Added value of frailty and social support in predicting risk of 30-day unplanned re-admission or death for patients with heart failure: an analysis from OPERA-HF

Background: Models for predicting the outcome of patients hospitalized for heart failure (HF) rarely take a holistic view. We assessed the ability of measures of frailty and social support in addition to demographic, clinical, imaging and laboratory variables to predict short-term outcome for patients discharged after a hospitalization for HF. Methods: OPERA-HF is a prospective observational cohort, enrolling patients hospitalized for HF in a single center in Hull, UK. Variables were combined in a logistic regression model after multiple imputation of missing data to predict the composite outcome of death or readmission at 30 days. Comparisons were made to a model using clinical variables alone. The discriminative performance of each model was internally validated with bootstrap re-sampling. Results: 1094 patients were included (mean age 77 [interquartile range 68–83] years; 40% women; 56% with moderate to severe left ventricular systolic dysfunction) of whom 213 (19%) had an unplanned re-admission and 60 (5%) died within 30 days. For the composite outcome, a model containing clinical variables alone had an area under the receiver-operating characteristic curve (AUC) of 0.68 [95% CI 0.64–0.72]. Adding marital status, support from family and measures of physical frailty increased the AUC (p < 0.05) to 0.70 [95% CI 0.66–0.74]. Conclusions: Measures of physical frailty and social support improve prediction of 30-day outcome after an admission for HF but predicting near-term events remains imperfect. Further external validation and improvement of the model is required

Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases

Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data

Hypothermic machine perfusion in liver transplantation: a randomized trial

BACKGROUNDTransplantation of livers obtained from donors after circulatory death is associated with an increased risk of nonanastomotic biliary strictures. Hypothermic oxygenated machine perfusion of livers may reduce the incidence of biliary complications, but data from prospective, controlled studies are limited.METHODSIn this multicenter, controlled trial, we randomly assigned patients who were undergoing transplantation of a liver obtained from a donor after circulatory death to receive that liver either after hypothermic oxygenated machine perfusion (machine-perfusion group) or after conventional static cold storage alone (control group). The primary end point was the incidence of nonanastomotic biliary strictures within 6 months after transplantation. Secondary end points included other graft-related and general complications.RESULTSA total of 160 patients were enrolled, of whom 78 received a machine-perfused liver and 78 received a liver after static cold storage only (4 patients did not receive a liver in this trial). Nonanastomotic biliary strictures occurred in 6% of the patients in the machine-perfusion group and in 18% of those in the control group (risk ratio, 0.36; 95% confidence interval [CI], 0.14 to 0.94; P=0.03). Postreperfusion syndrome occurred in 12% of the recipients of a machine-perfused liver and in 27% of those in the control group (risk ratio, 0.43; 95% CI, 0.20 to 0.91). Early allograft dysfunction occurred in 26% of the machine-perfused livers, as compared with 40% of control livers (risk ratio, 0.61; 95% CI, 0.39 to 0.96). The cumulative number of treatments for nonanastomotic biliary strictures was lower by a factor of almost 4 after machine perfusion, as compared with control. The incidence of adverse events was similar in the two groups.CONCLUSIONSHypothermic oxygenated machine perfusion led to a lower risk of nonanastomotic biliary strictures following the transplantation of livers obtained from donors after circulatory death than conventional static cold storage.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Mutations in IRS4 are associated with central hypothyroidism

Background: Four genetic causes of isolated congenital central hypothyroidism (CeH) have been identified, but many cases remain unexplained. We hypothesised the existence of other genetic causes of CeH with a Mendelian inheritance pattern. Methods: We performed exome sequencing in two families with unexplained isolated CeH and subsequently Sanger sequenced unrelated idiopathic CeH cases. We performed clinical and biochemical characterisation of the probands and carriers identified by family screening. We investigated IRS4 mRNA expression in human hypothalamus and pituitary tissue, and measured serum thyroid hormones and Trh and Tshb mRNA expression in hypothalamus and pituitary tissue of Irs4 knockout mice. Results: We found mutations in the insulin receptor substrate 4 (IRS4) gene in two pairs of brothers with CeH (one nonsense, one frameshift). Sequencing of IRS4 in 12 unrelated CeH cases negative for variants in known genes yielded three frameshift mutatio

Analysis of Robust Soft Learning Vector Quantization

One of the popular methods for multiclass classification is Learning Vector Quantization (LVQ). There have been developed several variants of LVQ lately, among which Robust Soft Learning Vector Quantization, or RSLVQ for short. An introductory study showed that RSLVQ performs better than other LVQ algorithms, even very close to the optimal linear classifier, within a controlled environment. In order to study its performance in detail, we performed a mathematical analysis of the algorithm, in the form of a system of coupled Ordinary Differential Equations (ODE's), which might also help development of an optimal LVQ algorithm. Following from our analysis, we compare the potential performance of RSLVQ in relation to other LVQ variants and present a guideline for settings of the control parameter, i.e. the softness parameter.

Grey matter damage in multiple sclerosis A pathology perspective

Over the past decade, immunohistochemical studies have provided compelling evidence that gray matter (GM) pathology in multiple sclerosis (MS) is extensive. Until recently, this GM pathology was difficult to visualize using standard magnetic resonance imaging (MRI) techniques. However, with newly developed MRI sequences, it has become clear that GM damage is present from the earliest stages of the disease and accrues with disease progression. GM pathology is clinically relevant, as GM lesions and/or GM atrophy were shown to be associated with MS motor deficits and cognitive impairment. Recent autopsy studies demonstrated significant GM demyelination and microglia activation. However, extensive immune cell influx, complement activation and blood-brain barrier leakage, like in WM pathology, are far less prominent in the GM. Hence, so far, the cause of GM damage in MS remains unknown, although several plausible underlying pathogenic mechanisms have been proposed. This paper provides an overview of GM damage in MS with a focus on its topology and histopathology

The tell-tale heart : perceived emotional intensity of heartbeats

Heartbeats are strongly related to emotions, and people are known to interpret their own heartbeat as emotional information. To explore how people interpret other’s cardiac activity, the authors conducted four experiments. In the first experiment, they aurally presented ten different levels of heart rate to participants and compare emotional intensity ratings. In the second experiment, the authors compare the effects of nine levels of heart rate variability around 0.10 Hz and 0.30 Hz on emotional intensity ratings. In the third experiment, they combined manipulations of heart rate and heart rate variability to compare their effects. Finally, in the fourth experiment, they compare effects of heart rate to effects of angry versus neutral facial expressions, again on emotional intensity ratings. Overall, results show that people relate increases in heart rate to increases in emotional intensity. These effects were similar to effects of the facial expressions. This shows possibilities for using human interpretations of heart rate in communication applications

Machines outperform laypersons in recognizing emotions elicited by autobiographical recollection

Over the last decade, an increasing number of studies have focused on automated recognition of human emotions by machines. However, performances of machine emotion recognition studies are difficult to interpret because benchmarks have not been established. In order to provide such a benchmark, we compared machine with human emotion recognition. We gathered facial expressions, speech, and physiological signals from 17 individuals expressing 5 different emotional states. Support vector machines achieved an 82% recognition accuracy based on a physiological and facial features. In experiments with 75 humans on the same data, a maximum recognition accuracy of 62.8% was obtained. As machines outperformed humans, automated emotion recognition might be ready to be tested in more practical applications