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BACKGROUND AND PURPOSE: In these guidelines, we aimed to develop evidence-based recommendations for the use of screening questionnaires and diagnostic tests in patients with neuropathic pain (NeP).METHODS: We systematically reviewed studies providing information on the sensitivity and specificity of screening questionnaires, and quantitative sensory testing, neurophysiology, skin biopsy, and corneal confocal microscopy. We also analysed how functional neuroimaging, peripheral nerve blocks, and genetic testing might provide useful information in diagnosing NeP.RESULTS: Of the screening questionnaires, Douleur Neuropathique en 4 Questions (DN4), I-DN4 (self-administered DN4), and Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) received a strong recommendation, and S-LANSS (self-administered LANSS) and PainDETECT weak recommendations for their use in the diagnostic pathway for patients with possible NeP. We devised a strong recommendation for the use of skin biopsy and a weak recommendation for quantitative sensory testing and nociceptive evoked potentials in the NeP diagnosis. Trigeminal reflex testing received a strong recommendation in diagnosing secondary trigeminal neuralgia. Although many studies support the usefulness of corneal confocal microscopy in diagnosing peripheral neuropathy, no study specifically investigated the diagnostic accuracy of this technique in patients with NeP. Functional neuroimaging and peripheral nerve blocks are helpful in disclosing pathophysiology and/or predicting outcomes, but current literature does not support their use for diagnosing NeP. Genetic testing may be considered at specialist centres, in selected cases.CONCLUSIONS: These recommendations provide evidence-based clinical practice guidelines for NeP diagnosis. Due to the poor-to-moderate quality of evidence identified by this review, future large-scale, well-designed, multicentre studies assessing the accuracy of diagnostic tests for NeP are needed.

Refereed/Peer-reviewed Control interventions (often called “sham,” “placebo,” or “attention controls”) are essential for studying the efficacy or mechanism of physical, psychological, and self-management interventions in clinical trials. This article presents core recommendations for designing, conducting, and reporting control interventions to establish a quality standard in non-pharmacological intervention research. A framework of additional considerations supports researchers’ decision making in this context. We also provide a reporting checklist for control interventions to enhance research transparency, usefulness, and rigour.

Background\ud \ud Diabetic peripheral neuropathic pain (DPNP) is common and often distressing. Most guidelines recommend amitriptyline, duloxetine, pregabalin, or gabapentin as initial analgesic treatment for DPNP, but there is little comparative evidence on which one is best or whether they should be combined. We aimed to assess the efficacy and tolerability of different combinations of first-line drugs for treatment of DPNP.\ud \ud \ud \ud Methods\ud \ud OPTION-DM was a multicentre, randomised, double-blind, crossover trial in patients with DPNP with mean daily pain numerical rating scale (NRS) of 4 or higher (scale is 0–10) from 13 UK centres. Participants were randomly assigned (1:1:1:1:1:1), with a predetermined randomisation schedule stratified by site using permuted blocks of size six or 12, to receive one of six ordered sequences of the three treatment pathways: amitriptyline supplemented with pregabalin (A-P), pregabalin supplemented with amitriptyline (P-A), and duloxetine supplemented with pregabalin (D-P), each pathway lasting 16 weeks. Monotherapy was given for 6 weeks and was supplemented with the combination medication if there was suboptimal pain relief (NRS >3), reflecting current clinical practice. Both treatments were titrated towards maximum tolerated dose (75 mg per day for amitriptyline, 120 mg per day for duloxetine, and 600 mg per day for pregabalin). The primary outcome was the difference in 7-day average daily pain during the final week of each pathway. This trial is registered with ISRCTN, ISRCTN17545443.\ud \ud \ud \ud Findings\ud \ud Between Nov 14, 2017, and July 29, 2019, 252 patients were screened, 140 patients were randomly assigned, and 130 started a treatment pathway (with 84 completing at least two pathways) and were analysed for the primary outcome. The 7-day average NRS scores at week 16 decreased from a mean 6·6 (SD 1·5) at baseline to 3·3 (1·8) at week 16 in all three pathways. The mean difference was –0·1 (98·3% CI –0·5 to 0·3) for D-P versus A-P, –0·1 (–0·5 to 0·3) for P-A versus A-P, and 0·0 (–0·4 to 0·4) for P-A versus D-P, and thus not significant. Mean NRS reduction in patients on combination therapy was greater than in those who remained on monotherapy (1·0 [SD 1·3] vs 0·2 [1·5]). Adverse events were predictable for the monotherapies: we observed a significant increase in dizziness in the P-A pathway, nausea in the D-P pathway, and dry mouth in the A-P pathway.\ud \ud \ud \ud Interpretation\ud \ud To our knowledge, this was the largest and longest ever, head-to-head, crossover neuropathic pain trial. We showed that all three treatment pathways and monotherapies had similar analgesic efficacy. Combination treatment was well tolerated and led to improved pain relief in patients with suboptimal pain control with a monotherapy.\ud \ud \ud \ud Funding\ud \ud National Institute for Health Research (NIHR) Health Technology Assessment programme.

