Is shared decision-making vanishing at the end-of-life? A descriptive and qualitative study of advanced cancer patients’ involvement in specific therapies decision-making

Background

Little is known about what is at stake at a subjective level for the oncologists and the advanced cancer patients when they face the question whether to continue, limit or stop specific therapies. We studied (1) the frequency of such questioning, and (2) subjective determinants of the decision-making process from the physicians’ and the patients’ perspectives.

Methods

(1) All hospitalized patients were screened during 1 week in oncology and/or hematology units of five institutions. We included those with advanced cancer for whom a questioning about the pursuit, the limitation or the withholding of specific therapies (QST) was raised. (2) Qualitative design was based on in-depth interviews.

Results

In conventional units, 12.8 % of cancer patients (26 out of 202) were concerned by a QST during the study period. Interviews were conducted with all physicians and 21 advanced cancer patients. The timing of this questioning occurred most frequently as physicians estimated life expectancy between 15 days and 3 months. Faced with the most frequent dilemma (uncertain risk-benefit balance), physicians showed different ways of involving patients. The first two were called the “no choice” models: 1) trying to resolve the dilemma via a technical answer or a “wait-and-see” posture, instead of involving the patients in the questioning and the thinking; and 2), giving a “last minute” choice to the patients, leaving to them the responsibility of the decision. In a third model, they engaged early in shared reflections and dialogue about uncertainties and limits with patients, proxies and care teams. These schematic trends influenced patients’ attitudes towards uncertainty and limits, as they were influenced by these ones. Individual and systemic barriers to a shared questioning were pointed out by physicians and patients.

Conclusions

This study indicate to what extent these difficult decisions are related to physicians’ and patients’ respective and mutually influenced abilities to deal with and share about uncertainties and limits, throughout the disease trajectory. These insights may help physicians, patients and policy makers to enrich their understanding of underestimated and sensitive key issues of the decision-making process.

     

Using a mobile health application to support self-management in COPD: a qualitative study

Background

Telehealth shows promise for supporting patients in managing their long-term health conditions, such as chronic obstructive pulmonary disease (COPD). However, it is currently unclear how patients, and particularly older people, may benefit from these technological interventions.

Aim

To explore patients’ expectations and experiences of using a mobile telehealth-based (mHealth) application and to determine how such a system may impact on their perceived wellbeing and ability to manage their COPD.

Design and setting

Embedded qualitative study using interviews with patients with COPD from various community NHS services: respiratory community nursing service, general practice, and pulmonary rehabilitation.

Method

An embedded qualitative study was conducted to which patients were recruited using purposive sampling to achieve maximum variation. Interviews were carried out prior to receiving the mHealth system and again after a 6-month period. Data were analysed using a grounded theory approach.

Results

The sample comprised 19 patients (aged 50–85 years) with varied levels of computer skills. Patients identified no difficulties in using the mHealth application. The main themes encapsulating patients’ experience of using the mHealth application related to an increased awareness of the variability of their symptoms (onset of exacerbation and recovery time) and reassurance through monitoring (continuity of care).

Conclusion

Patients were able to use the mHealth application, interpret clinical data, and use these within their self-management approach regardless of previous knowledge. Telehealth interventions can complement current clinical care pathways to support self-management behaviour.     

Glycated hemoglobin A: A predictor of outcome in trauma admissions to intensive care unit

Background and Aim:

Although large studies have demonstrated the association between hyperglycemia and adverse intensive care unit (ICU) outcomes, it is yet unclear which subset of patients benefit from tight sugar control in ICU. Recent evidence suggests that stress induced hyperglycemia (SIH) and co-incidentally detected diabetes mellitus are different phenomena with different prognoses. Differentiating SIH from diabetic hyperglycemia is challenging in ICU settings. We followed a cohort of trauma patients admitted to a surgical intensive care unit (SICU) to evaluate if initial glycated hemoglobin A (HbA₁c) level predicts the outcome of admission.

Materials and Methods:

A cohort of 120 consecutive admissions to SICU following trauma were recruited and admission blood sugar and HbA₁c were measured. Outcomes were prospectively measured by blinded ICU doctors. A logistic regression model was developed to assess if HbA₁c predicts poor outcomes in these settings.

Results:

Nearly 24% of the participants had HbA₁c ≥ 6. Those with HbA₁c ≥ 6 had 3.14 times greater risk of poor outcome at the end of hospital stay when compared to those with HbA₁c < 6 and this risk increased to an odds ratio of 4.57 on adjusting for other significant predictors: Acute Physiology and Chronic Health Evaluation II, injury severity score, admission blood sugar and age at admission.

