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Introduction: Hypotension is common after spinal anaesthesia during caesarean section (c/s). Methods for prevention of hypotension are mechanical approaches such as leg rise, compression stocks and positioning. On the other hand, mother position may have an effect on haemodynamic variables due to speed of onset of sensory block. Position during induction has maternal and foetal importance. Aim: To compare the maternal haemodynamic variables after spinal anaesthesia in sitting or lateral decubitus position in patients undergoing c/s. Materials and Methods: In this prospective, randomised and double-blinded clinical trial, 76 healthy parturient, undergoing c/s, were allocated in two groups. The study was conducted from September 2014 to August 2015 at Al-Zahra Hospital. Spinal anaesthesia was induced in lateral decubitus position (study group; n=38) or sitting position (control group; n=38). Maternal haemodynamic, block characteristic, side effects, and neonate Apgar scores were recorded. Data were analysed using SPSS version 16.0 software and student's t-test, Chi-square test, and Mann Whitney U test were used for statistical analysis. Results: Incidence of hypotension (50% vs 76.3%; p=0.016), bradycardia (0% vs 21.1%; p=0.014) and vasopressors consumption (36.2% vs 76.3%; p=0.012) were statistically lower in lateral position. There was no significant differences in sensory height (p=0.89) and duration of sensory and motor block between two groups (p=0.42, p=0.29; respectively). Conclusion: The changes in maternal haemodynamic were significantly lower in lateral position than sitting position in patients undergoing spinal anaesthesia for c/s.

Background: The coronavirus disease (COVID-19) pandemic has made a great impact on health-care services. The prognosis of the severity of the disease help reduces mortality by prioritizing the allocation of hospital resources. Early mortality prediction of this disease through paramount biomarkers is the main aim of this study., Materials and Methods: In this retrospective study, a total of 205 confirmed COVID-19 patients hospitalized from June 2020 to March 2021 were included. Demographic data, important blood biomarkers levels, and patient outcomes were investigated using the machine learning and statistical tools., Results: Random forests, as the best model of mortality prediction, (Matthews correlation coefficient = 0.514), were employed to find the most relevant dataset feature associated with mortality. Aspartate aminotransferase (AST) and blood urea nitrogen (BUN) were identified as important death-related features. The decision tree method was identified the cutoff value of BUN >47 mg/dL and AST >44 U/L as decision boundaries of mortality (sensitivity = 0.4). Data mining results were compared with those obtained through the statistical tests. Statistical analyses were also determined these two factors as the most significant ones with P values of 4.4 × 10 -7 and 1.6 × 10 -6 , respectively. The demographic trait of age and some hematological (thrombocytopenia, increased white blood cell count, neutrophils [%], RDW-CV and RDW-SD), and blood serum changes (increased creatinine, potassium, and alanine aminotransferase) were also specified as mortality-related features ( P < 0.05)., Conclusions: These results could be useful to physicians for the timely detection of COVID-19 patients with a higher risk of mortality and better management of hospital resources., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Advanced Biomedical Research.)