Infant Cardiac Orienting Responses Predict Later FASD in the Preschool Period

Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review

Fetal alcohol spectrum disorders (FASDs) affect at least 0.8% of the population globally. The diagnosis of FASD is uniquely complex, with a heterogeneous physical and neurobehavioral presentation that requires multidisciplinary expertise for diagnosis. Many researchers have begun to incorporate machine learning approaches into FASD research to identify children who are affected by prenatal alcohol exposure, including those with FASD. This narrative review highlights these efforts. Following an introduction to machine learning, we summarize examples from the literature of neurobehavioral screening tools and physiologic markers of exposure. We discuss individual efforts, including models that classify FASD based on parent-reported neurocognitive or behavioral questionnaires, 3D facial imaging, brain imaging, DNA methylation patterns, microRNA profiles, cardiac orienting response, and dysmorphic facial features. We highlight model performance and discuss the limitations of these approaches. We conclude by considering the scalability of these approaches and how these machine learning models, largely developed from clinical samples or highly exposed birth cohorts, may perform in the general population.

Predicting fetal alcohol spectrum disorders in preschool-aged children from early life factors

BACKGROUND

Early life factors, including parental sociodemographic characteristics, pregnancy exposures, and physical and neurodevelopmental features measured in infancy are associated with fetal alcohol spectrum disorders (FASD). The objective of this study was to evaluate the performance of a classifier model for diagnosing FASD in preschool-aged children from pregnancy and infancy-related characteristics.

METHODS

We analyzed a prospective pregnancy cohort in Western Ukraine enrolled between 2008 and 2014. Maternal and paternal sociodemographic factors, maternal prenatal alcohol use and smoking behaviors, reproductive characteristics, birth outcomes, infant alcohol-related dysmorphic and physical features, and infant neurodevelopmental outcomes were used to predict FASD. Data were split into separate training (80%: n = 245) and test (20%: n = 58; 11 FASD, 47 no FASD) datasets. Training data were balanced using data augmentation through a synthetic minority oversampling technique. Four classifier models (random forest, extreme gradient boosting [XGBoost], logistic regression [full model] and backward stepwise logistic regression) were evaluated for accuracy, sensitivity, and specificity in the hold-out sample.

RESULTS

Of 306 children evaluated for FASD, 61 had a diagnosis. Random forest models had the highest sensitivity (0.54), with accuracy of 0.86 (95% CI: 0.74, 0.94) in hold-out data. Boosted gradient models performed similarly, however, sensitivity was less than 50%. The full logistic regression model performed poorly (sensitivity = 0.18 and accuracy = 0.65), while stepwise logistic regression performed similarly to the boosted gradient model but with lower specificity. In a hold-out sample, the best performing algorithm correctly classified six of 11 children with FASD, and 44 of 47 children without FASD.

CONCLUSIONS

As early identification and treatment optimize outcomes of children with FASD, classifier models from early life characteristics show promise in predicting FASD. Models may be improved through the inclusion of physiologic markers of prenatal alcohol exposure and should be tested in different samples.

Identifying Prenatal Alcohol Exposure and Children Affected by It: A Review of Biomarkers and Screening Tools

PURPOSE

Early identification of prenatal alcohol exposure (PAE) and of those in need of services resulting from this exposure is an important public health concern. This study reviewed the existing literature on potential biomarkers and screening tools of PAE and its impact.

SEARCH METHODS

Electronic databases were searched for articles published between January 1, 1996, and November 30, 2021, using the following search terms: ("fetal alcohol" or "prenatal alcohol" or "FASD" or "alcohol-related neurodevelopmental disorder" or "ARND" or "ND-PAE") and ("screening" or "identification" or "biomarker"). Duplicate articles were electronically eliminated. Titles and abstracts were reviewed for appropriateness, and selected articles were retrieved for further analysis. Additional articles were added that were referenced in the reviewed articles or identified from expert knowledge. Information about the characteristics of the sample, the biomarker or screening tool, and the predictive validity outcome data were abstracted. A narrative analysis of the studies was then performed on the data.

SEARCH RESULTS

A total of 3,813 articles were initially identified, and 1,215 were removed as duplicates. Of the remaining articles, 182 were identified as being within the scope of the review based on title and abstract inspection, and 181 articles were successfully retrieved. Of these, additional articles were removed because they were preclinical (3), were descriptive only (13), included only self-report of PAE (42), included only mean group comparison (17), were additional duplicates (2), focused on cost analysis (9), missed predictive validity data (24), or for other reasons (23). The remaining articles (n = 48) were abstracted. An additional 13 manuscripts were identified from these articles, and two more from expert knowledge. A total of 63 articles contributed to the review.

