Patient Assisted Intervention for Neuropathy: Comparison of Treatment in Real Life Situations (PAIN-CONTRoLS): Bayesian Adaptive Comparative Effectiveness Randomized Trial

Peripheral Neuropathy

Peripheral neuropathy is a commonly encountered diagnosis in both neurology and primary care office settings. It is important for primary care providers to identify, characterize, and diagnose patients with neuropathy. This study aims to describe the clinical presentation, diagnostic work up, and treatment options for this entity, as well as the identification of atypical features that should prompt specialized laboratory testing, electrodiagnostic testing, and neurologic consultation.

Neuropathic pain: Evidence based recommendations

Neuropathic pain continues to be a significant problem that lacks effective solutions for every single patient. In 2015, international guidelines (NeuPSIG) were published, while the French recommendations were updated in 2020. The purpose of this minireview is to provide an update on the process of developing evidence-based recommendations and explore potential changes to the current recommendations. Primary treatments for neuropathic pain include selective serotonin-norepinephrine reuptake inhibitors (SNRIs) such as duloxetine and venlafaxine, gabapentin, tricyclic antidepressants, as well as topical lidocaine and transcutaneous electrical nerve stimulation, which are specifically suggested for focal peripheral neuropathic pain. Pregabalin is a first line treatment according to international guidelines but second-line in the more recent French guidelines, due to lower efficacy seen in more recent studies and misuse risk. Additionally, tramadol, combination therapies, and psychotherapy as adjuncts are proposed second line; high-concentration capsaicin patches, and botulinum toxin A are proposed specifically for focal peripheral neuropathic pain. In cases where primary and secondary treatments prove insufficient, third-line options come into play. These include high-frequency repetitive transcranial magnetic stimulation (rTMS) targeting the motor cortex, spinal cord stimulation, and the use of strong opioids when no alternative is available. To ensure optimal management of neuropathic pain in real-life situations, it is imperative to disseminate these recommendations widely and secure the acceptance of practitioners. By doing so, we can bridge the gap between theory and practice, and enhance the overall care and treatment of individuals suffering from neuropathic pain.

Bortezomib-induced peripheral neuropathy: Clinical features, molecular basis, and therapeutic approach

Bortezomib is the first-line standard and most effective chemotherapeutic for multiple myeloma; however, bortezomib-induced peripheral neuropathy (BIPN) severely affects the chemotherapy regimen and has long-term impact on patients under maintenance therapy. The pathogenesis of BIPN is poorly understood, and basic research and development of BIPN management drugs are in early stages. Besides chemotherapy dose reduction and regimen modification, no recommended prevention and treatment approaches are available for BIPN apart from the International Myeloma Working Group guidelines for peripheral neuropathy in myeloma. An in-depth exploration of the pathogenesis of BIPN, development of additional therapeutic approaches, and identification of risk factors are needed. Optimizing effective and standardized BIPN treatment plans and providing more decision-making evidence for clinical diagnosis and treatment of BIPN are necessary. This article reviews the recent advances in BIPN research; provides an overview of clinical features, underlying molecular mechanisms, and therapeutic approaches; and highlights areas for future studies.

Mobile Mindfulness Meditation for Cancer-Related Anxiety and Neuropathy: Protocol for a Randomized Controlled Trial

BACKGROUND

Anxiety- and cancer-related neuropathy are two persistent effects related to treatment for cancer. Mindfulness meditation has been used with substantial impact as a nonpharmacologic intervention to mitigate side and late effects of treatment. Mobile apps are ubiquitous for most of the general population, yet have a particular relevance for cancer survivors, given that physical and geographic limitations can be present.

OBJECTIVE

This study aims to describe an ongoing trial of the Mindfulness Coach mobile app for cancer survivors.

METHODS

In this randomized waitlist controlled trial, cancer survivors experiencing anxiety- or cancer-related neuropathy (200 for neuropathy and 200 for anxiety) and who had finished primary cancer treatment were invited to participate. Data were collected at 3 time points regardless of randomization condition: baseline, 8 weeks, and 16 weeks. Both face-to-face and web-based recruitment strategies were used. The trial was opened for 2 separate primary outcomes (anxiety- or cancer-related neuropathy). The goal was not to compare these groups but to compare treatment and waitlist groups for each condition. In addition to evaluating the impact of mobile mindfulness on reported anxiety- or cancer-related neuropathy, other pain, fatigue, trauma, sleep, and satisfaction with the Mindfulness Coach app will also be assessed.

