Over the past decade, the mechanisms underlying placebo effects have begun to be identified. At the same time, the placebo response appears to have increased in pharmacological trials and marked ...placebo effects are found in neurostimulation and surgical trials, thereby posing the question whether non-pharmacological interventions should be placebo-controlled to a greater extent. In this narrative review we discuss how the knowledge of placebo mechanisms may help to improve placebo control in pharmacological and non-pharmacological trials. We review the psychological, neurobiological, and genetic mechanisms underlying placebo analgesia and outline the current problems and potential solutions to the challenges with placebo control in trials on pharmacological, neurostimulation, and surgical interventions. We particularly focus on how patients' perception of the therapeutic intervention, and their expectations towards treatment efficacy may help develop more precise placebo controls and blinding procedures and account for the contribution of placebo factors to the efficacy of active treatments. Finally, we discuss how systematic investigations into placebo mechanisms across various pain conditions and types of treatment are needed in order to ‘personalise’ the placebo control to the specific pathophysiology and interventions, which may ultimately lead to identification of more effective treatment for pain patients. In conclusion this review shows that it is important to understand how patients' perception and expectations influence the efficacy of active and placebo treatments in order to improve the test of new treatments. Importantly, this applies not only to assessment of drug efficacy but also to non-pharmacological trials on surgeries and stimulation procedures.
PROMs measure the subjective elements of patients’ conditions, including health-related quality of life, pain intensity, activity limitations, participation restrictions, satisfaction or adherence to ...treatment and help to evaluate the burden of disease and treatment from patients’ perspectives.1 Originally, PROMs were used in pharmacological research to assess treatment effects in conditions such as cancer, in cases where the cure was not possible, and quality of life became the primary concern.2 In the last 20 years, the use of PROMs has increased considerably,1 3 and, currently, those outcomes are used to assess the effects of treatment and quality of care2 and to evaluate policies3 and to inform health economics.4 One of the factors that contributed to the popularity of PROMs was their recognition by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) as a measure of treatment efficacy.1 5 6 The FDA and the EMA define PROMs as any outcomes related to the patient’s health or treatment that is evaluated directly by the patient, without any interpretation by a doctor or anyone else.1 6 According to this definition, and in contrast to common perception, these outcomes do not necessarily measure what is the most important to patients or health itself.7 8 The role of PROMs The main role of PROMs is that they provide important indicators of treatment efficacy not captured by objective markers or clinical assessments.1 6 They may be used as ‘red flags’ during the assessment of acute symptoms and help to monitor response to treatment, for example, the efficacy of analgesia, especially if they are collected in real time.2 Moreover, well-developed PROMs for a specific condition may measure treatment effects on many domains, which is useful because treatment may have a different effect on disease-specific outcomes, health status and quality of life.9 Finally, reporting PROMs improves communication between the patient and the care provider and, in turn, increases patient’s satisfaction with treatment2 and, subsequently, helps with adherence to the treatment itself and with patient retention.10 Considerations for the use of PROMs Potential limitations and biases associated with PROMs are rarely acknowledged,2 3 5 11 although both EMA and FDA recognise that PROMs may not be suitable as primary outcomes in open-label settings and that they should be supported by objective or functional outcomes.1 6 By the nature of being subjective, PROMs may be affected by internal factors, such as mood, expectations, time and sentiments, and external factors such as treatment context, interactions with the healthcare providers and patients’ socioeconomic situation, which leads to fluctuations in the outcomes. ...patients are not necessarily able to identify an improvement in their own health. ...due to variability, PROMs measurements are often categorised, which reduces reliability and decreases power.17 Strategies to improve the use of PROMs In open-label settings, patients are likely to exaggerate the treatment effect, especially if the treatment is invasive.18 18 19 Blinding of patients reduces reporting bias as well as a risk of unbalanced attrition or cointerventions,18 and blinding of doctors or researches reduces observer’s and detection bias; however, biases related to unblinded patients tend to be larger than those caused by unblinded assessors.19 20 When blinding is not possible, PROMs collection should be combined with functional, imaging and biochemical biomarkers adding an element of objectivity.1 6 The treatment effect should improve patients’ satisfaction and surpass the placebo effect; therefore, when it is important to assess the magnitude of improvement or harms associated with an active intervention, a placebo-controlled randomised controlled trial (RCT) may be necessary.21 The variability of PROMs can be mitigated by collecting measurements at multiple time points to assess the trajectories of symptoms’ progression and recovery. ...PROMs should be used in conjunction with objective outcomes, especially when assessment by blinded clinicians or researchers is not possible.
The term nocebo effect refers to the harmful outcomes that result from people's negative beliefs, anticipations, or experiences related to the treatment rather than the pharmacological properties of ...the treatment. These outcomes may include a worsening of symptoms, a lack of expected improvement, or adverse events, and they may occur after the active treatment and the placebo that is supposed to imitate it. The nocebo effect is always unwanted and may distort estimates of treatment effectiveness and safety; moreover, it may cause discontinuation of therapy or withdrawal from a trial.
The nocebo effect may be unintentionally evoked by the explanations given by healthcare professionals during a clinical consultation or consent procedures, or by information from other patients, the media, or the Internet. Moreover, it may be a consequence of previous bad experiences with the treatment, through learning and conditioning, and the conditioning may happen without patients' conscious awareness. In trial settings, a study design, for example lack of blinding, may introduce bias from the nocebo effect.