Background: To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN. Methods: The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935). Models were externally validated in a large population-based cohort (N = 295) in the presence of missing values. Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy.Results: Random Forest (MCC=0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN. Conclusions: Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model’s performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.

Over the last two decades, awareness of the negative repercussions of flaws in the planning, conduct and reporting of preclinical research involving experimental animals has been growing. Several initiatives have set out to increase transparency and internal validity of preclinical studies, mostly publishing expert consensus and experience. While many of the points raised in these various guidelines are identical or similar, they differ in detail and rigour. Most of them focus on reporting, only few of them cover the planning and conduct of studies. The aim of this systematic review is to identify existing experimental design, conduct, analysis and reporting guidelines relating to preclinical animal research. A systematic search in PubMed, Embase and Web of Science retrieved 13 863 unique results. After screening these on title and abstract, 613 papers entered the full-text assessment stage, from which 60 papers were retained. From these, we extracted unique 58 recommendations on the planning, conduct and reporting of preclinical animal studies. Sample size calculations, adequate statistical methods, concealed and randomised allocation of animals to treatment, blinded outcome assessment and recording of animal flow through the experiment were recommended in more than half of the publications. While we consider these recommendations to be valuable, there is a striking lack of experimental evidence on their importance and relative effect on experiments and effect sizes.

Over the last two decades, awareness of the negative repercussions of flaws in the planning, conduct and reporting of preclinical research involving experimental animals has been growing. Several initiatives have set out to increase transparency and internal validity of preclinical studies, mostly publishing expert consensus and experience. While many of the points raised in these various guidelines are identical or similar, they differ in detail and rigour. Most of them focus on reporting, only few of them cover the planning and conduct of studies. The aim of this systematic review is to identify existing experimental design, conduct, analysis and reporting guidelines relating to preclinical animal research. A systematic search in PubMed, Embase and Web of Science retrieved 13 863 unique results. After screening these on title and abstract, 613 papers entered the full-text assessment stage, from which 60 papers were retained. From these, we extracted unique 58 recommendations on the planning, conduct and reporting of preclinical animal studies. Sample size calculations, adequate statistical methods, concealed and randomised allocation of animals to treatment, blinded outcome assessment and recording of animal flow through the experiment were recommended in more than half of the publications. While we consider these recommendations to be valuable, there is a striking lack of experimental evidence on their importance and relative effect on experiments and effect sizes.

Objective Within the last years, there has been growing awareness of the negative repercussions of unstandardized planning, conduct and reporting of preclinical and biomedical research. Several initiatives have set the aim of increasing validity and reliability in reporting of studies and publications, and publishers have formed similar groups. Additionally, several groups of experts across the biomedical spectrum have published experience and opinion-based guidelines and guidance on potential standardized reporting. While all these guidelines cover reporting of experiments, an important step prior to this should be rigours planning and conduction of studies. The aim of this systematic review is to identify and harmonize existing experimental design, conduct and analysis guidelines relating to internal validity and reproducibility of preclinical animal research. The review will also identify literature describing risks of bias pertaining to the design, conduct and analysis of preclinical biomedical research. Search strategy PubMed, Embase and Web of Science will be searched systematically to identify guidelines published in English language in peer-reviewed journals before January 2018 (box 1). All articles or systematic reviews in English language that describe or review guidelines on the internal validity and reproducibility of animal studies will be included. Google search for guidelines published on the websites of major funders and professional organisations can be found in (Box 2). Screening and annotation Unique references will be screened in two phases: screening for eligibility based on title and abstract, followed by screening for definitive inclusion based on full text. Screening will be performed in SyRF (http://syrf.org.uk). Each reference will be randomly presented to two independent reviewers. Disagreements between reviewers will be resolved by additional screening of the reference by a third, senior researcher. Data management and reporting All data, including extracted text and guidelines, will be stored in the SyRF platform. Elements of the included guidelines will be identified using a standardized extraction form. Reporting will follow the PRISMA guidelines as far as applicable.

