Automated Detection of External Ventricular and Lumbar Drain-Related Meningitis Using Laboratory and Microbiology Results and Medication Data

OBJECTIVE: Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM), a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse. METHODS: As part of the hospital infection control program, all patients receiving an external ventricular (EVD) or lumbar drain (ELD) (2004 to 2009; n = 742) had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability. RESULTS: 537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk). The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97). The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9%) and specificity of 87.9% (84.6% to 90.8%). Positive and negative predictive values were 56.9% (50.8% to 67.9%) and 99.9% (98.6% to 99.9%), respectively. Predicted yearly infection rates concurred with observed infection rates. CONCLUSION: A prediction model based on multi-source data stored in a clinical data warehouse could accurately quantify rates of DRM. Automated detection using this statistical approach is feasible and could be applied to other nosocomial infections

Uncharted waters: rare and unclassified cardiomyopathies characterized on cardiac magnetic resonance imaging

Cardiac magnetic resonance imaging (CMR) has undergone considerable technology advances in recent years, so that it is now entering into mainstream cardiac imaging practice. In particular, CMR is proving to be a valuable imaging tool in the detection, morphological assessment and functional assessment of cardiomyopathies. Although our understanding of this broad group of heart disorders continues to expand, it is an evolving group of entities, with the rarer cardiomyopathies remaining poorly understood or even unclassified. In this review, we describe the clinical and pathophysiological aspects of several of the rare/unclassified cardiomyopathies and their appearance on CMR

Proper genomic profiling of (BRCA1-mutated) basal-like breast carcinomas requires prior removal of tumor infiltrating lymphocytes

Introduction: BRCA1-mutated breast carcinomas may have distinct biological features, suggesting the involvement of specific oncogenic pathways in tumor development. The identification of genomic aberrations characteristic for BRCA1-mutated breast carcinomas could lead to a better understanding of BRCA1-associated oncogenic events and could prove valuable in clinical testing for BRCA1-involvement in patients. Methods: For this purpose, genomic and gene expression profiles of basal-like BRCA1-mutated breast tumors (n = 27) were compared with basal-like familial BRCAX (non-BRCA1/2/CHEK2*1100delC) tumors (n = 14) in a familial cohort of 120 breast carcinomas. Results: Genome wide copy number profiles of the BRCA1-mutated breast carcinomas in our data appeared heterogeneous. Gene expression analyses identified varying amounts of tumor infiltrating lymphocytes (TILs) as a major cause for this heterogeneity. Indeed, selecting tumors with relative low amounts of TILs, resulted in the identification of three known but also five previously unrecognized BRCA1-associated copy number aberrations. Moreover, these aberrations occurred with high frequencies in the BRCA1-mutated tumor samples. Using these regions it was possible to discriminate BRCA1-mutated from BRCAX breast carcinomas, and they were validated in two independent cohorts. To further substantiate our findings, we used flow cytometry to isolate cancer cells from formalin-fixed, paraffin-embedded, BRCA1-mutated triple negative breast carcinomas with estimated TIL percentages of 40% and higher. Genomic profiles of sorted and unsorted fractions were compared by shallow whole genome sequencing and confirm our findings. Conclusion: This study shows that genomic profiling of in particular basal-like, and thus BRCA1-mutated, breast carcinomas is severely affected by the presence of high numbers of TILs. Previous reports on genomic profiling of BRCA1-mutated breast carcinomas have largely neglected this. Therefore, our findings have direct consequences on the interpretation of published genomic data. Also, these findings could prove valuable in light of currently used genomic tools for assessing BRCA1-involvement in breast cancer patients and pathogenicity assessment of BRCA1 variants of unknown significance. The BRCA1-associated genomic aberrations identified in this study provide possible leads to a better understanding of BRCA1-associated oncogenesis. (C) 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved