First trimester combined screening for fetal aneuploidies enhanced with additional ultrasound markers: an 8-year prospective study.

OBJECTIVES: To describe our screening population and audit of the performance of first-trimester screening for Down syndrome, based on a combined test, enhanced with additional ultrasound markers, over the whole period of the study. MATERIAL AND METHODS: We performed a prospective study from 2009 to 2016, which included 1358 singleton fetuses with a crown-rump length of 45-84 mm. The risk of aneuploidy was calculated using nuchal translucency, fetal heart rate (FHR), and additional markers, such as nasal bone (NB), tricuspid flow (TF) and ductus venosus (DV), combined with maternal serum free ß-human chorionic gonadotropin (fß-hCG) and pregnancy-associated plasma protein-A (PAPP-A). RESULTS: 87% of patients were evaluated using all the additional ultrasound markers and 97% of patients were assessed using at least two markers, in any combination. 70.5% of patients were also evaluated using maternal serum biochemistry. The most common risk calculation used nuchal translucency, FHR, all additional ultrasound markers, fß-hCG and PAPP-A in 851 (62.7%) of cases. The adjusted risk of trisomy 21 was greater than 1:100 in 65 (4.8%) women. Of these patients, 58 (87.7%) chose to have an invasive test. There were 24 aneuploid fetuses (1.7%); and from these we identified 12 (50%) trisomy 21, 6 (25%) sex chromosome anomalies, with the remainder being triploidy and trisomy 18/13. The combined test detected 11 of the 12 cases as having trisomy 21, with a first trimester detection rate of 91.7%. 39 fetuses (2.8%) had various types of structural anomalies. CONCLUSIONS: The combined test enhanced with all additional ultrasound markers did not show any substantial improvement in T21 detection rate, when compared with using only one of the additional markers.

Harnessing Code Interpreters for Enhanced Predictive Modeling: A Case Study on High-Density Lipoprotein Level Estimation in Romanian Diabetic Patients.

Diabetes is a condition accompanied by the alteration of body parameters, including those related to lipids like triglyceride (TG), low-density lipoproteins (LDLs), and high-density lipoproteins (HDLs). The latter are grouped under the term dyslipidemia and are considered a risk factor for cardiovascular events. In the present work, we analyzed the complex relationships between twelve parameters (disease status, age, sex, body mass index, systolic blood pressure, diastolic blood pressure, TG, HDL, LDL, glucose, HbA1c levels, and disease onset) of patients with diabetes from Romania. An initial prospective analysis showed that HDL is inversely correlated with most of the parameters; therefore, we further analyzed the dependence of HDLs on the other factors. The analysis was conducted with the Code Interpreter plugin of ChatGPT, which was used to build several models from which Random Forest performed best. The principal predictors of HDLs were TG, LDL, and HbA1c levels. Random Forest models were used to model all parameters, showing that blood pressure and HbA1c can be predicted based on the other parameters with the least error, while the less predictable parameters were TG and LDL levels. By conducting the present study using the ChatGPT Code Interpreter, we show that elaborate analysis methods are at hand and easy to apply by researchers with limited computational resources. The insight that can be gained from such an approach, such as what we obtained on HDL level predictors in diabetes, could be relevant for deriving novel management strategies and therapeutic approaches.