For genetic research to contribute more fully to furthering our knowledge of neuropathic pain, we require an agreed, valid, and feasible approach to phenotyping, to allow collaboration and replication in samples of sufficient size. Results from genetic studies on neuropathic pain have been inconsistent and have met with replication difficulties, in part because of differences in phenotypes used for case ascertainment. Because there is no consensus on the nature of these phenotypes, nor on the methods of collecting them, this study aimed to provide guidelines on collecting and reporting phenotypes in cases and controls for genetic studies. Consensus was achieved through a staged approach: (1) systematic literature review to identify all neuropathic pain phenotypes used in previous genetic studies; (2) Delphi survey to identify the most useful neuropathic pain phenotypes and their validity and feasibility; and (3) meeting of experts to reach consensus on the optimal phenotype(s) to be collected from patients with neuropathic pain for genetic studies. A basic "entry level" set of phenotypes was identified for any genetic study of neuropathic pain. This set identifies cases of "possible" neuropathic pain, and controls, and includes: (1) a validated symptom-based questionnaire to determine whether any pain is likely to be neuropathic; (2) body chart or checklist to identify whether the area of pain distribution is neuroanatomically logical; and (3) details of pain history (intensity, duration, any formal diagnosis). This NeuroPPIC "entry level" set of phenotypes can be expanded by more extensive and specific measures, as determined by scientific requirements and resource availability.

BACKGROUND: This review is an update of a review published in 2011, itself a major update of previous reviews published in 2005 and 2000, investigating the effects of gabapentin in chronic neuropathic pain (pain due to nerve damage). Antiepileptic drugs are used to manage chronic neuropathic pain and fibromyalgia. OBJECTIVES: To assess the analgesic efficacy and adverse effects of gabapentin in chronic neuropathic pain and fibromyalgia. SEARCH METHODS: We identified randomised trials of gabapentin for chronic neuropathic pain or fibromyalgia by searching the databases MEDLINE (1966 to March 2014), EMBASE (1980 to 2014 week 10), and CENTRAL in The Cochrane Library (Issue 3 of 12, 2014). We obtained clinical trial reports and synopses of published and unpublished studies from Internet sources, and searched Clinicaltrials.gov. Searches were run originally in 2011 and the date of the most recent search was 17 March 2014. SELECTION CRITERIA: Randomised, double‐blind studies reporting the analgesic and adverse effects of gabapentin in neuropathic pain or fibromyalgia with assessment of pain intensity, pain relief, or both, using validated scales. Participants were adults. DATA COLLECTION AND ANALYSIS: Three review authors independently extracted efficacy and adverse event data, examined issues of study quality, and assessed risk of bias. We performed analysis using three tiers of evidence. First tier evidence derived from data meeting current best standards and subject to minimal risk of bias (outcome equivalent to substantial pain intensity reduction, intention‐to‐treat analysis without imputation for dropouts; at least 200 participants in the comparison, 8 to 12 weeks duration, parallel design), second tier from data that failed to meet one or more of these criteria and were considered at some risk of bias but with adequate numbers in the comparison, and third tier from data involving small numbers of participants that were considered very likely to be biased or used outcomes of limited clinical utility, or both. For efficacy, we calculated the number needed to treat to benefit (NNT), concentrating on at least 50% pain intensity reduction, and Initiative on Methods, Measurement and Pain Assessment in Clinical Trials (IMMPACT) definitions of at least moderate and substantial benefit. For harm we calculated number needed to treat for harm (NNH) for adverse effects and withdrawal. Meta‐analysis was undertaken using a fixed‐effect model. We emphasised differences between conditions now defined as neuropathic pain, and other conditions like masticatory pain, complex regional pain syndrome type 1 (CRPS‐1), and fibromyalgia. MAIN RESULTS: Seven new studies with 1919 participants were added. Another report (147 participants) provided results for a study already included, but which previously had no usable data. A further report (170 participants) used an experimental formulation of intrathecal gabapentin. Thirty‐seven studies (5633 participants) studied oral gabapentin at daily doses of 1200 mg or more in 12 chronic pain conditions; 84% of participants were in studies of postherpetic neuralgia, painful diabetic neuropathy or mixed neuropathic pain. There was no first tier evidence. Second tier evidence for the outcome of at least 50% pain intensity reduction, considered valuable by patients with chronic pain, showed that gabapentin was significantly better than placebo in postherpetic neuralgia (34% gabapentin versus 21% placebo; NNT 8.0, 95% CI 6.0 to 12) and painful diabetic neuropathy (38% versus 21%, NNT 5.9, 95% CI 4.6 to 8.3). There was insufficient information in other pain conditions to reach any reliable conclusion. There was no obvious difference between standard gabapentin formulations and recently‐introduced extended‐release or gastro‐retentive formulations, or between different doses of gabapentin. Adverse events occurred significantly more often with gabapentin. Persons taking gabapentin could expect to have at least one adverse event (62%), withdraw because of an adverse event (11%), suffer dizziness (19%), somnolence (14%), peripheral oedema (7%), and gait disturbance (9%). Serious adverse events (3%) were no more common than with placebo. There were insufficient data for direct comparisons with other active treatments, and only third tier evidence for other painful conditions. AUTHORS' CONCLUSIONS: There was no top tier evidence that was unequivocally unbiased. Second tier evidence, with potentially important residual biases, showed that gabapentin at doses of 1200 mg or more was effective for some people with some painful neuropathic pain conditions. The outcome of at least 50% pain intensity reduction is regarded as a useful outcome of treatment by patients, and the achievement of this degree of pain relief is associated with important beneficial effects on sleep interference, fatigue, and depression, as well as quality of life, function, and work. About 35% achieved this degree of pain relief with gabapentin, compared with 21% for placebo. Over half of those treated with gabapentin will not have worthwhile pain relief. Results might vary between different neuropathic pain conditions, and the amount of evidence for gabapentin in neuropathic pain conditions except postherpetic neuralgia and painful diabetic neuropathy, and in fibromyalgia, is very limited. The levels of efficacy found for gabapentin are consistent with those found for other drug therapies in postherpetic neuralgia and painful diabetic neuropathy.

