Assessing the impact of transitional care units on dialysis patient outcomes: A multicenter, propensity score-matched analysis.

INTRODUCTION: Inadequate predialysis care and education impacts the selection of a dialysis modality and is associated with adverse clinical outcomes. Transitional care units (TCUs) aim to meet the unmet educational needs of incident dialysis patients, but their impact beyond increasing home dialysis utilization has been incompletely characterized. METHODS: This retrospective study included adults initiating in-center hemodialysis at a TCU, matched to controls (1:4) with no TCU history initiating in-center hemodialysis. Patients were followed for up to 14 months. TCUs are dedicated spaces where staff provide personalized education and as-needed adjustments to dialysis prescriptions. For many patients, therapy was initiated with four to five weekly dialysis sessions, with at least some sessions delivered by home dialysis machines. Outcomes included survival, first hospitalization, transplant waiting-list status, post-TCU dialysis modality, and vascular access type. FINDINGS: The study included 724 patients initiating dialysis across 48 TCUs, with 2892 well-matched controls. At the end of 14 months, patients initiating dialysis in a TCU were significantly more likely to be referred and/or wait-listed for a kidney transplant than controls (57% vs. 42%; p < 0.0001). Initiation of dialysis at a TCU was also associated with significantly lower rates of receiving in-center hemodialysis at 14 months (74% vs. 90%; p < 0.0001) and higher rates of arteriovenous access (70% vs. 63%; p = 0.003). Although not statistically significant, TCU patients were more likely to survive and less likely to be hospitalized during follow-up than controls. DISCUSSION: Although TCUs are sometimes viewed as only a means for enhancing utilization of home dialysis, patients attending TCUs exhibited more favorable outcomes across all endpoints. In addition to being 2.5-fold more likely to receive home dialysis, TCU patients were 42% more likely to be referred for transplantation. Our results support expanding utilization of TCUs for patients with inadequate predialysis support.

The Transcriptional Signature of Active Tuberculosis Reflects Symptom Status in Extra-Pulmonary and Pulmonary Tuberculosis.

BACKGROUND: Mycobacterium tuberculosis infection is a leading cause of infectious death worldwide. Gene-expression microarray studies profiling the blood transcriptional response of tuberculosis (TB) patients have been undertaken in order to better understand the host immune response as well as to identify potential biomarkers of disease. To date most of these studies have focused on pulmonary TB patients with gene-expression profiles of extra-pulmonary TB patients yet to be compared to those of patients with pulmonary TB or sarcoidosis. METHODS: A novel cohort of patients with extra-pulmonary TB and sarcoidosis was recruited and the transcriptional response of these patients compared to those with pulmonary TB using a variety of transcriptomic approaches including testing a previously defined 380 gene meta-signature of active TB. RESULTS: The 380 meta-signature broadly differentiated active TB from healthy controls in this new dataset consisting of pulmonary and extra-pulmonary TB. The top 15 genes from this meta-signature had a lower sensitivity for differentiating extra-pulmonary TB from healthy controls as compared to pulmonary TB. We found the blood transcriptional responses in pulmonary and extra-pulmonary TB to be heterogeneous and to reflect the extent of symptoms of disease. CONCLUSIONS: The transcriptional signature in extra-pulmonary TB demonstrated heterogeneity of gene expression reflective of symptom status, while the signature of pulmonary TB was distinct, based on a higher proportion of symptomatic individuals. These findings are of importance for the rational design and implementation of mRNA based TB diagnostics.

Quantitative gene set analysis generalized for repeated measures, confounder adjustment, and continuous covariates.