RESULTS

Outcomes of the study are expected in early 2024.

CONCLUSIONS

Mindfulness meditation has become widely practiced, and the use of mobile technology has become ubiquitous. Finding ways to deliver mindfulness meditation to people who have been treated for cancer allows for the intervention to be accessible to a larger number of survivors. The results of this intervention could have implications for further understanding the impact of mindfulness meditation on 2 persistent side and late effects of treatment of cancer, namely anxiety- and cancer-related neuropathy.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03581357; https://ClinicalTrials.gov/study/NCT03581357.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47745.

A practical guide to adopting Bayesian analyses in clinical research

Background

Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher's ability to gain confidence in interpreting and implementing Bayesian analyses.

Methods

We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research.

Results

The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems.

Conclusion

Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN

Background

Response adaptive randomization is popular in adaptive trial designs, but the literature detailing its execution is lacking. These designs are desirable for patients/stakeholders, particularly in comparative effectiveness research, due to the potential benefits including improving participant buy-in by providing more participants with better treatment during the trial. Frequentist approaches have often been used, but adaptive designs naturally fit the Bayesian methodology; it was developed to deal with data as they come in by updating prior information.

Methods

PAIN-CONTRoLS was a comparative-effectiveness trial utilizing Bayesian response adaptive randomization to four drugs, nortriptyline, duloxetine, pregabalin, or mexiline, for cryptogenic sensory polyneuropathy (CSPN) patients. The aim was to determine which treatment was most tolerable and effective in reducing pain. Quit and efficacy rates were combined into a utility function to develop a single outcome, which with treatment sample size, drove the adaptive randomization. Prespecified interim analyses allowed the study to stop for early success or update the randomization probabilities to the better-performing treatments.

Results

Seven adaptations to the randomization occurred before the trial ended due to reaching the maximum sample size, with more participants receiving nortriptyline and duloxetine. At the end of the follow-up, nortriptyline and duloxetine had lower probabilities of participants that had stopped taking the study medication and higher probabilities were efficacious. Mexiletine had the highest quit rate, but had an efficacy rate higher than pregabalin.

Conclusions

Response adaptive randomization has become a popular trial tool, especially for those utilizing Bayesian methods for analyses. By illustrating the execution of a Bayesian adaptive design, using the PAIN-CONTRoLS trial data, this paper continues the work to provide literature for conducting Bayesian response adaptive randomized trials.

Roadmap to engagement: Bringing patient partners into cancer research and beyond

The University of Kansas Cancer Center (KU Cancer Center) initiated an engagement program to leverage the lived experience of individuals and families with cancer. KU Cancer Center faculty, staff, and patient partners built an infrastructure to achieve a patient-designed, patient-led, and research-informed engagement program called Patient and Investigator Voices Organizing Together (PIVOT). This special communication offers an engagement roadmap that can be replicated, scaled, and adopted at other cancer centers and academic health systems. PIVOT demonstrates that collaboration among academic leaders, investigators, and people with a lived experience yields a patient-centered, vibrant environment that enriches the research enterprise.

Using a Bayesian model of the joint distribution of pain and time on medication to decide on pain medication for neuropathy

The PAIN-CONTRoLS trial compared four medications in treating Cryptogenic sensory polyneuropathy. The primary outcome was a utility function that combined two outcomes, patients' pain score reduction and patients' quit rate. However, additional analysis of the individual outcomes could also be leveraged to inform selecting an optimal medication for future patients. We demonstrate how joint modeling of longitudinal and time-to-event data from PAIN-CONTRoLS can be used to predict the effects of medication in a patient-specific manner and helps to make patient-focused decisions. A joint model was used to evaluate the two outcomes while accounting for the association between the longitudinal process and the time-to-event processes. Results suggested no significant association between the patients' pain scores and time to the medication quit in the PAIN-CONTRoLS study, but the joint model still provided robust estimates and a better model fit. Using the model estimates, given patients' baseline characteristics, a drug profile on both the pain reduction and medication time could be obtained for each drug, providing information on how likely they would quit and how much pain reduction they should expect. Our analysis suggested that drugs viable for one patient may not be beneficial for others.