Summary Study participants with HIV-associated sensory polyneuropathy (HIV-SN) had higher plasma triglyceride concentrations, depression, anxiety, catastrophizing scores, and prevalence of insomnia than HIV participants without HIV-SN., HIV-associated sensory neuropathy (HIV-SN) is a frequent complication of HIV infection and a major source of morbidity. A cross-sectional deep profiling study examining HIV-SN was conducted in people living with HIV in a high resource setting using a battery of measures which included the following: parameters of pain and sensory symptoms (7 day pain diary, Neuropathic Pain Symptom Inventory [NPSI] and Brief Pain Inventory [BPI]), sensory innervation (structured neurological examination, quantitative sensory testing [QST] and intraepidermal nerve fibre density [IENFD]), psychological state (Pain Anxiety Symptoms Scale-20 [PASS-20], Depression Anxiety and Positive Outlook Scale [DAPOS], and Pain Catastrophizing Scale [PCS], insomnia (Insomnia Severity Index [ISI]), and quality of life (Short Form (36) Health Survey [SF-36]). The diagnostic utility of the Brief Peripheral Neuropathy Screen (BPNS), Utah Early Neuropathy Scale (UENS), and Toronto Clinical Scoring System (TCSS) were evaluated. Thirty-six healthy volunteers and 66 HIV infected participants were recruited. A novel triumvirate case definition for HIV-SN was used that required 2 out of 3 of the following: 2 or more abnormal QST findings, reduced IENFD, and signs of a peripheral neuropathy on a structured neurological examination. Of those with HIV, 42% fulfilled the case definition for HIV-SN (n = 28), of whom 75% (n = 21) reported pain. The most frequent QST abnormalities in HIV-SN were loss of function in mechanical and vibration detection. Structured clinical examination was superior to QST or IENFD in HIV-SN diagnosis. HIV-SN participants had higher plasma triglyceride, concentrations depression, anxiety and catastrophizing scores, and prevalence of insomnia than HIV participants without HIV-SN.

Summary Intrathecal delivery of histone deacetylase inhibitors ameliorates hypersensitivity in models of neuropathic pain. This effect may be mediated at the level of the spinal cord through inhibition of HDAC1 function., Histone deacetylase inhibitors (HDACIs) interfere with the epigenetic process of histone acetylation and are known to have analgesic properties in models of chronic inflammatory pain. The aim of this study was to determine whether these compounds could also affect neuropathic pain. Different class I HDACIs were delivered intrathecally into rat spinal cord in models of traumatic nerve injury and antiretroviral drug–induced peripheral neuropathy (stavudine, d4T). Mechanical and thermal hypersensitivity was attenuated by 40% to 50% as a result of HDACI treatment, but only if started before any insult. The drugs globally increased histone acetylation in the spinal cord, but appeared to have no measurable effects in relevant dorsal root ganglia in this treatment paradigm, suggesting that any potential mechanism should be sought in the central nervous system. Microarray analysis of dorsal cord RNA revealed the signature of the specific compound used (MS-275) and suggested that its main effect was mediated through HDAC1. Taken together, these data support a role for histone acetylation in the emergence of neuropathic pain.

We propose that predictive validity of tests for analgesia may be improved by looking to reinstate specific, innate behaviors suppressed by pain, e.g., burrowing, because effective plasma concentrations in the rat are closer to effective clinical plasma concentrations than those generally used in rodent reflex withdrawal assays.

Neuropathic pain results from damage to the nervous system due to many diverse processes. It causes persistent, distressing pain that is reputedly unresponsive to conventional analgesics. Treatment is best managed in a multi-therapy pain clinic setting and pharmacotherapy is one facet of this treatment. There is no single effective drug treatment and patients have been empirically treated with antidepressants and antiepileptics in the past. This review focuses on evidence from randomized controlled trials to assess the efficacy of the currently available drug treatments.

Background To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors. Methods The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935) and externally validated in a large population-based cohort (N = 295). Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy. Results Random Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN. Conclusions Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model’s performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.