Alkaline Urine in the Emergency Department Predicts Nitrofurantoin Resistance.

BACKGROUND: The Proteeae group (i.e., Proteus species, Morganella morganii, and Providencia species) frequently causes urinary tract infections (UTIs) and is generally resistant to nitrofurantoin. Proteeae species can produce urease, which can increase urine pH. OBJECTIVE: Our aim was to determine whether higher urine pH in the emergency department is associated with nitrofurantoin resistance. METHODS: A single health system database of emergency department patients aged 18 years and older who received urinalysis between April 18, 2014, and March 7, 2017, was examined using χ2 test and multivariable regression analysis. RESULTS: Of 67,271 urine samples analyzed, 13,456 samples grew a single bacterial species. Urine cultures growing the Proteeae group were associated with significantly more alkaline urine than other bacteriuria cultures (odds ratio [OR] 2.20, 95% confidence interval [CI] 2.06-2.36; p < 0.001). The Proteeae species represented 4.4% of urine samples at pH 5-7, 24.4% at pH 8-9, and 40.0% at pH 9. At urine pH 5-7, 80.4% of urine samples were sensitive to nitrofurantoin; however, this percentage decreased to 66.1% for urine pH 8-9 and 54.6% for urine pH 9. Nitrofurantoin had the highest OR (2.10, 95% CI 1.85-2.39) among cefazolin, ciprofloxacin, and trimethoprim/sulfamethoxazole for bacteriuria sensitive to those antibiotics at urine pH 5-7. At urine pH 8-9 and 9, nitrofurantoin had the lowest OR among the antibiotics: 0.48 (95% CI 0.42-0.54) and 0.31 (95% CI 0.24-0.40), respectively (p < 0.001 for both). CONCLUSIONS: Urine pH of 8 or higher is associated with high rates of nitrofurantoin resistance.

BiomeHorizon: Visualizing Microbiome Time Series Data in R.

Despite playing a key role in the health of their hosts, host-associated microbial communities demonstrate considerable variation over time. These communities comprise thousands of temporally dynamic taxa, which makes visualizing microbial time series data challenging. As such, a method to visualize both the proportional and absolute change in the relative abundance of multiple taxa across multiple subjects over time is needed. To address this gap, we developed BiomeHorizon, the first automated, open-source R package that visualizes longitudinal compositional microbiome data using horizon plots. BiomeHorizon is available at https://github.com/blekhmanlab/biomehorizon/ and a guide with step-by-step instructions for using the package is provided at https://blekhmanlab.github.io/biomehorizon/. IMPORTANCE Host-associated microbiota (i.e., the number and types of bacteria in the body) can have profound impacts on an animal's day-to-day functioning as well as their long-term health. Recent work has shown that these microbial communities change substantially over time, so it is important to be able to link changes in the abundance of certain microbes with host health outcomes. However, visualizing such changes is difficult because the microbiome comprises thousands of different microbes. To address this issue, we developed BiomeHorizon, an R package for visualizing longitudinal microbiome data using horizon plots. BiomeHorizon accepts a range of data formats and was developed with two common microbiome study designs in mind: human health studies, where the microbiome is sampled at set time points, and observational wildlife studies, where samples may be collected at irregular time intervals. BiomeHorizon thus provides a flexible, user-friendly approach to microbiome time series data visualization and analysis.