METHODS:PURPOSE:In utero myelomeningocele closure is a valid alternative to postnatal repair with unclear benefits to bladder function. We compared bladder status in patients who underwent fetal myelomeningocele surgery versus postnatal repair. MATERIALS AND METHODS:We retrospectively reviewed our database, with group 1 consisting of in utero surgery and group 2 consisting of postnatal repair. Group 3 was a subgroup of group 2, including patients initially presenting at age less than 12 months. We recorded medical history, radiological investigation with renal ultrasonography, voiding cystourethrography, urodynamic evaluation and clinical outcome of the bladder pattern after treatment. RESULTS:We identified 88 patients in group 1, 86 in group 2 and 38 in group 3. The incidence of urinary tract infection was higher in the postnatal period (45% vs 20%). Hydronephrosis occurred in 20.7%, 22.6% and 28.9% of patients in groups 1, 2 and 3, respectively. Vesicoureteral reflux was diagnosed in 15% in all groups. Urodynamic data showed a higher prevalence of detrusor overactivity in group 1 and no difference in other urodynamic parameters. The high risk bladder pattern at initial evaluation occurred in 56%, 50% and 46% of patients in groups 1, 2 and 3, respectively. There was a trend to decrease the percentages of the high risk bladder pattern and to increase the normal pattern after treatment in all groups. CONCLUSIONS:In utero repair did not improve urological parameters compared to repair in the postnatal period.

METHODS::The development and evolution of antimicrobial resistance (AMR) in pathogens has been reported to be one of the major issues confronting the global health community. The aim of this study was to examine the period prevalence of antibiotic resistance, as well as the trends and patterns in sensitivity profile of enteric bacteria isolated from urine samples of patients with UTIs in a teaching Hospital in south west Nigeria. Urine samples were collected from 77 patients with UTIs from February 2017 to October 2018. Standard laboratory methods were used for urine sample culture and bacterial identification. The Kirby-Bauer disk diffusion method was used to evaluate antimicrobial sensitivity. Predominant enteric bacteria isolates were Escherichia coli (24, 39.3%), Salmonella species (12, 19.7%), Klebsiella species (4, 6.6%), Providencia species (6, 9.8%), Proteus species (8, 13.1%), Serratia species (2, 3.3%), Yersinia species (1, 1.6%) and Morganella species (4, 6.6%). A large proportion (90.2%) of isolates obtained were multi-drug resistant. High resistance in amoxycillin-clavulanate (98%), cefuroxime (92%), erythromycin (90%) and ceftazidime (84%) were recorded. These results emphasize the importance of continuous screening and surveillance programmes for detection of AMR in enteric bacteria of public health importance.

METHODS::Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics. The purpose of this article is to explain a knowledge discovery and data mining approach that was employed to discover knowledge about hospital-acquired catheter-associated urinary tract infections from multiple data sources, including electronic health records and nurse staffing data. Three different machine learning techniques are described: decision trees, logistic regression, and support vector machines. The decision tree model created rules to interpret relationships among associated factors of hospital-acquired catheter-associated urinary tract infections. The logistic regression model showed what factors were related to a higher risk of hospital-acquired catheter-associated urinary tract infections. The support vector machines model was included to compare performance with the other two interpretable models. This article introduces the examples of cutting-edge machine learning approaches that will advance secondary use of electronic health records and integration of multiple data sources as well as provide evidence necessary to guide nursing professionals in practice.