant studies are needed before its application in the clinic.