“Acute urinary antibiotics”—A simple metric to identify outpatient antibiotic stewardship opportunities in renal transplant

Background: International Classification of Diseases, Tenth Edition (ICD-10) data help track outpatient antibiotic prescribing but lack validation in immunocompromised populations or subspecialty clinics for this purpose. Asymptomatic bacteriuria (ASB) and urinary tract infection (UTI) are important stewardship targets in renal transplant (RT) patients, but they may require alternative metrics to best monitor prescribing patterns. We describe ICD-10 utilization for RT clinic encounters in which antibiotics were prescribed. We developed a metric classifying “acute urinary antibiotics” (AUA) to track antibiotic use for ASB and UTI, and we validated systematic identification of AUA to enable practical implementation. Methods: We examined RT clinic visit and telemedicine encounters from 2018 to 2021 conducted 1 month after transplant. This project was deemed non–human-subjects research by the Stanford Panel on Human Subjects in Medical Research. Results: The analytic cohort included 420 antibacterial prescriptions from 408 encounters (Fig. 1). Of 238 patients, 136 (57%) were male and 112 (47%) were Hispanic or Latino. The most common primary ICD-10 code was Z94.0 (kidney transplant status) (N = 302 of 408 encounters, 75%); 26 encounters (6%) were coded for UTI (eg, N39.0, urinary tract infection, site not specified); and 214 encounters (53%) had multiple ICD-10 codes. The R82.71 code (bacteriuria) was never used. However, 215 prescriptions (51%) were classified as AUA (Fig. 2). The validation cohort included 130 prescriptions; 59 (45%) were classified as AUA and 51 (39%) had documented intent to treat ASB or UTI (positive percent agreement, 83%; negative percent agreement, 97%) (Table 1). For patients >1 month after transplant, the positive percent agreement was 95% and the negative percent agreement was 98%. Of 51 patients receiving AUA, 32 (63%) were asymptomatic despite frequently having a code for UTI (Fig. 3). Conclusions: ICD-10 coding may not be helpful in monitoring antibiotic prescribing in RT patients. The AUA metric offers a practical alternative to track antibiotic prescribing for urinary syndromes and reliably correlates with physician intent. Monitoring AUA prescribing rates could help identify opportunities to optimize antibiotic use in this complex outpatient setting

A case study of activity learning in secondary school business subjects

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An expanded regulatory network temporally controls Candida albicans biofilm formation.

Candida albicans biofilms are composed of highly adherent and densely arranged cells with properties distinct from those of free-floating (planktonic) cells. These biofilms are a significant medical problem because they commonly form on implanted medical devices, are drug resistant and are difficult to remove. C. albicans biofilms are not static structures; rather they are dynamic and develop over time. Here we characterize gene expression in biofilms during their development, and by comparing them to multiple planktonic reference states, we identify patterns of gene expression relevant to biofilm formation. In particular, we document time-dependent changes in genes involved in adhesion and metabolism, both of which are at the core of biofilm development. Additionally, we identify three new regulators of biofilm formation, Flo8, Gal4, and Rfx2, which play distinct roles during biofilm development over time. Flo8 is required for biofilm formation at all time points, and Gal4 and Rfx2 are needed for proper biofilm formation at intermediate time points

Measurements of the Total and Differential Higgs Boson Production Cross Sections Combining the H??????? and H???ZZ*???4??? Decay Channels at s\sqrt{s}=8??????TeV with the ATLAS Detector

Measurements of the total and differential cross sections of Higgs boson production are performed using 20.3~fb$^{-1}$ of $pp$ collisions produced by the Large Hadron Collider at a center-of-mass energy of $\sqrt{s} = 8$ TeV and recorded by the ATLAS detector. Cross sections are obtained from measured $H \rightarrow \gamma \gamma$ and $H \rightarrow ZZ ^{*}\rightarrow 4\ell$ event yields, which are combined accounting for detector efficiencies, fiducial acceptances and branching fractions. Differential cross sections are reported as a function of Higgs boson transverse momentum, Higgs boson rapidity, number of jets in the event, and transverse momentum of the leading jet. The total production cross section is determined to be $\sigma_{pp \to H} = 33.0 \pm 5.3 \, ({\rm stat}) \pm 1.6 \, ({\rm sys}) \mathrm{pb}$. The measurements are compared to state-of-the-art predictions.Measurements of the total and differential cross sections of Higgs boson production are performed using 20.3  fb-1 of pp collisions produced by the Large Hadron Collider at a center-of-mass energy of s=8  TeV and recorded by the ATLAS detector. Cross sections are obtained from measured H→γγ and H→ZZ*→4ℓ event yields, which are combined accounting for detector efficiencies, fiducial acceptances, and branching fractions. Differential cross sections are reported as a function of Higgs boson transverse momentum, Higgs boson rapidity, number of jets in the event, and transverse momentum of the leading jet. The total production cross section is determined to be σpp→H=33.0±5.3 (stat)±1.6 (syst)  pb. The measurements are compared to state-of-the-art predictions.Measurements of the total and differential cross sections of Higgs boson production are performed using 20.3 fb$^{-1}$ of $pp$ collisions produced by the Large Hadron Collider at a center-of-mass energy of $\sqrt{s} = 8$ TeV and recorded by the ATLAS detector. Cross sections are obtained from measured $H \rightarrow \gamma \gamma$ and $H \rightarrow ZZ ^{*}\rightarrow 4\ell$ event yields, which are combined accounting for detector efficiencies, fiducial acceptances and branching fractions. Differential cross sections are reported as a function of Higgs boson transverse momentum, Higgs boson rapidity, number of jets in the event, and transverse momentum of the leading jet. The total production cross section is determined to be $\sigma_{pp \to H} = 33.0 \pm 5.3 \, ({\rm stat}) \pm 1.6 \, ({\rm sys}) \mathrm{pb}$. The measurements are compared to state-of-the-art predictions