Response-adaptive randomization in clinical trials: from myths to practical considerations

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.

A secondary analysis of PAIN-CONTRoLS: Pain's impact on sleep, fatigue, and activities of daily living

INTRODUCTION/AIMS

Peripheral neuropathies commonly affect quality of life of patients due to pain, sleep disturbances, and fatigue, although trials have not adequately explored these domains of care. The aim of this study was to assess the impact of nortriptyline, duloxetine, pregabalin, and mexiletine on pain, sleep, and fatigue in patients diagnosed with cryptogenic sensory polyneuropathy (CSPN).

METHODS

We implemented a Bayesian adaptive design to perform a 12-wk multisite, randomized, prospective, open-label comparative effectiveness study in 402 CSPN patients. Participants received either nortriptyline (n = 134), duloxetine (n = 126), pregabalin (n = 73), or mexiletine (n = 69). At prespecified analysis timepoints, secondary outcomes, Patient Reported Outcomes Measurement Information System (PROMIS) surveys including Short Form (SF)-12, pain interference, fatigue, and sleep disturbance, were collected.

RESULTS

Mexiletine had the highest quit rate (58%) due to gastrointestinal side effects, while nortriptyline (38%) and duloxetine (38%) had the lowest quit rates. If tolerated for the full 12 wk of the study, mexiletine had the highest probability (>90%) of positive outcomes for improvements in pain interference and fatigue. There was no significant difference among the medications for sleep disturbance or SF-12 scores. Adverse events and lack of efficacy were the two most common reasons for cessation of therapy.

DISCUSSION

Physicians caring for patients with CSPN should consider mexiletine to address pain and fatigue, although nortriptyline and duloxetine are better medications to trial first since they are better tolerated. Future research should compare other commonly used medications for CSPN to determine evidence-based treatment strategies.

Prevention and Management Strategies for Diabetic Neuropathy

Diabetic neuropathy (DN) is a common complication of diabetes that is becoming an increasing concern as the prevalence of diabetes rapidly rises. There are several types of DN, but the most prevalent and studied type is distal symmetrical polyneuropathy, which is the focus of this review and is simply referred to as DN. It can lead to a wide range of sensorimotor and psychosocial symptoms and is a major risk factor for diabetic foot ulceration and Charcot neuropathic osteoarthropathy, which are associated with high rates of lower limb amputation and mortality. The prevention and management of DN are thus critical, and clinical guidelines recommend several strategies for these based on the best available evidence. This article aims to provide a narrative review of DN prevention and management strategies by discussing these guidelines and the evidence that supports them. First, the epidemiology and diverse clinical manifestations of DN are summarized. Then, prevention strategies such as glycemic control, lifestyle modifications and footcare are discussed, as well as the importance of early diagnosis. Finally, neuropathic pain management strategies and promising novel therapies under investigation such as neuromodulation devices and nutraceuticals are reviewed.

Micro- and nanocarriers for pain alleviation

Acute or chronic pain is a major source of impairment in quality of life and affects a substantial part of the population. To date, pain is alleviated by a limited range of treatments with significant toxicity, increased risk of misuse and inconsistent efficacy, owing, in part, to lack of specificity and/or unfavorable pharmacokinetic properties. Thanks to the unique properties of nanoscaled drug carriers, nanomedicine may enhance drug biodistribution and targeting, thus contributing to improved bioavailability and lower off-target toxicity. After a brief overview of the current situation and the main critical issues regarding pain alleviation, this review will examine the most advanced approaches using nanomedicine of each drug class, from the preclinical stage to approved nanomedicines.

