Research Paper
Peripheral neuropathic pain: a mechanism-related
organizing principle based on sensory profiles
Ralf Baron
a,
*, Christoph Maier
b
, Nadine Attal
c,d
, Andreas Binder
a
, Didier Bouhassira
c,d
, Giorgio Cruccu
e
,
Nanna B. Finnerup
f
, Maija Haanp ¨a¨a
g,h
, Per Hansson
i,j
, Philipp H ¨ullemann
a
, Troels S. Jensen
f
, Rainer Freynhagen
k
,
Jeffrey D. Kennedy
l
, Walter Magerl
m
, Tina Mainka
b,n
, Maren Reimer
a
, Andrew S.C. Rice
o
,M¨arta Segerdahl
p,q
,
Jordi Serra
r
,S¨oren Sindrup
s
, Claudia Sommer
t
, Thomas T ¨olle
u
, Jan Vollert
b,m
, Rolf-Detlef Treede
m
, on behalf of the
German Neuropathic Pain Research Network (DFNS), and the EUROPAIN, and NEUROPAIN consortia
Abstract
Patients with neuropathic pain are heterogeneous in etiology, pathophysiology, and clinical appearance. They exhibit a variety of pain-
related sensory symptoms and signs (sensory profile). Different sensory profiles might indicate d ifferent classes of neurobiological
mechanisms, and hence subgroups with different sensory profiles might respond differently to treatment. The aim of the investigation was
to identify subgroups in a large sample of patients with neuropathic pain using hypothesis-free statistical methods on the database of 3
large multinational research networks (German Resear ch Network on Neuropathic Pain (DFNS), IMI-Europain , and Neuropain).
Standardized quantitative sensory testing was used in 902 (test cohort) and 233 (validation cohort) patients with peripheral neuropathic
pain of different etiologies. For subgrouping, we performed a cluster analysis using 13 quantitative sensory testing parameters. Three
distinct subgroups with characteristic sensory profiles were identified and replicated. Cluster 1 (sensory loss, 42%) showed a loss of small
and large fiber function in combination with paradoxical heat sensations. Cluster 2 (thermal hyperalgesia, 33%) was characterized by
preserved sensory functions in combination with heat and cold hyperalgesia and mild dynamic mechanical allodynia. Cluste r 3
(mechanical hyperalgesia, 24%) was characterized by a loss of small fiber function in combination with pinprick hyperalgesia and dynamic
mechanical allodynia. All clusters occurred across etiologies but frequencies differed. We present a new approach of subgrouping
patients with peripheral neuropathic pain of different etiologies according to intrinsic sensory profiles. These 3 profiles may be related to
pathophysiological mechanisms and may be useful in clinical trial design to enrich the study population for treatment responders.
Keywords: Neuropathic pain, Sensory signs, Clinical trials, QST, Epidemiology
1. Introduction
Neuropathic pain syndromes develop after a les ion or disease
affecting the somatosensory nervous system.
22,58
Despite
advances in understanding the complex neurobiology of pain,
the pharmacological management of these syndromes
remains insufficient and several promising drugs have failed
in late-stage development.
21,35
Thus, there is a need to predict
treatment r esponders both for clinical practice, in which e ven
first-line treatments are beneficial in less than 50% of patients,
and f or clinical trial design, in which a negative outcome may be
due t o a low responder rate rather than uniform inefficacy of the
treatment.
