Access and non–access site bleeding after percutaneous coronary intervention and risk of subsequent mortality and major adverse cardiovascular events:Systematic review and meta-analysis

Background: The prognostic impact of site-specific major bleeding complications after percutaneous coronary intervention (PCI) has yielded conflicting data. The aim of this study is to provide an overview of site-specific major bleeding events in contemporary PCI and study their impact on mortality and major adverse cardiovascular event outcomes. Methods and Results: We conducted a meta-analysis of PCI studies that evaluated site-specific periprocedural bleeding complications and their impact on major adverse cardiovascular events and mortality outcomes. A systematic search of MEDLINE and Embase was conducted to identify relevant studies and random effects meta-analysis was used to estimate the risk of adverse outcomes with site-specific bleeding complications. Twenty-five relevant studies including 2 400 645 patients that underwent PCI were identified. Both non–access site (risk ratio [RR], 4.06; 95% confidence interval [CI], 3.21–5.14) and access site (RR, 1.71; 95% CI, 1.37–2.13) related bleeding complications were independently associated with an increased risk of periprocedural mortality. The prognostic impact of non–access site–related bleeding events on mortality related to the source of anatomic bleeding, for example, gastrointestinal RR, 2.78; 95% CI, 1.25 to 6.18; retroperitoneal RR, 5.87; 95% CI, 1.63 to 21.12; and intracranial RR, 22.71; 95% CI, 12.53 to 41.15. Conclusions: The prognostic impact of bleeding complications after PCI varies according to anatomic source and severity. Non–access site-related bleeding complications have a similar prevalence to those from the access site but are associated with a significantly worse prognosis partly related to the severity of the bleed. Clinicians should minimize the risk of major bleeding complications during PCI through judicious use of bleeding avoidance strategies irrespective of the access site used

Prognostic and therapeutic significance of carbohydrate antigen 19-9 as tumor marker in patients with pancreatic cancer

In pancreatic cancer ( PC) accurate determination of treatment response by imaging often remains difficult. Various efforts have been undertaken to investigate new factors which may serve as more appropriate surrogate parameters of treatment efficacy. This review focuses on the role of carbohydrate antigen 19- 9 ( CA 19- 9) as a prognostic tumor marker in PC and summarizes its contribution to monitoring treatment efficacy. We undertook a Medline/ PubMed literature search to identify relevant trials that had analyzed the prognostic impact of CA 19- 9 in patients treated with surgery, chemoradiotherapy and chemotherapy for PC. Additionally, relevant abstract publications from scientific meetings were included. In advanced PC, pretreatment CA 19- 9 levels have a prognostic impact regarding overall survival. Also a CA 19- 9 decline under chemotherapy can provide prognostic information for median survival. A 20% reduction of CA 19- 9 baseline levels within the first 8 weeks of chemotherapy appears to be sufficient to define a prognostic relevant subgroup of patients ('CA 19- 9 responder'). It still remains to be defined whether the CA 19- 9 response is a more reliable method for evaluating treatment efficacy compared to conventional imaging. Copyright (c) 2006 S. Karger AG, Basel

[Prognosis of colorectal cancer and socio-economic inequalities].

It is well established that socio-economic status is a major prognostic factor for many cancers, including colorectal cancer. The aims of this review are (i) to report epidemiological data showing how socio-economic status influences colorectal cancer survival, (ii) to attempt to describe the mechanisms underlying these survival inequalities, and (iii) to assess their impact on survival of colorectal cancer

Survival prediction in mesothelioma using a scalable lasso regression model: instructions for use and initial performance using clinical predictors

Introduction: Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models. Methods: Data regarding 269 patients with MPM were allocated to balanced training (n=169) and validation sets (n=100). Prognostic signatures (minimal length best performing multivariate trained models) were generated by least absolute shrinkage and selection operator regression for overall survival (OS), OS <6 months and OS <12 months. OS prediction was quantified using Somers DXY statistic, which varies from 0 to 1, with increasing concordance between observed and predicted outcomes. 6-month survival and 12-month survival were described by area under the curve (AUC) scores. Results: Median OS was 270 (IQR 140–450) days. The primary OS model assigned high weights to four predictors: age, performance status, white cell count and serum albumin, and after cross-validation performed significantly better than would be expected by chance (mean DXY0.332 (±0.019)). However, validation set DXY was only 0.221 (0.0935–0.346), equating to a 22% improvement in survival prediction than would be expected by chance. The 6-month and 12-month OS signatures included the same four predictors, in addition to epithelioid histology plus platelets and epithelioid histology plus C-reactive protein (mean AUC 0.758 (±0.022) and 0.737 (±0.012), respectively). The <6-month OS model demonstrated 74% sensitivity and 68% specificity. The <12-month OS model demonstrated 63% sensitivity and 79% specificity. Model content and performance were generally comparable with previous studies. Conclusions: The prognostic value of the basic clinical information contained in these, and previously published models, is fundamentally of limited value in accurately predicting MPM prognosis. The methods described are suitable for expansion using emerging predictors, including tumour genomics and volumetric staging