Pharmacological treatments of neuropathic pain: real-life comparisons using propensity score matching

ABSTRACT

Studies comparing different drug treatments for chronic neuropathic pain (NP) are very limited. We, therefore, examined 4 recommended treatments, namely, antidepressants (duloxetine, venlafaxine, and tricyclic antidepressants), antiepileptics (gabapentine and pregabalin), weak opioids, and strong opioids, among patients with NP evaluated before first visit in a tertiary pain treatment centre and 6 months later. Patients with both a clinical diagnosis of NP and a DN4 score ≥3/7 were selected from patients enrolled in the Quebec Pain Registry. Each participant was assigned an inverse weighting of the probability of receiving any NP treatment, taking into account their age, sex, baseline pain intensity, pain duration, pain catastrophizing tendency, education level, employment, and comedications at 6-month follow-up (M6). Patients were considered as improved if they presented at least a 30% reduction on average pain intensity at M6 compared with baseline. A total of 944 patients completed both baseline and M6 evaluations. Overall, 23.0% of patients were significantly improved for pain intensity at M6. There was no significant difference in proportions patients taking or not antidepressants, gabapentinoids, or weak opioids. Among patients taking strong opioids (N = 288), 13.9% (N = 40/288) were improved vs 27.0% (177/656) of those who were not on opioids (P < 0.004). Inverse probability of treatment weighting confirmed that the proportion of patients who improved was significantly lower among those taking strong opioids compared with those who did not (P < 0.001). In conclusion, long-term use of strong opioids is a treatment suited for a limited proportion of patients with chronic NP.

The Antinociceptive Potential of Camellia japonica Leaf Extract, (−)-Epicatechin, and Rutin against Chronic Constriction Injury-Induced Neuropathic Pain in Rats

Neuropathic pain is caused by a lesion or disease of the somatosensory nervous system. Currently, prescribed treatments are still unsatisfactory or have limited effectiveness. Camellia japonica leaves are known to have antioxidant and anti-inflammatory properties.; however, their antinociceptive efficacy has not yet been explored. We examined the antinociceptive efficacy and underlying mechanism of C. japonica leaf extract (CJE) in chronic constriction injury (CCI)-induced neuropathic pain models. To test the antinociceptive activity of CJE, three types of allodynia were evaluated: punctate allodynia using von Frey filaments, dynamic allodynia using a paintbrush and cotton swab, and cold allodynia using a cold plate test. CCI rats developed neuropathic pain representing increases in the three types of allodynia and spontaneous pain. In addition, CCI rats showed high phosphorylation levels of mitogen-activated protein kinases (MAPKs), transcription factors, and nociceptive mediators in dorsal root ganglion (DRG). The ionized calcium-binding adapter molecule 1 levels and neuroinflammation also increased following CCI surgery in the spinal cord. CJE and its active components have potential antinociceptive effects against CCI-induced neuropathic pain that might be mediated by MAPK activation in the DRG and microglial activation in the spinal cord. These findings suggest that CJE, (−)-epicatechin, and rutin could be novel candidates for neuropathic pain management.

Oral and Topical Treatment of Painful Diabetic Polyneuropathy: Practice Guideline Update Summary: Report of the AAN Guideline Subcommittee

OBJECTIVE

To update the 2011 American Academy of Neurology (AAN) guideline on the treatment of painful diabetic neuropathy (PDN) with a focus on topical and oral medications and medical class effects.

METHODS

The authors systematically searched the literature from January 2008 to April 2020 using a structured review process to classify the evidence and develop practice recommendations using the AAN 2017 Clinical Practice Guideline Process Manual.

RESULTS

Gabapentinoids (standardized mean difference [SMD] 0.44; 95% confidence interval [CI], 0.21-0.67), serotonin-norepinephrine reuptake inhibitors (SNRIs) (SMD 0.47; 95% CI, 0.34-0.60), sodium channel blockers (SMD 0.56; 95% CI, 0.25-0.87), and SNRI/opioid dual mechanism agents (SMD 0.62; 95% CI, 0.38-0.86) all have comparable effect sizes just above or just below our cutoff for a medium effect size (SMD 0.5). Tricyclic antidepressants (TCAs) (SMD 0.95; 95% CI, 0.15-1.8) have a large effect size, but this result is tempered by a low confidence in the estimate.

RECOMMENDATIONS SUMMARY

Clinicians should assess patients with diabetes for PDN (Level B) and those with PDN for concurrent mood and sleep disorders (Level B). In patients with PDN, clinicians should offer TCAs, SNRIs, gabapentinoids, and/or sodium channel blockers to reduce pain (Level B) and consider factors other than efficacy (Level B). Clinicians should offer patients a trial of medication from a different effective class when they do not achieve meaningful improvement or experience significant adverse effects with the initial therapeutic class (Level B) and not use opioids for the treatment of PDN (Level B).

