Background

Urinary tract infection (UTI) is a frequent, serious complication in kidney allograft recipients.

Methods

We reviewed the records of 1166 kidney allograft recipients who received their allografts at our institution between January 2005 and December 2010 and determined the incidence of UTI during the first 3 months after transplantation (early UTI). We used Cox proportional hazards models to determine the risk factors for early UTI and whether early UTI was an independent risk factor for subsequent bacteremia or acute cellular rejection (ACR).

Results

UTI, defined as 10 or more bacterial colony-forming units/mL urine, developed in 247 (21%) of the 1166 recipients. Independent risk factors for the first episode of UTI were female gender (hazard ratio [HR], 2.9; 95% confidence intervals [CI], 2.2-3.7; P<0.001), prolonged use of Foley catheter (HR, 3.9; 95% CI, 2.8-5.4; P <0.001), ureteral stent (HR, 1.4; 95% CI, 1.1-1.8; P=0.01), age (HR, 1.1; 95% CI, 1.0-1.2; P=0.03), and delayed graft function (HR, 1.4; 95% CI, 1.0-1.9; P=0.06). Trimethoprim/sulfamethoxazole prophylaxis was associated with a reduced risk of UTI (HR, 0.6; 95% CI, 0.3-0.9; P=0.02). UTI was an independent risk factor for subsequent bacteremia (HR, 2.4; 95% CI, 1.2-4.8; P=0.01). Untreated UTI, but not treated UTI, was associated with an increased risk of ACR (HR, 2.8; 95% CI, 1.3-6.2; P=0.01).

Conclusions

Female gender, prolonged use of Foley catheter, ureteral stent, age, and delayed graft function are independent risk factors for early UTI. UTI is independently associated with the development of bacteremia, and untreated UTI is associated with subsequent ACR.

Purpose

To use the power of knowledge acquisition and machine learning in the development of a collaborative computer classification system based on the features of age-related macular degeneration (AMD).

Methods

A vocabulary was acquired from four AMD experts who examined 100 ophthalmoscopic images. The vocabulary was analyzed, hierarchically structured, and incorporated into a collaborative computer classification system called IDOCS. Using this system, three of the experts examined images from a second set of digital images compiled from more than 1000 patients with AMD. Images were annotated, and features were identified and defined. Decision trees, a machine learning method, were trained on the data collected and used to extract patterns. Interrelationships between the data from the different clinicians were investigated.

Results

Six drusen classes in the structured vocabulary were largely sufficient to describe all the identified features. The decision trees classified the data with 76.86% to 88.5% accuracy and distilled patterns in the form of hierarchical trees composed of 5 to 15 nodes. Experts were largely consistent in their characterization of soft, and to a lesser extent, hard drusen, but diverge in definition of other drusen. Size and crystalline morphology were the main determinants of drusen type across all experts.

Conclusions

Machine learning is a powerful tool for the characterization of disease phenotypes. The creation of a defined feature set for AMD will facilitate the development of an IDOCS-based classification system.

Background

Acute rejection (AR) and recurrent hepatitis C virus (R-HCV) are significant complications in liver allograft recipients. Noninvasive diagnosis of intragraft pathologies may improve their management.

Methods

We performed small RNA sequencing and microRNA (miRNA) microarray profiling of RNA from sera matched to liver allograft biopsies from patients with nonimmune, nonviral (NINV) native liver disease. Absolute levels of informative miRNAs in 91 sera matched to 91 liver allograft biopsies were quantified using customized real-time quantitative PCR (RT-qPCR) assays: 30 biopsy-matched sera from 26 unique NINV patients and 61 biopsy-matched sera from 41 unique R-HCV patients. The association between biopsy diagnosis and miRNA abundance was analyzed by logistic regression and calculating the area under the receiver operating characteristic curve.

Results

Nine miRNAs-miR-22, miR-34a, miR-122, miR-148a, miR-192, miR-193b, miR-194, miR-210, and miR-885-5p-were identified by both sRNA-seq and TLDA to be associated with NINV-AR. Logistic regression analysis of absolute levels of miRNAs and goodness-of-fit of predictors identified a linear combination of miR-34a + miR-210 (P < 0.0001) as the best statistical model and miR-122 + miR-210 (P < 0.0001) as the best model that included miR-122. A different linear combination of miR-34a + miR-210 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft inflammation, and miR-34a + miR-122 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft fibrosis.

Conclusions

Circulating levels of miRNAs, quantified using customized RT-qPCR assays, may offer a rapid and noninvasive means of diagnosing AR in human liver allografts and for discriminating AR from intragraft inflammation or fibrosis due to R-HCV.