Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy

The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

Routine screening for gestational diabetes: a review

PURPOSE OF REVIEW

Rates of gestational diabetes mellitus (GDM) throughout the world continue to increase associated with the increasing rates of obesity. Given this epidemiologic burden, the importance of proper screening, diagnosis, and management cannot be understated. This review focuses on the current screening guidelines utilized throughout the world and new data recently published regarding the most optimal screening techniques and future directions for research.

RECENT FINDINGS

Despite unanimous opinion that GDM warrants screening, the optimal screening regimen remains controversial. Notably, in the United States per the consensus recommendation by the American College of Obstetrics and Gynecology and the Society for Maternal-Fetal Medicine, a 2-step screening approach is often used. Recently, there have been multiple studies published that have compared the 1-step and 2-step screening process with respect to GDM incidence and perinatal outcomes. These new findings are summarized below.

SUMMARY

Utilization of the 1-step screening as opposed to the 2-step screening results in an increased diagnosis of GDM without significant population level benefit in outcomes. However, these studies remain underpowered to allow for meaningful comparison of outcomes in those diagnosed with GDM.