Comparative Effectiveness Research using Bayesian Adaptive Designs for Rare Diseases: Response Adaptive Randomization Reusing Participants

Slow accrual rate is a major challenge in clinical trials for rare diseases and is identified as the most frequent reason for clinical trials to fail. This challenge is amplified in comparative effectiveness research where multiple treatments are compared to identify the best treatment. Novel efficient clinical trial designs are in urgent need in these areas. Our proposed response adaptive randomization (RAR) reusing participants trial design mimics the real-world clinical practice that allows patients to switch treatments when desired outcome is not achieved. The proposed design increases efficiency by two strategies: 1) Allowing participants to switch treatments so that each participant can have more than one observation and hence it is possible to control for participant specific variability to increase statistical power; and 2) Utilizing RAR to allocate more participants to the promising arms such that ethical and efficient studies will be achieved. Extensive simulations were conducted and showed that, compared with trials where each participant receives one treatment, the proposed participants reusing RAR design can achieve comparable power with a smaller sample size and a shorter trial duration, especially when the accrual rate is low. The efficiency gain decreases as the accrual rate increases.

French guidelines for neuropathic pain: An update and commentary

Neuropathic pain remains a significant unmet need. French recommendations were updated in 2020. The goal of this minireview is to provide an update on these published guidelines. Despite newer relevant studies, our proposed algorithm remains relevant. First-line treatments include serotonin-noradrenaline reuptake inhibitors (duloxetine and venlafaxine), gabapentin and tricyclic antidepressants, topical lidocaine and transcutaneous electrical nerve stimulation being specifically proposed for focal peripheral neuropathic pain. Second-line treatments include pregabalin (such position being confirmed by newer studies), tramadol, combinations and psychotherapy as add on, high-concentration capsaicin patches and botulinum toxin A being proposed specifically for focal peripheral neuropathic pain. Third-line treatments include high-frequency repetitive transcranial magnetic stimulation of the motor cortex, spinal cord stimulation and strong opioids (in the lack of alternative). Disseminating these recommendations and ensuring that they are well accepted by French practitioners will be necessary to optimize neuropathic pain management in real life.

Drug-induced Myopathies

Differential diagnosis of muscle pain and weakness is extensive, including neurological, vertebral, arthrogenic, vascular, traumatic, immunological, endocrine, genetic and infectious aetiologies, as well as medication or toxin-related causes. Muscles are highly sensitive to a large number of drugs, especially with high doses. Although many drug classes can cause toxic myopathy, a significant number of cases are caused by lipid-lowering drugs, long-term use of corticosteroids, and, most often, alcohol misuse. Some drug interactions, e.g. those that are metabolised via the enzyme CYP3A4, can increase the serum levels of the drugs and drug-induced toxicity. A careful history of patient's drug and alcohol consumption is therefore vital. Clinical symptoms depend on the drug, dosage and patient's sensitivity. They can vary from asymptomatic increase in serum levels of creatine kinase, mild myalgia and cramps to muscle weakness, rhabdomyolysis, kidney failure and even death. The pathogenesis is often only partially known and multifactorial. Toxic myopathy is often reversible once the drug is discontinued, alternative drug therapy is started or a different dosage regimen is chosen. Complications such as acute kidney failure must be avoided, and analgesic therapy may be indicated.

Designing and analyzing clinical trials for personalized medicine via Bayesian models

Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medicine studies in cases where early stopping rules for success or futility are met. We apply integrated two-component prediction method (ITP) for longitudinal data simulation, and simple linear regression for longitudinal data imputation to optimize the study design. The designs' performance in terms of power for the subgroup treatment effects and overall treatment effect, sample size, and study duration are investigated via simulation. We found the hierarchical model is an optimal approach to identifying subgroup treatment effects, and the cluster hierarchical model is an excellent alternative approach in cases where sufficient information is not available for specifying the priors. The interim analysis introduction to the study design lead to the trade-off between power and expected sample size via the adjustment of the early stopping criteria. The introduction of the longitudinal modeling slightly improves the power. These findings can be applied to future personalized medicine studies with discrete or time-to-event endpoints.