Conclusions:

Substantial proportion of trauma admissions has underlying diabetes. HbA₁c, a measure of pre admission glycaemic status is an important predictor of ICU outcome in trauma patients.     

Surface and thermal properties of synthesized cationic poly(ethylene oxide) gemini surfactants: the role of the spacer

The solubility and heat stability of surfactants are the prerequisites for their oilfield applications. Most commercial surfactants undergo hydrolysis at high temperature and prolonged heating at 40 °C or above leads to decomposition. In this report, three cationic poly(ethylene oxide) gemini surfactants (GSs) containing flexible and rigid spacers were synthesized for oilfield applications. The chemical structures of the GSs were elucidated with the aid of ¹³C NMR, ¹H NMR, FT-IR, and MALDI-TOF MS. The GSs exhibit pronounced solubility in deionized water, seawater, and formation brine and no cloudiness, phase separation, or precipitation were detected after keeping GS solutions in an oven at 90 °C for three weeks. According to thermal gravimetric analysis, the degradation temperature of all the GSs was above 240 °C, which is higher than the existing oilfield temperature (≥90 °C). The critical micelle concentration (CMC) of the synthesized GSs decreases upon increasing the temperature. Additionally, CMC values were observed to increase even further with increasing salinity. The low CMC values of gemini surfactants containing a flexible structure indicate that they create a more closely packed micelle structure compared with gemini surfactants with a rigid structure. The distinct surface and thermal features of the synthesized GSs reveal them to be appropriate materials for high salinity and elevated temperature reservoirs.

Synthesis of new cationic poly(ethylene oxide) gemini surfactants containing flexible and rigid spacer groups to tolerate harsh reservoir condition.     

Genetic Variations and Diseases in UniProtKB/Swiss‐Prot: The Ins and Outs of Expert Manual Curation

During the last few years, next‐generation sequencing (NGS) technologies have accelerated the detection of genetic variants resulting in the rapid discovery of new disease‐associated genes. However, the wealth of variation data made available by NGS alone is not sufficient to understand the mechanisms underlying disease pathogenesis and manifestation. Multidisciplinary approaches combining sequence and clinical data with prior biological knowledge are needed to unravel the role of genetic variants in human health and disease. In this context, it is crucial that these data are linked, organized, and made readily available through reliable online resources. The Swiss‐Prot section of the Universal Protein Knowledgebase (UniProtKB/Swiss‐Prot) provides the scientific community with a collection of information on protein functions, interactions, biological pathways, as well as human genetic diseases and variants, all manually reviewed by experts. In this article, we present an overview of the information content of UniProtKB/Swiss‐Prot to show how this knowledgebase can support researchers in the elucidation of the mechanisms leading from a molecular defect to a disease phenotype.     

Efficient denoising algorithms for large experimental datasets and their applications in Fourier transform ion cyclotron resonance mass spectrometry

