Diagnostic and antibiotic stewardship lessons: an outpatient assessment of symptomatic reflex urinalysis ordering accuracy using an electronic best-practice alert.

BACKGROUND: It is not well known how reliably clinicians order reflex urinalysis to microscopy and culture (rUA-cx) for outpatient urinary tract infection (UTI) workup. Antibiotic appropriateness cannot be fully appreciated until the prevalence of UTIs and asymptomatic bacteriuria (ASB) are realized. OBJECTIVE: This quality improvement study has two major aims, first to determine UTI symptom accuracy for rUA-cx ordering and second, to confirm UTI and ASB cases by integrating rUA-cx and cascaded urinalysis results. Antibiotic utilization and diagnostic coding were secondarily linked to UTIs and ASB. METHODS: An electronic best-practice alert informed the ordering of two rUA-cx options: symptomatic- rUA-cx specifically for dysuria, frequency, urgency, costovertebral pain, suprapubic pain or fever versus non-specific-rUA-cx for vague complaints. UTI symptoms were verified by chart review. Confirmed UTI was defined as a significant culture with UTI symptoms and ASB as a significant culture without UTI symptoms. RESULTS: rUA-cx (2065) were prospectively collected over 6 months from female patients at risk for uncomplicated UTIs. Symptomatic-rUA-cx and non-specific-rUA-cx were associated with UTI symptoms for 53% (809/1527) and 20% (107/538), respectively. Overall, 44% (916/2065) of all rUA-cx had UTI symptoms. rUA-cx were overordered by a factor of 9 (2065/225) for every confirmed UTI. The UTI-to-ASB relative ratio was 2.6 (225/86). Regarding UTI-relevant antibiotics, 39% (214/553) were appropriately associated with UTI whereas only 22% (74/339) of inappropriate antibiotics were captured by the ASB definition, underestimating the problem 4-fold. CONCLUSIONS: UTI and ASB remain challenging to categorize despite a meticulous method that applied acceptable criteria.

Investigations of scope and mechanism of nickel-catalyzed transformations of glycosyl trichloroacetimidates to glycosyl trichloroacetamides and subsequent, atom-economical, one-step conversion to α-urea-glycosides.

The development and mechanistic investigation of a highly stereoselective methodology for preparing α-linked-urea neo-glycoconjugates and pseudo-oligosaccharides is described. This two-step procedure begins with the selective nickel-catalyzed conversion of glycosyl trichloroacetimidates to the corresponding α-trichloroacetamides. The α-selective nature of the conversion is controlled with a cationic nickel(II) catalyst, [Ni(dppe)(OTf)2 ] (dppe=1,2-bis(diphenylphosphino)ethane, OTf=triflate). Mechanistic studies have identified the coordination of the nickel catalyst with the equatorial C2 -ether functionality of the α-glycosyl trichloroacetimidate to be paramount for achieving an α-stereoselective transformation. A cross-over experiment has indicated that the reaction does not proceed in an exclusively intramolecular fashion. The second step in this sequence is the direct conversion of α-glycosyl trichloroacetamide products into the corresponding α-urea glycosides by reacting them with a wide variety of amine nucleophiles in presence of cesium carbonate. Only α-urea-product formation is observed, as the reaction proceeds with complete retention of stereochemical integrity at the anomeric CN bond.

Denoising MR spectroscopic imaging data with low-rank approximations.

This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed by arranging the measured data in appropriate matrix forms (i.e., Casorati and Hankel) and applying low-rank approximations by singular value decomposition (SVD). The proposed method has been validated using simulated and experimental data, producing encouraging results. Specifically, the method can effectively denoise MRSI data in a wide range of SNR values while preserving spatial-spectral features. The method could prove useful for denoising MRSI data and other spatial-spectral and spatial-temporal imaging data as well.