Evaluation of Maternal and Fetal Hemodynamic Alterations in Delivery in Epidural and Combined Spinal-Epidural Analgesia: A Randomized Clinical Trial

Background: The pain of vaginal delivery is considered as the worst experience in women life that negatively affects mother and fetus. The most important methods advised by anesthesiologists for pain reduction include epidural and combined spinal-epidural analgesia. The ideal method provides convenient pain relief and guarantees maternal and fetal safety, simultaneously. Fetal heart rate (FHR), fetal movement (FM), and maternal hemodynamics (i.e. blood pressure (BP), heart rate (HR), and SpO2) monitoring are the most available ways for controlling the fetus and mother’s conditions during the delivery process. Methods: This randomized-blinded clinical trial was performed on 100 pregnant women (50 cases in each group) during labor under epidural or combined spinal-epidural analgesia using lidocaine, fentanyl, and bupivacaine. FHR, FM, BP, HR, and SpO2 were monitored and recorded by blinded nurses. Data were analyzed by SPSS 22. Results: There were no significant differences in FHR, FM, and Apgar scores between the two groups. No significant difference was found between the two groups in maternal hemodynamics. Generally, FHR, maternal BP, and HR were in the normal ranges. The C/S rate was lower in the epidural group but not statistically significantly. Conclusions: In our survey, epidural and combined spinal-epidural analgesia were comparable in terms of FHR, FM, and maternal hemodynamics. Therefore, there is no priority in using each of the methods. The monitoring of FHR and maternal hemodynamics is essential during analgesia. It is suggested that further surveys evaluate the incidence and causes of C/S after analgesia

Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification

Background Acute pulmonary embolism (PE) is a critical medical emergency that necessitates prompt identification and intervention. Accurate prognostication of early mortality is vital for recognizing patients at elevated risk for unfavourable outcomes and administering suitable therapy. Machine learning (ML) algorithms hold promise for enhancing the precision of early mortality prediction in PE patients. Objective To devise an ML algorithm for early mortality prediction in PE patients by employing clinical and laboratory variables. Methods This study utilized diverse oversampling techniques to improve the performance of various machine learning models including ANN, SVM, DT, RF, and AdaBoost for early mortality prediction. Appropriate oversampling methods were chosen for each model based on algorithm characteristics and dataset properties. Predictor variables included four lab tests, eight physiological time series indicators, and two general descriptors. Evaluation used metrics like accuracy, F1_score, precision, recall, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves, providing a comprehensive view of models' predictive abilities. Results The findings indicated that the RF model with random oversampling exhibited superior performance among the five models assessed, achieving elevated accuracy and precision alongside high recall for predicting the death class. The oversampling approaches effectively equalized the sample distribution among the classes and enhanced the models' performance. Conclusions The suggested ML technique can efficiently prognosticate mortality in patients afflicted with acute PE. The RF model with random oversampling can aid healthcare professionals in making well-informed decisions regarding the treatment of patients with acute PE. The study underscores the significance of oversampling methods in managing imbalanced data and emphasizes the potential of ML algorithms in refining early mortality prediction for PE patients