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Abstract Background Volunteers have always been integral to hospice and palliative care. However, their roles have been left relatively undefined and broad. Aim This study aims to examine the role of hospice volunteers in German inpatient hospice and palliative care. The question we seek to answer is: What do hospice volunteers contribute to everyday life in inpatient hospice and palliative care units? Methods We undertook a multicenter, on-site qualitative interview study, utilizing problem-centered interviews with 16 volunteers from five inpatient hospice units and one hospital palliative care unit. Interviews were analyzed using grounded theory. Results Analysis of the interviews revealed three typical characteristics of how hospice volunteers’ describe their own role: (1) performing small acts of kindness, (2) creating a family-like atmosphere, (3) expecting emotional experiences. A common theme across all categories is the emphasis on spontaneous actions and personal experiences. The process of dying becomes an experience interpreted by volunteers as enriching, as a gift, as a “teacher”. Conclusion Granting hospice volunteers freedom to act spontaneously and intuitively benefits hospice and palliative care delivery. Organizations should leave sufficient room for spontaneity in order to involve volunteers effectively. Open and unstandardized roles facilitate dynamic work practices.

Plain language summary The clinical course of patients with COVID-19 is highly variable, with some patients barely affected and others dying. We wanted to better understand why this is the case and identify markers of COVID-19-associated mortality. To this end, we looked into the entire available medical history of more than 100,000 COVID-19 patients from the United States. We found that patients who had experienced a disturbance of electrolyte or fluid levels in the year before they contracted SARS-CoV-2 were more likely to die than patients without such a history. This observation suggests that careful monitoring and balancing of the hydration and electrolyte status during and even before a SARS-CoV-2 infection may be beneficial and possibly reduce the risk of death with COVID-19.

Abstract Classic galactosemia (OMIM #230400) is an autosomal recessive disorder caused by homozygous or compound heterozygous pathogenic variants in the galactose-1-phosphate uridylyltransferase gene (GALT; 606999) on chromosome 9p13. Its diagnosis is established by detecting elevated erythrocyte galactose-1-phosphate concentration, reduced erythrocyte galactose-1-phosphate uridylyltransferase (GALT) enzyme activity. Biallelic pathogenic variants in the GALT gene is confirmed by DNA analysis. Our paper presents molecular characteristics of 195 Polish patients diagnosed with galactosemia I, intending to expand the current knowledge of this rare disease's molecular etiology. To the best of our knowledge, the described cohort of galactosemia patients is the largest single-center cohort presented so far.

Material models are required to solve continuum mechanical problems. These models contain parameters that are usually determined by application-specific test setups. In general, the theoretically developed models and, thus, the parameters to be determined become increasingly complex, e.g., incorporating higher-order motion derivatives, such as the strain or strain rate. Therefore, the strain rate behaviour needs to be extracted from experimental data. Using image data, the most-common way in solid experimental mechanics to do so is digital image correlation. Alternatively, optical flow methods, which allow an adaption to the underlying motion estimation problem, can be applied. In order to robustly estimate the strain rate fields, an optical flow approach implementing higher-order spatial and trajectorial regularisation is proposed. Compared to using a purely spatial variational approach of higher order, the proposed approach is capable of calculating more accurate displacement and strain rate fields. The procedure is finally demonstrated on experimental data of a shear cutting experiment, which exhibited complex deformation patterns under difficult optical conditions.

Abstract Background Cardiopulmonary bypass (CPB) is often associated with degrees of complex inflammatory response mediated by various cytokines. This response can, in severe cases, lead to systemic hypotension and organ dysfunction. Cytokine removal might therefore improve outcomes of patients undergoing cardiac surgery. CytoSorb® (Cytosorbents, NJ, USA) is a recent device designed to remove cytokine from the blood using haemoadsorption (HA). This trial aims to evaluate the potential of CytoSorb® to decrease peri-operative cytokine levels in cardiac surgery. Methods We have conducted a single-centre pilot randomized controlled trial in 30 patients undergoing elective cardiac surgery and deemed at risk of complications. Patients were randomly allocated to either standard of care (n = 15) or CytoSorb® HA (n = 15) during cardiopulmonary bypass (CPB). Our primary outcome was the difference between the two groups in cytokines levels (IL-1a, IL-1b, IL-2, IL-4, IL-5, IL-6, IL-10, TNF-α, IFN-γ, MCP-1) measured at anaesthesia induction, at the end of CPB, as well as 6 and 24 h post-CPB initiation. In a consecutive subgroup of patients (10 in HA group, 11 in control group), we performed cross-adsorber as well as serial measurements of coagulation factors’ activity (antithrombin, von Willebrand factor, factor II, V, VIII, IX, XI, and XII). Results Both groups were similar in terms of baseline and peri-operative characteristics. CytoSorb® HA during CPB was not associated with an increased incidence of adverse event. The procedure did not result in significant coagulation factors’ adsorption but only some signs of coagulation activation. However, the intervention was associated neither with a decrease in pro- or anti-inflammatory cytokine levels nor with any improvement in relevant clinical outcomes. Conclusions CytoSorb® HA during CPB was not associated with a decrease in pro- or anti-inflammatory cytokines nor with an improvement in relevant clinical outcomes. The procedure was feasible and safe. Further studies should evaluate the efficacy of CytoSorb® HA in other clinical contexts. Trial registration ClinicalTrials.gov NCT02775123. Registered 17 May 2016.

