Background: Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations.
Methods: The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance.
Results: Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72-0.74, accuracy between 0.73-0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82.
Conclusions: The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.
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http://dx.doi.org/10.1186/s12884-023-05766-4 | DOI Listing |
Diabetol Metab Syndr
January 2025
Department of Cardiology, Wuhan Third Hospital & Tongren Hospital of Wuhan University, Wuhan, 430074, Hubei, China.
Background: The triglyceride glucose-body mass index (TyG-BMI) is considered to be a reliable surrogate marker of insulin resistance (IR). However, limited evidence exists regarding its association with the severity of coronary artery disease (CAD), particularly in hypertensive patients with different glucose metabolic states, including those with H-type hypertension. This study aimed to investigate the relationship between TyG-BMI and CAD severity across different glucose metabolism conditions.
View Article and Find Full Text PDFBMC Musculoskelet Disord
January 2025
Department of Orthopaedics and Traumatology, Ankara Bilkent City Hospital, University of Health Sciences, Ankara, Turkey.
Background: Artcure diffusional patch (ADP) is a novel transdermal therapeutic system that started to be used in the last decade for lumbar disc herniation (LDH). Previous studies have reported early results of the therapy. In this study, we aimed to evaluate the medium- to long-term functional outcomes of this treatment in LDH patients and examine factors predicting the need for surgery after treatment.
View Article and Find Full Text PDFJ Pediatr Endocrinol Metab
January 2025
Division of Gastroenterology, Hepatology, & Nutrition, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Objectives: The association of celiac disease (CD) in type 1 diabetes mellitus (T1DM) is well-established, yet variation exists in screening practices. This study measures the accuracy of early screening with tissue transglutaminase Immunoglobulin A (TTG-IgA) and endomysial antibody (EMA) in newly diagnosed T1DM.
Methods: This is a retrospective study of children with T1DM between 2013 and 2019 with early CD screening and follow-up.
Neurocrit Care
January 2025
Department of Anesthesia, Intensive Care, and Pain Management, Faculty of Medicine, Zagazig University, Zagazig, Egypt.
Background: Ultrasonographic optic nerve sheath diameter (ONSD) is a satisfactory noninvasive intracranial pressure (ICP) monitoring test. Our aim was to evaluate ONSD as an objective screening tool to predict and diagnose ICP changes early in sepsis-associated encephalopathy (SAE).
Methods: Our prospective observational study was conducted on patients with sepsis, and after intensive care unit (ICU) admission, the time to diagnose SAE was recorded, and patients were divided into a non-SAE group including conscious patients with sepsis and a SAE group including patients with sepsis with acute onset of disturbed conscious level.
Bone Marrow Transplant
January 2025
Division of Pediatric Hematology/Oncology, Department of Pediatrics, Asan Medical Center Children's Hospital, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Transplant-associated thrombotic microangiopathy (TA-TMA) is an increasingly recognized complication in hematopoietic cell transplantation (HCT). Given the rarity of prospective pediatric studies on TA-TMA, this study aimed to evaluate the incidence, survival outcomes, and risk factors for predicting early the development of TA-TMA in a pediatric population following allogeneic HCT. We conducted a prospective analysis of 173 pediatric patients to evaluate the incidence, survival outcome, and risk factors of TA-TMA.
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