This study aimed to assess medication use in pregnant women in Malaysia by measuring use, knowledge, awareness, and beliefs about medications. This was an observational, cross-sectional study involving a total of 447 pregnant women who attended the Obstetrics and Gynecology Clinic, Hospital Kuala Lumpur (HKL), Malaysia. A validated, self-administered questionnaire was used to collect participant data. Most of pregnant women had taken medication during pregnancy and more than half of them (52.8%) showed a poor level of knowledge about the medication use during pregnancy. Eighty-three percent had a poor level of awareness and 56.5% had negative beliefs. Age and education level were significantly associated with the level of knowledge regarding medication use during pregnancy. Multiparous pregnant women, and pregnant women from rural areas were observed to have a higher level of awareness compared with those who lived in urban areas. Use of medication during pregnancy was determined to be significantly associated with education level, and race. Although there was prevalent use of medication among pregnant women, many had negative beliefs, and insufficient knowledge and awareness about the risks of taking medication during pregnancy. Several sociodemographic characteristics were significantly associated with the use (race and education level), level of knowledge (age and education level), awareness (parity and place of residence), and beliefs (race, education level, and occupation status) towards medication use during pregnancy.

In the management of sepsis, providing adequate empiric antimicrobial therapy is one of the most important pillars of sepsis management. Therefore, it is important to evaluate the adequacy of empiric antimicrobial therapy (EAMT) in sepsis patients admitted to intensive care units (ICU) and to identify the determinants of inadequate EAMT. The aim of this study was to evaluate the adequacy of empiric antimicrobial therapy in patients admitted to the ICU with sepsis or septic shock, and the determinants of inadequate EAMT. The data of patients admitted to the ICU units due to sepsis or septic shock in two tertiary healthcare facilities in Al-Madinah Al-Munawwarah were retrospectively reviewed. The current study used logistic regression analysis and artificial neural network (ANN) analysis to identify determinants of inadequate empiric antimicrobial therapy, and evaluated the performance of these two approaches in predicting the inadequacy of EAMT. The findings of this study showed that fifty-three per cent of patients received inadequate EAMT. Determinants for inadequate EAMT were APACHE II score, multidrug-resistance organism (MDRO) infections, surgical history (lower limb amputation), and comorbidity (coronary artery disease). ANN performed as well as or better than logistic regression in predicating inadequate EAMT, as the receiver operating characteristic area under the curve (ROC-AUC) of the ANN model was higher when compared with the logistic regression model (LRM): 0.895 vs. 0.854. In addition, the ANN model performed better than LRM in predicting inadequate EAMT in terms of classification accuracy. In addition, ANN analysis revealed that the most important determinants of EAMT adequacy were the APACHE II score and MDRO. In conclusion, more than half of the patients received inadequate EAMT. Determinants of inadequate EAMT were APACHE II score, MDRO infections, comorbidity, and surgical history. This provides valuable inputs to improve the prescription of empiric antimicrobials in Saudi Arabia going forward. In addition, our study demonstrated the potential utility of applying artificial neural network analysis in the prediction of outcomes in healthcare research.

Sepsis is a medical emergency that involves a systemic immunological response due to an infection, resulting in the end-stage-organs malfunction and death. It is associated with high mortality rate despite a better understanding of the disease pathology and the mechanism involved. This review was designed to summarise the available evidences regarding the adequacy of the empiric antimicrobial therapy (EAMT), its predictors and its impact on the outcomes in intensive care unit (ICU) sepsis patients. Providing an adequate EAMT is considered one of the cornerstones of sepsis management as it has been found to be associated with better survival and is a good predictor for shorter ICU-length-of-stay. In contrast, inadequate EAMT in sepsis patients is associated with poor clinical outcomes including increased mortality and prolonged hospital stay. Evidence from this review suggest that it is important to identify determinants of inadequate EAMT to optimise the antimicrobial therapy provided to sepsis patients. Predictors of inadequate EAMT included co-morbidities (cancer), source and type of infection, higher Acute Physiology and Chronic Health Evaluation (APACHI-II) score and long hospital stay prior to the infection. As EAMT is considered as one of the effective treatment strategies to prevent sepsis associated death, healthcare providers should ensure the adequate antimicrobial therapy is provided for sepsis patients to improve and optimise their management.