Abstract Background Since 2007, the German statutory health insurance covers Specialized Outpatient Palliative Care (SAPV). SAPV offers team-based home care for patients with advanced and progressive disease, complex symptoms and life expectancy limited to days, weeks or months. The introduction of SAPV is ruled by a directive (SAPV directive). Within this regulation, SAPV delivery models can and do differ regarding team structures, financing models, cooperation with other care professionals and processes of care. The research project SAVOIR is funded by G-BA’s German Innovations Fund to evaluate the implementation of the SAPV directive. Methods The processes, content and quality of SAPV will be evaluated from the perspectives of patients, SAPV teams, general practitioners and other care givers and payers. The influence of different contracts, team and network structures and regional and geographic settings on processes and results including patient-reported outcomes will be analyzed in five subprojects: [1] structural characteristics of SAPV and their impact on patient care, [2] quality of care from the perspective of patients, [3] quality of care from the perspective of SAPV teams, hospices, ambulatory nursing services, nursing homes and other care givers, content and extent of care from [4] the perspective of General Practitioners and [5] from the perspective of payers. The evaluation will be based on different types of data: team and organizational structures, treatment data based on routine documentation with electronic medical record systems, prospective assessment of patient-reported outcomes in a sample of SAPV teams, qualitative interviews with other stakeholders like nursing and hospice services, a survey in general practitioners and a retrospective analysis of claims data of all SAPV patients, covered by the health insurance fund BARMER in 2016. Discussion Data analysis will allow identification of variables, associated with quality of SAPV. Based on these findings, the SAVOIR study group will develop recommendations for the Federal Joint Committee for a revision of the SAPV directive. Trial registration German Clinical Trials Register (DRKS): DRKS00013949 (retrospectively registered, 14.03.2018), DRKS00014726 (14.05.2018), DRKS00014730 (30.05.2018). Subproject 3 is an interview study with professional caregivers and therefore not registered in DRKS as a clinical study.

Increasingly complex numerical analyses require more and more precise, accurate and varied input parameters in order to achieve results that are as realistic and reliable as possible. Therefore, experimental analyses for material parameter identification are of high importance and a driving force for further developments. In this work, opportunities by applying fractal analysis to optical measurement data of a shear cutting process are investigated. The fractal analysis is based on a modification of the concept of scale-space filtering. Scale exponent fields are calculated for the image sequences of the shear cutting process that are taken by a mobile microscope. A least-square approximation is used for the automated evaluation of the local scale exponent values. In order to determine the change of the scale exponent of individual material points, a digital image correlation is applied.

Introduction: Prognostic scores are important tools in oncology to facilitate clinical decision-making based on patient characteristics. To date, classic survival analysis using Cox proportional hazards regression has been employed in the development of these prognostic scores. With the advance of analytical models, this study aimed to determine if more complex machine-learning algorithms could outperform classical survival analysis methods.Methods: In this benchmarking study, two datasets were used to develop and compare different prognostic models for overall survival in pan-cancer populations: a nationwide EHR-derived de-identified database for training and in-sample testing and the OAK (phase III clinical trial) dataset for out-of-sample testing. A real-world database comprised 136K first-line treated cancer patients across multiple cancer types and was split into a 90% training and 10% testing dataset, respectively. The OAK dataset comprised 1,187 patients diagnosed with non-small cell lung cancer. To assess the effect of the covariate number on prognostic performance, we formed three feature sets with 27, 44 and 88 covariates. In terms of methods, we benchmarked ROPRO, a prognostic score based on the Cox model, against eight complex machine-learning models: regularized Cox, Random Survival Forests (RSF), Gradient Boosting (GB), DeepSurv (DS), Autoencoder (AE) and Super Learner (SL). The C-index was used as the performance metric to compare different models.Results: For in-sample testing on the real-world database the resulting C-index [95% CI] values for RSF 0.720 [0.716, 0.725], GB 0.722 [0.718, 0.727], DS 0.721 [0.717, 0.726] and lastly, SL 0.723 [0.718, 0.728] showed significantly better performance as compared to ROPRO 0.701 [0.696, 0.706]. Similar results were derived across all feature sets. However, for the out-of-sample validation on OAK, the stronger performance of the more complex models was not apparent anymore. Consistently, the increase in the number of prognostic covariates did not lead to an increase in model performance.Discussion: The stronger performance of the more complex models did not generalize when applied to an out-of-sample dataset. We hypothesize that future research may benefit by adding multimodal data to exploit advantages of more complex models.