Unlike the placebo effect, which is usually taken into consideration while interpreting treatment outcomes and controlled for in clinical trials, the nocebo effect is under-recognised by clinical researchers and clinicians. This is worrying, because the nocebo phenomenon is common and may have potentially negative consequences for the results of clinical treatment and trials. It is therefore important that doctors and medical researchers consider any potential nocebo effect while assessing the treatment effect and try to minimise it through careful choice and phrasing of treatment-related information given to patients.
Arterial stiffness, cerebral pulsatility, and beat-to-beat blood pressure variability partly mediate the relationship between hypertension and stroke, but it is unknown if these intermediate ...phenotypes of vascular ageing differ between stroke aetiologies. We therefore aimed to characterize differences in these intermediate cardiovascular phenotypes between patients presenting with strokes of different aetiologies.
In consecutive patients on best medical management 1 month after TIA or nondisabling stroke (Oxford Vascular Study), arterial stiffness (PWV) was measured by applanation tonometry (Sphygmocor), middle cerebral blood flow velocity, and pulsatility index (MCA-PI) were measured by transcranial ultrasound (TCD, DWL Doppler Box), and beat-to-beat BP variability was measured with a Finometer. Differences between patients with large artery (LAS), small vessel (SVD), cardioembolic (CE), or undetermined events were derived, including adjustment for cardiovascular risk factors. Relationships were characterized by mixed linear models.
In 909 eligible patients, MCA-PI, PWV, and SBPV were all positively skewed. Mean values were greatest in LAS than CE and lowest in SVD (p < 0.001). However, after adjustment for age, sex, and risk factors, PI was greatest in LAS and lowest in CE stroke, whilst PWV was greatest in SVD and undetermined stroke (p < 0.001). In multivariate linear models, age was more strongly associated with PWV and PI in patients with small vessel stroke than other aetiologies, particularly under the age of 65, but SBPV was only weakly associated with demographic indices in all stroke subtypes.
Intermediate cardiovascular phenotypes of vascular ageing had similar demographic associations between stroke aetiologies, but these were particularly strong in patients with small vessel stroke under the age of 65, implying a potential role of these phenotypes in increasing stroke risk in this patient group.
This article will consider how physiological monitoring can be used both as an intrinsic part of an experiment, or for removing unwanted physiological signals from the FMRI time series. As functional ...MRI is used for a wide variety of applications beyond the identification of regions involved in a task, different sources of noise in the time series become important. The use of arterial spin labelling sequences, either in isolation or combined with BOLD imaging, means that temporal noise must be dealt with differently. Moreover, when these are combined with global cerebrovascular stimuli, such as respiratory challenges, the standard analysis tools must be employed with great care so as not to detrimentally distort the data. Acquiring and analysing physiological data is sometimes more art than science, and this article attempts to provide some insight into common techniques as well as advice on identifying and correcting some of the problems that may be encountered.
•An overview of tips, advice and collected wisdom for acquiring physiological data.•A guide to diagnosing artefacts and issues with respiratory stimuli for FMRI.•The what and the how of incorporating physiological data into FMRI analysis.
Small vessel disease is associated with age, mean blood pressure (MAP) and blood pressure pulsatility (PP). We used data from the UK Biobank cohort study to determine the relative importance of MAP ...versus PP driving white matter injury within individual white matter tracts, particularly in the anterior and posterior vascular territory. The associations between blood pressure and diffusion indices in 27 major tracts were analysed using unadjusted and fully-adjusted general linear models and mixed-effect linear models. Blood pressure and neuroimaging data were available for 37,041 participants (mean age 64+/−7.5 years, 53% female). In unadjusted analyses, MAP and PP were similarly associated with diffusion indices in the anterior circulation. In the posterior circulation, the associations were weaker, particularly for MAP. In fully-adjusted analyses, MAP remained associated with all diffusion indices in the anterior circulation, independently of age. In the posterior circulation, the effect of MAP became protective. PP remained associated with greater mean diffusivity and extracellular free water diffusion in the anterior circulation and all diffusion indices in the posterior circulation. There was a significant interaction between PP and age. This implies discordant mechanisms for chronic white matter injury in different brain regions and potentially in the associated stroke risks.
Abstract
Objective
The development of persistent pain following total knee arthroplasty (TKA) is common, but its underlying mechanisms are unknown. The goal of the study was to assess brain grey ...matter structure and its correlation with function of the nociceptive system in people with good and poor outcomes following TKA.
Subjects
Thirty-one people with LOW_PAIN (<3/10 on the numerical ratings scale NRS) at six months following TKA and 15 people with HIGH_PAIN (≥3/10 on the NRS) were recruited into the study.
Methods
Grey matter in key brain areas related to nociception was analyzed using voxel-based morphometry (VBM). Nociceptive facilitatory and inhibitory processes were evaluated using quantitative sensory testing (QST). QST scores and grey matter density in prespecified brain regions were compared between the LOW_PAIN and HIGH_PAIN groups. Regression analyses were used to analyze the associations between the grey matter and QST scores.
Results
There were no between-group differences in QST measures. In the VBM analysis, the HIGH_PAIN group had a higher grey matter density in the right amygdala, right nucleus accumbens, and in the periaqueductal grey (PAG), but lower grey matter density in the dorsal part of the left caudate nucleus. Grey matter density in the right amygdala and PAG correlated positively with temporal summation of pain.
Conclusions
Persistent pain at six months after TKA is associated with a higher grey matter density in the regions involved in central sensitization and pain-related fear, which may contribute to the development of persistent pain after surgery.
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