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
a
Division of Ne urological Pain Research and Therapy, Departmen t of Neurolog y, Universit ¨atsklinikum Schleswig-Holstein, Campus Kiel, Germany,
b
Department of
Pain Medicine, BG University Hospital Bergmannsheil GmbH, Ruhr-University Bochum, Bochum, Germany,
c
INSERM U-987, Centre d’Evaluation et de Traitement
de la Doul eur, CHU Ambroise Par ´e, Boulogne-Billancourt, France,
d
Universit ´e Versaill es-Sai nt-Quentin, Versailles, France,
e
Department of Ne urology and
Psychiatry, Sapienza University, Roma, Italy,
f
Department of Neurol ogy, Danish Pain Res earch Center, Aarhus University Hospital, Aarhus, Denmark,
g
Helsinki
University Central Hospital, Helsinki, Finland,
h
Etera Mutual Pension Insurance Company, Helsinki, Finland,
i
Department of Pain Management and Research,
Division of Emergencies and Critical Care, Oslo University Hospital, Oslo, Norway,
j
Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm,
Sweden,
k
Department of Anaesthesiology, Critical Care Medicine, Pain Therapy & Palliative Care, Pain Center Lake Starnberg, Benedictus Hospital Tutzing, Tutzing,
Germany, and Klinik f ¨ur An ¨asthesie, Technische Universit ¨at M ¨unchen, Munich , Germany,
l
Neuroscience Discovery Research, Eli Lilly and Company, Indiana polis,
IN, USA.,
m
Department of Neurophysiology, Center of Biomedicine and Medical Technology Mannheim CBTM, Medical Faculty Mannheim, Heidelberg University,
Mannheim, Germany,
n
Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,
o
Pain Research, Department of Surgery
and Cancer, Imperial College, London, United Kingdom,
p
Clinical R&D Neurol ogy, Lundbeck A/S, Copenhagen, Denmark,
q
Department of P hysiology and
Pharmacology, Karolinska Institute, Stockholm, Sweden,
r
Neuroscience Technologies SLP, Barcelona, Spain,
s
Department of Neurology, Odense University
Hospital, Odense, Denmark,
t
Department of Neurology, University Hospital W ¨urzburg, W ¨urzburg, Germany,
u
Department of Neurology, Klinikum rechts der Isar,
Technische Universit ¨at M ¨unchen, Munich, Germany
*Corresponding author. Address: Division o f Neurological Pain Research and Therapy, Dept. of Neurology, Universit ¨atsklinikum Schleswig-Holstein, Campus Kiel, House 41,
Arnold-Heller-Strasse 3, 24105 Kiel, Germany. Tel.: 149 431 500 23805; fax: 149 431 500 23914. E-mail address: r.baron@neurologie.uni-kiel.de (R. Baron).
PAIN 158 (2017) 261–272
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Although all neuropathic pain disorders have a common
denominator, ie, damage of the somatosensory nervous system,
the underlying etiologies and pathogeneses of these damages
are distinct. Furthermore, the patterns of sensory signs and
symptoms that develop after neuropathy vary between the
different etiologies and even between individual patients with
neuropathies of the same etiology.
5,40
The expression of these
sensory signs, the mosaic of hyperalgesia, allodynia, and sensory
loss, which we call the individual somatosensory profile, reflects
pathophysiological mechanisms in damaged and surviving
afferent nerve fibers such as conduction block, ectopic impulse
generation, peripheral sensitization, and central sensitization.
10
Historically, neuropathic pain has been classified, investigated
in clinical trials, and treated on the basis of the underlying etiology.
However, recognising the heterogeneity of pain mechanisms
other classification schemes might be more appropriate.
2,64
Thus, an entirely different strategy in which pain is differentiated
on the basis of the underlying mechanisms has been proposed
emphasizing the rationale for a treatment approach directed at
mechanisms rather than diseases.
30,34,44,66
Pathophysiological mechanisms of pain generation cannot be
readily examined in patients. Nevertheless, the expression of
some sensory signs can be related to mechanisms, eg, heat
hyperalgesia to peripheral sensitization
36
and pinprick hyper-
algesia to central sensitization.
6,55
Thus, the individual somato-
sensory profile may reveal some clues of pathophysiological
dysfunctions of afferent processing.
5,40
The aim of this investigation was to identify patient subgroups
with distinct sensory profiles in a large sample of patients with
neuropathic pain from a wide range of etiologies collected in 3
multinational research networks. Instead of testing previously
published hypotheses of associations between sensory profiles
and mechanisms, this large data set enabled us to apply
hypothesis-free statistical segmentation methods. This way we
explored the intrinsic patterning of sensory profiles in a represen-
tative spectrum of patients with peripheral neuropathic pain. The
number and type of intrinsic patterns—if reproducible—can then
be related back to pathophysiological and pharmacological
mechanisms in future studies.