Using manual derivatization in gas chromatography-mass spectrometry samples have varying equilibration times before analysis which increases technical variability and limits the number of potential samples analyzed. By contrast, automated derivatization methods can derivatize and inject each sample in an identical manner. We present a fully automated (on-line) derivatization method used for targeted analysis of different matrices. We describe method optimization and compare results from using off-line and on-line derivatization protocols, including the robustness and reproducibility of the methods. Our final parameters for the derivatization process were 20 µL of methoxyamine (MeOx) in pyridine for 60 min at 30 °C followed by 80 µL N-Methyl-N-trimethylsilyltrifluoracetamide (MSTFA) for 30 min at 30 °C combined with 4 h of equilibration time. The repeatability test in plasma and liver revealed a median relative standard deviation (RSD) of 16% and 10%, respectively. Serum samples showed a consistent intra-batch median RSD of 20% with an inter-batch variability of 27% across three batches. The direct comparison of on-line versus off-line demonstrated that on-line was fit for purpose and improves repeatability with a measured median RSD of 11% compared to 17% using the same method off-line. In summary, we recommend that optimized on-line methods may improve results for metabolomics and should be used where available.

Atrial septostomy (AS) is recommended for pulmonary arterial hypertension (PAH)-associated right ventricular (RV) failure, recurrent syncope, or pulmonary hypertensive crisis (PHC). We aimed to evaluate the feasibility and efficacy of AS to manage PAH from infancy to adulthood. From June 2009 to December 2016, transcatheter atrial communications were created in 11 PAH patients (4 girls/women; median age = 4.3 years; range = 33 days–26 years; median body weight = 14 kg; range = 3–71 kg; NYHA-/Ross class IV; n = 11). PAH was classified as idiopathic (n = 6) or secondary (n = 5). History of syncope was dominant (n = 6); two with patent foramen ovale (PFO) admitted with recurrent PHC, three patients required resuscitation before AS. Three patients had PAH-associated low cardiac output. The average pulmonary arterial pressures (PAP systolic/diastolic) were 101/50 (±34/23); the corresponding systemic arterial pressures (SAP) were 99/54 (±23/11); and the mean ratio of PAPd / SAPd was 0.97 (±0.4). Percutaneous trans-septal puncture was uneventfully performed in nine patients; a PFO was dilated in two patients. There was no procedure-related mortality. The median balloon size was 10 mm (range = 6–14 mm); the mean catheter time was 174.6 ± 48 min; fluoroscopy time was 19.8 (±11) min. Syncope and PHC were successfully treated in all patients. The mean arterial oxygen saturation decreased from 97 ± 2 to 89 ± 11.7. One patient died awaiting lung transplantation, one continues to be listed; two patients received a reverse Potts-shunt, one patient died during follow-up; seven patients are stable with PAH-specific treatment. Percutaneous AS is an effective method palliating PAH-associated syncope, PHCs or right (bi-) ventricular heart failure.

We manipulated the sense of body ownership with the rubber hand illusion (RHI) to determine if perception of a potentially painful threat to the rubber hand can modify the mechanical pain threshold (MPT). Simultaneous tactile stimulation of the subject’s concealed hand and the appropriately positioned visible rubber hand generated the illusion of false body ownership. The MPT was recorded on the left hand of the subjects before and after induction of the RHI, as well as during the phase in which the model hand was pricked with a sharp knife or touched by the blunt knife handle. The results indicate that the RHI could be successfully generated with our set-up. Mechanical stimuli were perceived as more painful in the condition where the rubber hand was simultaneously pricked with a knife. Our findings suggest that the illusion of body ownership gates nociceptive processing of potentially painful stimuli.

Abstract In past years, comprehensive representations of cell signalling pathways have been developed by manual curation from literature, which requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of its structural features and its dynamic behaviour can take place. Mathematical modelling techniques are used to simulate the complex behaviour of cell signalling networks, which ultimately sheds light on the mechanisms leading to complex diseases or helps in the identification of drug targets. A variety of databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data. In principle, the scenario is prepared to make the most of this information for the analysis of the dynamics of signalling pathways. However, are the knowledge repositories of signalling pathways ready to realize the systems biology promise? In this article we aim to initiate this discussion and to provide some insights on this issue.