Telomere dysfunction accurately predicts clinical outcome in chronic lymphocytic leukaemia, even in patients with early stage disease

© 2014 John Wiley & Sons Ltd. Defining the prognosis of individual cancer sufferers remains a significant clinical challenge. Here we assessed the ability of high-resolution single telomere length analysis (STELA), combined with an experimentally derived definition of telomere dysfunction, to predict the clinical outcome of patients with chronic lymphocytic leukaemia (CLL). We defined the upper telomere length threshold at which telomere fusions occur and then used the mean of the telomere 'fusogenic' range as a prognostic tool. Patients with telomeres within the fusogenic range had a significantly shorter overall survival (P  <  0·0001; Hazard ratio [HR] = 13·2, 95% confidence interval [CI]  = 11·6-106·4) and this was preserved in early-stage disease patients (P  <  0·0001, HR=19·3, 95% CI = 17·8-802·5). Indeed, our assay allowed the accurate stratification of Binet stage A patients into those with indolent disease (91% survival at 10 years) and those with poor prognosis (13% survival at 10 years). Furthermore, patients with telomeres above the fusogenic mean showed superior prognosis regardless of their IGHV mutation status or cytogenetic risk group. In keeping with this finding, telomere dysfunction was the dominant variable in multivariate analysis. Taken together, this study provides compelling evidence for the use of high-resolution telomere length analysis coupled with a definition of telomere dysfunction in the prognostic assessment of CLL

T-Cell Subsets Predict Mortality in Malnourished Zambian Adults Initiating Antiretroviral Therapy.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedTo estimate the prognostic value of T-cell subsets in Zambian patients initiating antiretroviral therapy (ART), and to assess the impact of a nutritional intervention on T-cell subsets.This work was supported by European and Developing Countries Clinical Trials Partnership grant # IP.2009.33011.004; trial foods were prepared and supplied by Nutriset, Malauney, Franc

Prognostic indication of power cable degradation

The reliability and the health performance of network assets are of a great interest due to power network operators. This project investigates methods of developing a prognostic capability for evaluating the health and long term performance of ageing distribution cable circuits. From the instant of installation and operation, the insulating materials of a cable will begin to age as a result of a combination of mechanical, thermal and electrical factors. Development of simulation models can significantly improve the accuracy of prognostics, allowing the targeting of maintenance and reduction of in service failures [1]. Real-time measurements taken close to underground cables can update the simulation models giving a more accurate prognostic model.Currently the project investigates a thermal prognostic simulation model which will predict the likely temperature impact on a cable at burial depth according to weather conditions and known loading. Anomalies of temperature measurements along the cable compared to predicted temperatures will indicate a possible degradation activity in a cable. An experimental surface trough has been set up where operation of power cables is simulated with a control system which is able to model any cable loading. The surface temperature of the cable is continuously monitored as well as the weather conditions such as solar radiation, soil moisture content, wind speed, humidity, rainfall and air-temperature<br/

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

Persistent neutrophil to lymphocyte ratio >3 during treatment with enzalutamide and clinical outcome in patients with castration-resistant prostate cancer

The baseline value of neutrophil to lymphocyte ratio (NLR) has been found to be prognostic in patients with metastatic castration resistant prostate cancer (CRPC). We evaluated the impact of baseline NLR and its change in patients receiving enzalutamide. We included consecutive metastatic CRPC patients treated with enzalutamide after docetaxel and studies the change of NLR (&gt;3 vs ≤3) after week 4 and 12 weeks. Progression-free survival (PFS), overall survival (OS) and their 95% Confidence Intervals (95% CI) were estimated by the Kaplan-Meier method and compared with the log-rank test. The impact of NLR on PFS and OS was evaluated by Cox regression analyses and on prostate-specific antigen response rates (PSA RR; PSA decline &gt;50%) were evaluated by binary logistic regression. Data collected on 193 patients from 9 centers were evaluated. Median age was 73.1 years (range, 42.8–90.7). The median baseline NLR was 3.2. The median PFS was 3.2 months (95% CI = 2.7–4.2) in patients with baseline NLR &#62;3 and 7.4 months (95% CI = 5.5–9.7) in those with NLR ≤3, p &#60; 0.0001. The median OS was 10.4 months (95% CI = 6.5–14.9) in patients with baseline NLR &gt;3 and 16.9 months (95% CI = 11.2–20.9) in those with baseline NLR ≤3, p &#60; 0.0001. In multivariate analysis, changes in NLR at 4 weeks were significant predictors of both PFS [hazard ratio (HR) 1.24, 95% confidence interval (95% CI) 1.07–1.42, p = 0.003, and OS (HR 1.29, 95% CI 1.10–1.51, p = 0.001. A persistent NLR &#62;3 during treatment with enzalutamide seems to have both prognostic and predictive value in CRPC patients