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Pain. 2017 Aug;158(8):1446-1455. doi: 10.1097/j.pain.0000000000000935.

Stratifying patients with peripheral neuropathic pain based on sensory profiles: algorithm and sample size recommendations.

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1
aDepartment of Pain Medicine, BG University Hospital Bergmannsheil GmbH, Ruhr-University Bochum, Bochum, GermanybCenter of Biomedicine and Medical Technology Mannheim CBTM, Medical Faculty Mannheim, Heidelberg University, Heidelberg, GermanycINSERM U-987, Centre d'Evaluation et de Traitement de la Douleur, CHU Ambroise Paré, Boulogne-Billancourt, FrancedUniversité Versailles-Saint-Quentin, Versailles, FranceeNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomfDepartment of Neurology, BG University Hospital Bergmannsheil GmbH, Ruhr-University Bochum, Bochum, GermanygDepartment of Neurology, Danish Pain Research Center, Aarhus University Hospital, Aarhus, DenmarkhDepartment of Anaesthesiology, Critical Care Medicine, Pain Therapy and Palliative Care, Pain Center Lake Starnberg, Benedictus Hospital Tutzing, Tutzing, GermanyiAnaesthesiological clinic, Klinikum rechts der Isar, Technische Universität München, Munich, GermanyjDivision of Neurological Pain Research and Therapy, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Campus Kiel, Kiel, GermanykDepartments of Helsinki University Central Hospital, Helsinki, FinlandlEtera Mutual Pension Insurance Company, Helsinki, FinlandmDepartment of Pain Management and Research, Division of Emergencies and Critical Care, Oslo University Hospital, Oslo, NorwaynDepartment of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, SwedenoPain Research, Department of Surgery and Cancer, Imperial College, London, United KingdompH. Lundbeck A/S, Copenhagen, DenmarkqDepartment of Physiology and Pharmacology, Karolinska Institute, Stockholm, SwedenrNeuroscience Technologies, Ltd, Barcelona, SpainsDiabetes Research Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United KingdomtBrain Function Research Group, School of Physiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South AfricauDepartment of Neurology, Odense University Hospital, Odense, DenmarkvDepartment of Neurology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.

Abstract

In a recent cluster analysis, it has been shown that patients with peripheral neuropathic pain can be grouped into 3 sensory phenotypes based on quantitative sensory testing profiles, which are mainly characterized by either sensory loss, intact sensory function and mild thermal hyperalgesia and/or allodynia, or loss of thermal detection and mild mechanical hyperalgesia and/or allodynia. Here, we present an algorithm for allocation of individual patients to these subgroups. The algorithm is nondeterministic-ie, a patient can be sorted to more than one phenotype-and can separate patients with neuropathic pain from healthy subjects (sensitivity: 78%, specificity: 94%). We evaluated the frequency of each phenotype in a population of patients with painful diabetic polyneuropathy (n = 151), painful peripheral nerve injury (n = 335), and postherpetic neuralgia (n = 97) and propose sample sizes of study populations that need to be screened to reach a subpopulation large enough to conduct a phenotype-stratified study. The most common phenotype in diabetic polyneuropathy was sensory loss (83%), followed by mechanical hyperalgesia (75%) and thermal hyperalgesia (34%, note that percentages are overlapping and not additive). In peripheral nerve injury, frequencies were 37%, 59%, and 50%, and in postherpetic neuralgia, frequencies were 31%, 63%, and 46%. For parallel study design, either the estimated effect size of the treatment needs to be high (>0.7) or only phenotypes that are frequent in the clinical entity under study can realistically be performed. For crossover design, populations under 200 patients screened are sufficient for all phenotypes and clinical entities with a minimum estimated treatment effect size of 0.5.

PMID:
28595241
PMCID:
PMC5515640
DOI:
10.1097/j.pain.0000000000000935
[Indexed for MEDLINE]
Free PMC Article

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