Proton pump inhibitors (PPIs) have been associated with adverse clinical outcomes amongst clopidogrel users after an acute coronary syndrome. Recent pre-clinical results suggest that this risk might extend to subjects without any prior history of cardiovascular disease. We explore this potential risk in the general population via data-mining approaches.Using a novel approach for mining clinical data for pharmacovigilance, we queried over 16 million clinical documents on 2.9 million individuals to examine whether PPI usage was associated with cardiovascular risk in the general population.In multiple data sources, we found gastroesophageal reflux disease (GERD) patients exposed to PPIs to have a 1.16 fold increased association (95% CI 1.09-1.24) with myocardial infarction (MI). Survival analysis in a prospective cohort found a two-fold (HR = 2.00; 95% CI 1.07-3.78; P = 0.031) increase in association with cardiovascular mortality. We found that this association exists regardless of clopidogrel use. We also found that H2 blockers, an alternate treatment for GERD, were not associated with increased cardiovascular risk; had they been in place, such pharmacovigilance algorithms could have flagged this risk as early as the year 2000.Consistent with our pre-clinical findings that PPIs may adversely impact vascular function, our data-mining study supports the association of PPI exposure with risk for MI in the general population. These data provide an example of how a combination of experimental studies and data-mining approaches can be applied to prioritize drug safety signals for further investigation.

BackgroundPeripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF).Methods and resultsWe analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1:5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients.ConclusionsThis proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover 'natural experiments' such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

Pharmacovigilance plays a key role in the healthcare domain through the assessment, monitoring and discovery of interactions amongst drugs and their effects in the human organism. However, technological advances in this field have been slowing down over the last decade due to miscellaneous legal, ethical and methodological constraints. Pharmaceutical companies started to realize that collaborative and integrative approaches boost current drug research and development processes. Hence, new strategies are required to connect researchers, datasets, biomedical knowledge and analysis algorithms, allowing them to fully exploit the true value behind state-of-the-art pharmacovigilance efforts. This manuscript introduces a new platform directed towards pharmacovigilance knowledge providers. This system, based on a service-oriented architecture, adopts a plugin-based approach to solve fundamental pharmacovigilance software challenges. With the wealth of collected clinical and pharmaceutical data, it is now possible to connect knowledge providers' analysis and exploration algorithms with real data. As a result, new strategies allow a faster identification of high-risk interactions between marketed drugs and adverse events, and enable the automated uncovering of scientific evidence behind them. With this architecture, the pharmacovigilance field has a new platform to coordinate large-scale drug evaluation efforts in a unique ecosystem, publicly available at http://bioinformatics.ua.pt/euadr/.

Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings.To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network.Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible.Overall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs ('prime suspects'): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate.Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out.A strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of 'prime suspects' makes a good starting point for further clinical, laboratory, and epidemiologic investigation.

BACKGROUND: Aortopulmonary collaterals (APCs) are frequently found in patients with a single-ventricle (SV) circulation. However, knowledge about the clinical significance of the systemic-to-pulmonary shunt flow in patients after the modified Fontan procedure and its potential causes is limited. Accordingly, the aim of our study was to detect and quantify APC flow using cardiovascular magnetic resonance (CMR) and assess its impact on SV volume and function as well as to evaluate the role of the size of the pulmonary arteries in regard to the development of APCs. METHODS: 60 patients (mean age 13.3 ± 6.8 years) after the Fontan procedure without patent tunnel fenestration underwent CMR as part of their routine clinical assessment that included ventricular functional analysis and flow measurements in the inferior vena cava (IVC), superior vena cava (SVC) and ascending aorta (Ao). APC flow was quantified using the systemic flow estimator: (Ao) - (IVC + SVC). Pulmonary artery index (Nakata index) was calculated as RPA + LPA area/body surface area using contrast enhanced MR angiography. The patient cohort was divided into two groups according to the median APC flow: group 1 < 0.495 l/min/m(2) and group 2 > 0.495 l/min/m(2). RESULTS: Group 1 patients had significant smaller SV enddiastolic (71 ± 16 vs 87 ± 25 ml/m(2); p=0.004) and endsystolic volumes (29 ± 11 vs 40 ± 21 ml/m(2); p=0.02) whereas ejection fraction (59 ± 9 vs 56 ± 13%; p=0.38) differed not significantly. Interestingly, pulmonary artery size showed a significant inverse correlation with APC flow (r=-0.50, p=0.002). CONCLUSIONS: Volume load due to APC flow in Fontan patients affected SV dimensions, but did not result in an impairment of SV function. APC flow was related to small pulmonary artery size, suggesting that small pulmonary arteries represent a potential stimulus for the development of APCs.

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

BackgroundScientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.Principal findingsWe developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.ConclusionsFor the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases.AvailabilityThe gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.