BACKGROUND: Gene set analysis (GSA) of gene expression data can be highly powerful when the biological signal is weak compared to other sources of variability in the data. However, many gene set analysis approaches utilize permutation tests which are not appropriate for complex study designs. For example, the correlation of subjects is broken when comparing time points within a longitudinal study. Linear mixed models provide a method to analyze longitudinal studies as well as adjust for potential confounding factors and account for sources of variability that are not of primary interest. Currently, there are no known gene set analysis approaches that fully account for these study design and analysis aspects. In order to do so, we generalize the QuSAGE gene set analysis algorithm, denoted Q-Gen, and provide the necessary estimation adjustments to incorporate linear mixed model analyses. RESULTS: We assessed the performance of our generalized method in comparison to the original QuSAGE method in settings such as longitudinal repeated measures analysis and accounting for potential confounders. We demonstrate that the original QuSAGE method can not control for type-I error when these complexities exist. In addition to statistical appropriateness, analysis of a longitudinal influenza study suggests Q-Gen can allow for greater sensitivity when exploring a large number of gene sets. CONCLUSIONS: Q-Gen is an extension to the gene set analysis method of QuSAGE, and allows for linear mixed models to be applied appropriately within a gene set analysis framework. It provides GSA an added layer of flexibility that was not currently available. This flexibility allows for more appropriate statistical modeling of complex data structures that are inherent to many microarray study designs and can provide more sensitivity.

Prophylactic PEG placement in head and neck cancer: how many feeding tubes are unused (and unnecessary)?

AIM: To determine the rate of use and non-use of prophylactic percutaneous endoscopic gastrostomy (PEG) tubes among patients with head and neck cancer (HNC) patients. METHODS: All patients with HNC undergoing PEG between January 1, 2004 and June 30, 2006 were identified. Patients (or their next-of-kin) were surveyed by phone and all available medical records and cancer registry data were reviewed. Prophylactic PEG was defined as placement in the absence of dysphagia and prior to radiation or chemoradiation. Each patient with a prophylactic PEG was assessed for cancer diagnosis, type of therapy, PEG use, and complications related to PEG. RESULTS: One hundred and three patients had PEG tubes placed for HNC. Thirty four patients (33%) could not be contacted for follow-up. Of the 23 (22.3%) patients with prophylactic PEG tubes, 11/23 (47.8%) either never used the PEG or used it for less than 2 wk. No association with PEG use vs non-use was observed for cancer diagnosis, stage, or specific cancer treatment. Non-use or limited use was observed in 3/6 (50%) treated with radiation alone vs 8/17 (47.1%) treated with chemoradiation (P = 1.0), and 3 of 10 (30%) treated with surgery vs 8 of 13 (62%) not treated with surgery (P = 0.21). Minor complications were reported in 5/23 (21.7%). One (4.3%) major complication was reported. CONCLUSION: There is a high rate of unnecessary PEG placement when done prophylactically in patients with head and neck cancer.

Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis.

BACKGROUND: Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, a Gram-negative bacillus classified by the National Institute of Allergy and Infectious Diseases (NIAID) as a category B priority agent. Septicemia is the most common presentation of the disease with a 40% mortality rate even with appropriate treatments. Better diagnostic tests are therefore needed to improve therapeutic efficacy and survival rates. RESULTS: We have used microarray technology to generate genome-wide transcriptional profiles (>48,000 transcripts) from the whole blood of patients with septicemic melioidosis (n = 32), patients with sepsis caused by other pathogens (n = 31), and uninfected controls (n = 29). Unsupervised analyses demonstrated the existence of a whole blood transcriptional signature distinguishing patients with sepsis from control subjects. The majority of changes observed were common to both septicemic melioidosis and sepsis caused by other infections, including genes related to inflammation, interferon-related genes, neutrophils, cytotoxic cells, and T-cells. Finally, class prediction analysis identified a 37 transcript candidate diagnostic signature that distinguished melioidosis from sepsis caused by other organisms with 100% accuracy in a training set. This finding was confirmed in 2 independent validation sets, which gave high prediction accuracies of 78% and 80%, respectively. This signature was significantly enriched in genes coding for products involved in the MHC class II antigen processing and presentation pathway. CONCLUSIONS: Blood transcriptional patterns distinguish patients with septicemic melioidosis from patients with sepsis caused by other pathogens. Once confirmed in a large scale trial this diagnostic signature might constitute the basis of a differential diagnostic assay.