Modern scientific research produces datasets of increasing size and complexity that require dedicated numerical methods to be processed. In many cases, the analysis of spectroscopic data involves the denoising of raw data before any further processing. Current efficient denoising algorithms require the singular value decomposition of a matrix with a size that scales up as the square of the data length, preventing their use on very large datasets. Taking advantage of recent progress on random projection and probabilistic algorithms, we developed a simple and efficient method for the denoising of very large datasets. Based on the QR decomposition of a matrix randomly sampled from the data, this approach allows a gain of nearly three orders of magnitude in processing time compared with classical singular value decomposition denoising. This procedure, called urQRd (uncoiled random QR denoising), strongly reduces the computer memory footprint and allows the denoising algorithm to be applied to virtually unlimited data size. The efficiency of these numerical tools is demonstrated on experimental data from high-resolution broadband Fourier transform ion cyclotron resonance mass spectrometry, which has applications in proteomics and metabolomics. We show that robust denoising is achieved in 2D spectra whose interpretation is severely impaired by scintillation noise. These denoising procedures can be adapted to many other data analysis domains where the size and/or the processing time are crucial.Big data are becoming predominant in modern science, and found in many scientific domains: astrophysics (1), large-scale physics experiments (2), biology (3, 4), or even natural text analysis (5). This introduces a new challenge for data handling and analysis, as it requires special processing approaches, which avoid accessing the whole data at once (6), and make use of adapted algorithms easily parallelized. Such constraints may be difficult to fulfill, even for simple procedures such as noise reduction.Every measurement is corrupted by unwanted noise, which is the combination of the effect of random fluctuations in the sample and the apparatus, but can also originate from external events like environmental noise. Denoising methods focus mainly on removing or reducing as much as possible an additive Gaussian wide sense stationary noise.For stationary signals the optimal linear denoising filter in the mean-square error sense is the Wiener filter. However, it suffers from the requirement of a precise estimate of the signal and noise auto- and cross-correlation functions. Many advanced denoising methods have been developed using linear algebra, which usually requires considerable processing power. One of the main alternative approaches relies on a multiresolution analysis, which sets noise apart from signal components more efficiently than classical orthogonal basis methods. In this respect, wavelets associated with soft thresholding have been considered for denoising purposes (7).Harmonic signals can be modeled as the sum of damped sinusoids. They are typically found in spectroscopies like NMR, Fourier transform mass spectrometry (FT-MS), FTIR but also in seismology, astrophysics, genetics, or financial analysis. They are easily analyzed by Fourier transformation if regularly sampled. For such specific signals, one class of denoising methods is based on modeling a sum of a fixed number of exponentials as devised by Prony (8). This was recently revisited and improved by Beylkin and Monzon (9, 10).There are also statistical methods related to the Karhunen–Loève transform which use adaptive basis instead of a priori basis. Relying on the autoregressive model (AR) (11, 12), a Hankel matrix is built and its rank is then reduced to the number of expected frequencies. Rank reduction by the singular value decomposition (SVD) (13) of this matrix is known as Cadzow’s method (14), also known as singular spectrum analysis (SSA) (15). The advantage is that no assumption about the noise or signal power is required and the number of frequencies is the only parameter.However, the benefits of denoising are balanced by several drawbacks. If the assumed number of frequencies is incorrect, the denoised signal is polluted with spurious artifacts that are indistinguishable from the real signal. Additionally, the SVD decomposition is slow and scales in operations. Alternative rapid SVD algorithms can be used, such as the Lanczos bidiagonalization method (16, 17), the truncated SVD (18), or random projections (19), as was recently applied in seismology (20). However, these algorithms do not solve the artifacts issue.Capitalizing on recent progress in algebra on random projection and probabilistic algorithms (21–24), we present here an efficient approach to denoising which can be easily applied to the large datasets found in Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry experiments, and more generally, to any big data analysis. The main driving idea is to avoid explicit computation of data-derived quantities, but rather estimate the needed values, based on a partial sampling of the data. Extending from previous ideas (19), the denoising algorithm is based on a subsampling of the data-associated matrix. Here, rather than truncating the rank by removing some of the components of the SVD decomposition, we compute a randomized low-rank approximation of the Hankel matrix (24) that retains preferentially more signal than noise.We show that this leads to a substantial improvement of the processing in terms of speed, with little compromise on the quality, allowing gains of 2–3 orders of magnitude in processing time and in memory size. Applications of this approach are demonstrated on the large datasets obtained in FT-ICR mass spectrometry.     

Soluble epoxide hydrolase inhibition improves myocardial perfusion and function in experimental heart failure

The study addressed the hypothesis that soluble epoxide hydrolase (sEH) inhibition, which increases cardiovascular protective epoxyeicosatrienoic acids (EETs), exerts beneficial effects in an established chronic heart failure (CHF) model. In CHF rats, left ventricular (LV) function, perfusion and remodeling were assessed using MRI and invasive hemodynamics after 42-day (starting 8 days after coronary ligation) and delayed 3-day (starting 47 days after coronary ligation) treatments with the sEH inhibitor AUDA (twice 0.25 mg/day). Delayed 3-day and 42-day AUDA increased plasma EETs demonstrating the effective inhibition of sEH. Delayed 3-day and 42-day AUDA enhanced cardiac output without change in arterial pressure, thus reducing total peripheral resistance. Both treatment periods increased the slope of the LV end-systolic pressure-volume relation, but only 42-day AUDA decreased LV end-diastolic pressure, relaxation constant Tau and the slope of the LV end-diastolic pressure-volume relation, associated with a reduced LV diastolic volume and collagen density. Delayed 3-day and, to a larger extent, 42-day AUDA increased LV perfusion associated with a decreased LV hypoxia-inducible factor-1alpha. Both treatment periods decreased reactive oxygen species level and increased reduced-oxidized glutathione ratio. Finally, MSPPOH, an inhibitor of the EET-synthesizing enzyme cytochrome epoxygenases, abolished the beneficial effects of 3-day AUDA on LV function and perfusion. Augmentation of EET availability by pharmacological inhibition of sEH increases LV diastolic and systolic functions in established CHF. This notably results from short-term processes, i.e. increased LV perfusion, reduced LV oxidative stress and peripheral vasodilatation, but also from long-term effects, i.e. reduced LV remodeling.