Prognostic value of simple frailty and malnutrition screening tools in patients with acute heart failure due to left ventricular systolic dysfunction

Background: Frailty and malnutrition are common in patients with heart failure (HF), and are associated with adverse outcomes. We studied the prognostic value of three malnutrition and three frailty indices in patients admitted acutely to hospital with HF. Methods: 265 consecutive patients [62% males, median age 80 (interquartile range (IQR): 72–86) years, median NTproBNP 3633 (IQR: 2025–6407) ng/l] admitted with HF between 2013 and 2014 were enrolled. Patients were screened for frailty using the Derby frailty index (DFI), acute frailty network (AFN) frailty criteria, and clinical frailty scale (CFS) and for malnutrition using the geriatric nutritional risk index (GNRI), controlling nutritional status (CONUT) score and prognostic nutritional index (PNI). Results: According to the CFS (> 4), DFI, and AFN, 53, 50, and 53% were frail, respectively. According to the GNRI (≤ 98), CONUT score (> 4), and PNI (≤ 38), 46, 46, and 42% patients were malnourished, respectively. During a median follow-up of 598 days (IQR 319–807 days), 113 patients died. One year mortality was 1% for those who were neither frail nor malnourished; 15% for those who were either malnourished or frail; and 65% for those who were both malnourished and frail. Amongst the malnutrition scores, PNI, and amongst the frailty scores, CFS increased model performance most compared with base model. A final model, including CFS and PNI, increased c-statistic for mortality prediction from 0.68 to 0.84. Conclusion: Worsening frailty and malnutrition indices are strongly related to worse outcome in patients hospitalised with HF

Finding gene regulatory network candidates using the gene expression knowledge base

BACKGROUND: Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘omics’ data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. RESULTS: We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. CONCLUSIONS: Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users