DISCUSSION AND CONCLUSIONS

Biomarkers and screening tools of PAE and its impact fall short of ideal predictive validity characteristics. Higher specificity than sensitivity was found for many of the biomarkers and screening tools used to identify PAE and its impact, suggesting that current methods continue to under-identify the full range of individuals impacted by PAE. Exceptions to this were found in recent investigations using microRNAs related to growth and vascular development, proteomic changes associated with PAE, and combinations of markers estimating levels of various cytokines. Replications of these findings are needed across other samples to confirm the limited data available. Future research on biomarkers and screening tools should attend to feasibility and scalability of implementation. This article also recommends a systematic process of evaluation to improve early identification of individuals impacted by PAE so that harm reduction and habilitative care efforts can be implemented.

Alcohol-related dysmorphic features as predictors of neurodevelopmental delay in infants and preschool-aged children: Results from a birth cohort in Ukraine

BACKGROUND

Cardinal and non-cardinal dysmorphic features are associated with prenatal alcohol exposure (PAE); however, their association with neurodevelopment is less clear. The objective of this study was to determine whether alcohol-related dysmorphic features predict neurodevelopmental delay in infants and toddlers.

METHODS

We analyzed a prospective pregnancy cohort in western Ukraine enrolled between 2008 and 2014. A dysmorphology examination comprising body size and three cardinal and 14 non-cardinal dysmorphic features was performed at approximately 6 to 12 months of age. PAE was self-reported and operationalized as absolute ounces of alcohol per day around the time of conception. Neurodevelopment was assessed at 6 to 12 months with the Bayley Scales of Infant Development-II (BSID-II), and at 3.5 to 4.5 years of age with the Differential Ability Scales-II, the Child Behavior Checklist, and multiple measures that were used to create an executive functioning factor score. We performed logistic regression to predict children's neurodevelopment from dysmorphic features, growth measures, sex, and PAE.

RESULTS

From an analytic sample of 582 unique children, 566 had BSID-II scores in infancy, and 289 completed the preschool battery. Models with all cardinal and non-cardinal dysmorphic features, growth measures, sex, and PAE performed better than models with subsets of those inputs. In general, models had poor performance classifying delays in infancy (area under the curve (AUC) <0.7) and acceptable performance on preschool-aged outcomes (AUC ~0.75). When the sample was limited to children with moderate-to-high PAE, predictive ability improved on preschool-aged outcomes (AUC 0.76 to 0.89). Sensitivity was relatively low for all models (12% to 63%), although other metrics of performance were higher.

CONCLUSION

Predictive analysis based on dysmorphic features and measures of growth performed modestly in this sample. As these features are more reliably measured than neurodevelopment at an earlier age, the inclusion of dysmorphic features and measures of growth in predictive models should be further explored and validated in different settings and populations.

Wireless Heart Sensor for Capturing Cardiac Orienting Response for Prediction of Neurodevelopmental Delay in Infants

Early identification of infants at risk of neurodevelopmental delay is an essential public health aim. Such a diagnosis allows early interventions for infants that maximally take advantage of the neural plasticity in the developing brain. Using standardized physiological developmental tests, such as the assessment of neurophysiological response to environmental events using cardiac orienting responses (CORs), is a promising and effective approach for early recognition of neurodevelopmental delay. Previous CORs have been collected on children using large bulky equipment that would not be feasible for widespread screening in routine clinical visits. We developed a portable wireless electrocardiogram (ECG) system along with a custom application for IOS tablets that, in tandem, can extract CORs with sufficient physiologic and timing accuracy to reflect the well-characterized ECG response to both auditory and visual stimuli. The sensor described here serves as an initial step in determining the extent to which COR tools are cost-effective for the early screening of children to determine who is at risk of developing neurocognitive deficits and may benefit from early interventions. We demonstrated that our approach, based on a wireless heartbeat sensor system and a custom mobile application for stimulus display and data recording, is sufficient to capture CORs from infants. The COR monitoring approach described here with mobile technology is an example of a desired standardized physiologic assessment that is a cost-and-time efficient, scalable method for early recognition of neurodevelopmental delay.

Systematic review of health and disease in Ukrainian children highlights poor child health and challenges for those treating refugees

Aim

Millions of Ukrainian children have been internally displaced or fled to other countries because of the Russian war. This systematic review focused on their health needs and future challenges for clinicians.

Methods

A systematic literature search of the Medline, Embase and MedRxiv databases from 1 January 2010 to 31 March 2022 identified 1628 papers on the health of Ukrainian children and 112 were relevant to this review.

Results

In 2019, under‐5 mortality was 8 per 1000 live births in Ukraine. Underweight and adverse childhood experiences, including child abuse, were frequent compared to other European countries, while childhood obesity seemed less common. Alcohol consumption was common in women of reproductive age, including during pregnancy, risking foetal alcohol syndrome. Neonatal screening programmes provided low coverage. Vaccine hesitancy was common and vaccination rates were low. Other concerns were measles, HIV, antibiotic resistance and multi‐resistant tuberculosis. Many children are expected to suffer from psychological and physical trauma due to the war. Other healthcare challenges include low COVID‐19 vaccination rates and a preference for secondary and tertiary care, rather than primary care. Many people cannot afford medication.

Conclusion

Ukrainian children often have poor health and host countries need to be aware of their needs.