Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy

Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics

OBJECTIVE

To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical characteristics with metabolomic and lipidomic profiles.

METHODS

This was a multicenter case-control study of patients with placenta previa with PAS (case group n=33) and previa alone (control group n=21). Maternal third-trimester plasma samples were collected and stored at -80°C. Untargeted metabolomic and targeted lipidomic assays were measured with flow-injection mass spectrometry. Univariate analysis provided an association of each lipid or metabolite with the outcome. The Benjamini-Hochberg procedure was used to control for the false discovery rate. Elastic net machine learning models were trained on patient characteristics to predict risk, and an integrated elastic net model of lipidome or metabolome with nine clinical features was trained. Performance using the area under the receiver operating characteristic curve (AUC) was determined with Monte Carlo cross-validation. Statistical significance was defined at P<.05.

RESULTS

The mean gestational age at sample collection was 33 3/7 weeks (case group) and 35 5/7 weeks (control group) (P<.01). In total, 786 lipid species and 2,605 metabolite features were evaluated. Univariate analysis revealed 31 lipids and 214 metabolites associated with the outcome (P<.05). After false discovery rate adjustment, these associations no longer remained statistically significant. When the machine learning model was applied, prediction of PAS with only clinical characteristics (AUC 0.685, 95% CI, 0.65-0.72) performed similarly to prediction with the lipidome model (AUC 0.699, 95% CI, 0.60-0.80) and the metabolome model (AUC 0.71, 95% CI, 0.66-0.76). However, integration of metabolome and lipidome with clinical features did not improve the model.

CONCLUSION

Metabolomic and lipidomic profiling performed similarly to, and not better than, clinical risk factors using machine learning to predict PAS among patients with PAS with previa and previa alone.

Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: Unveiling transformative capabilities and challenges

PURPOSE

This review aims to comprehensively explore the application of Artificial Intelligence (AI) to an area that has not been traditionally explored in depth: the continuum of maternal-fetal health. In doing so, the intent was to examine this physiologically continuous spectrum of mother and child health, as well as to highlight potential pitfalls, and suggest solutions for the same.

METHOD

A systematic search identified studies employing AI techniques for prediction, diagnosis, and decision support employing various modalities like imaging, electrophysiological signals and electronic health records in the domain of obstetrics and fetal health. In the selected articles then, AI applications in fetal morphology, gestational age assessment, congenital defect detection, fetal monitoring, placental analysis, and maternal physiological monitoring were critically examined both from the perspective of the domain and artificial intelligence.

RESULT

AI-driven solutions demonstrate promising capabilities in medical diagnostics and risk prediction, offering automation, improved accuracy, and the potential for personalized medicine. However, challenges regarding data availability, algorithmic transparency, and ethical considerations must be overcome to ensure responsible and effective clinical implementation. These challenges must be urgently addressed to ensure a domain as critical to public health as obstetrics and fetal health, is able to fully benefit from the gigantic strides made in the field of artificial intelligence.

CONCLUSION

Open access to relevant datasets is crucial for equitable progress in this critical public health domain. Integrating responsible and explainable AI, while addressing ethical considerations, is essential to maximize the public health benefits of AI-driven solutions in maternal-fetal care.

AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios

BACKGROUND

Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.

METHODS

Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not. We also sought to identify context-specific risk relationships among inter-related variables in FGR. Performance was assessed as area under the receiver-operating characteristics curve (AUC).

RESULTS

Feature selection identified the 16 most informative variables, which yielded a PGM with good overall performance in the validation cohort (AUC 0.83, 95% CI 0.79-0.87), including among "N of 1" unique scenarios (AUC 0.81, 0.72-0.90). Using the PGM, we identified FGR scenarios with a risk of perinatal morbidity no different from that of the cohort background (e.g. female fetus, estimated fetal weight (EFW) 3-9th percentile, no preexisting diabetes, no progesterone use; RR 0.9, 95% CI 0.7-1.1) alongside others that conferred a nearly 10-fold higher risk (female fetus, EFW 3-9th percentile, maternal preexisting diabetes, progesterone use; RR 9.8, 7.5-11.6). This led to the recognition of a PGM-identified latent interaction of fetal sex with preexisting diabetes, wherein the typical protective effect of female fetal sex was reversed in the presence of maternal diabetes.

CONCLUSIONS

PGMs are able to capture and quantify context-specific risk relationships in FGR and identify latent variable interactions that are associated with large differences in risk. FGR scenarios that are separated by nearly 10-fold perinatal morbidity risk would be managed similarly under current FGR clinical guidelines, highlighting the need for more precise approaches to risk estimation in FGR.

AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios

Background

Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.

Methods

Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not. We also sought to identify context-specific risk relationships among inter-related variables in FGR. Performance was assessed as area under the receiver-operating characteristics curve (AUC).

Results

Feature selection identified the 16 most informative variables, which yielded a PGM with good overall performance in the validation cohort (AUC 0.83, 95% CI 0.79-0.87), including among "N of 1" unique scenarios (AUC 0.81, 0.72-0.90). Using the PGM, we identified FGR scenarios with a risk of perinatal morbidity no different from that of the cohort background (e.g. female fetus, estimated fetal weight (EFW) 3-9th percentile, no preexisting diabetes, no progesterone use; RR 0.9, 95% CI 0.7-1.1) alongside others that conferred a nearly 10-fold higher risk (female fetus, EFW 3-9th percentile, maternal preexisting diabetes, progesterone use; RR 9.8, 7.5-11.6). This led to the recognition of a PGM-identified latent interaction of fetal sex with preexisting diabetes, wherein the typical protective effect of female fetal sex was reversed in the presence of maternal diabetes.

Conclusions

PGMs are able to capture and quantify context-specific risk relationships in FGR and identify latent variable interactions that are associated with large differences in risk. FGR scenarios that are separated by nearly 10-fold perinatal morbidity risk would be managed similarly under current FGR clinical guidelines, highlighting the need for more precise approaches to risk estimation in FGR.

Strategies for Safeguarding High-Risk Pregnancies From Preterm Birth: A Narrative Review

Preterm birth is the delivery of a live fetus before the 37th week of gestation. Preterm birth may stem from various factors, including premature rupture of membranes, spontaneous preterm labor, or medically induced circumstances. Premature delivery can result in serious and long-lasting difficulties even for infants who survive, as it is the leading cause of death for infants under five years old. Numerous nations have implemented initiatives to detect and track pregnant women who may give birth before their due date. Numerous therapies are available to protect these at-risk groups from the devastating effects of premature delivery, given the complex nature of preterm birth risk factors. Among the preventive measures, prophylactic progesterone appears to hold significant promise, while cervical cerclage proves effective in cases of cervical insufficiency. Conversely, pessaries show no discernible beneficial effects in reducing the risk of preterm birth. Regular antenatal visits are imperative for frequent patient evaluation and screening for potential risk factors. Adopting a healthy lifestyle can influence the risk of developing preeclampsia, with regular physical activity, a fiber-rich diet, and smoking cessation serving to mitigate the risk of preterm birth. The efficacy of bed rest in preventing preterm birth remains inconclusive due to insufficient evidence. This study aims to explore various preventive strategies for averting premature birth in high-risk women.