We used a standardized protocol of quantitative sensory
testing (QST) in patients with peripheral neuropathic pain of
different etiologies with the following aims:
(1) to describe and analyse typical patterns of sensory signs in
more than 900 patients,
(2) to subgroup the patients on the basis of characteristic sensory
profiles,
(3) to establish a sensory profile-based organizing principle of
neuropathic pain, and
(4) to replicate the results in a second independent cohort of more
than 200 patients.
2. Materials and methods
2.1. Consortia
Three large multinational consortia collected phenotypic data of
patients with peripheral neuropathic pain (test cohort): the
German Research Network on Neuropathic Pain (DFNS), the
EUROPAIN, and the NEUROPAIN collaboration. The gathered
data comprised demographic, psychometric, and clinical data as
well as results of a standardized quantitative sensory assessment
that were captured in one joined central database of the DFNS.
40
Each study center used a computer-assisted program for
data entry locally in each center (Neuroquast, Statconsult,
Magdeburg, Germany). For data export into the central database,
a special data export file was created, encrypted, and sent to the
central database through e-mail. All centers and investigators
underwent a strict quality assessment and certification process to
allow future pooling of data across sites and countries.
39,63
A
confirmatory analysis of heterogeneity between the participating
centers in healthy subjects and patients painful neuropathies
showed a high degree of homogeneity between the different
centers, making it possible to analyze the database as
a homogenous group.
62
The DFNS (http://www.neuropathischer-schmerz.de) was
established to investigate mechanisms and treatments of
neuropathic pain and consists of 10 German centers. The study
protocol was approved by the ethics committee of the University
Hospital Kiel, Germany, and subsequently by the ethics
committees of all participating centers. The EUROPAIN consor-
tium (http://www.imieuropain.org) consists of academic study
groups working on pain research from Germany, Denmark, and
the United Kingdom, a Spanish SME and Europe’s most active
pharmaceutical companies working in the pain field. The ethics
committees of each center approved the study protocol in-
dividually. The NEUROPAIN project is an investigator-initiated
project (sponsored by Pfizer Ltd) consisting of several research-
ers in the field of neuropathic pain research within Europe
(principle investigator [R.B.]) and aims to characterize subgroups
of patients with neuropathic pain. The ethics committees of each
participating center approved the study protocols individually.
2.2. Inclusion criteria
Patients with peripheral neuropathic pain of several etiologies
(polyneuropathy [PNP], peripheral nerve injury [PNI], postherpetic
neuralgia [PHN], and radiculopathy [RAD]) were included
(Table 1).
2.2.1. German Research Network on Neuropathic Pain
Patients were included when the following criteria for each
respective diagnosis were fulfilled:
(1) polyneuropathy: according to the clinical criteria published by
England et al.
18
Peripheral nerve injury: presence of somato-
sensory signs in the innervation territory of the injured nerve
according to clinical examination and/or sensory neurography.
Postherpetic neuralgia: presence of neuropathic pain for more
than 3 months in the affected area after healing of the acute
herpes zoster rash. Radiculopathy: history of nerve root
damage and consistent neurological findings.
Table 1
Patient characteristics.
Original data set Validation data set P
Age, y 58 6 14 57 6 14 0.834
Female, n (%) 429/902 (48) 97/233 (42) 0.106
Pain
Current 6.0 6 3.1 5.9 6 2.1 0.275
Duration ,1 y 193/902 (21%) 39/233 (17%) 0.116
Duration .5 y 201/902 (22%) 46/233 (21%) 0.402
Aetiology ,0.001
Polyneuropathy 512/902 (57%) 113/233 (48%)
Peripheral nerve injury 227/902 (25%) 110/233 (47%)
PHN 88/902 (10%) 10/233 (4%)
Radiculopathy 75/902 (8%) —
P values are given for the chi-square approximate test or analysis of variance.
PHN, postherpetic neuralgia.
262 R. Baron et al.
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2.2.2. Europain and Neuropain consortia
The main inclusion criterion was recurrent or ongoing peripheral
neuropathic pain with a pain intensity $3 (Numerical Rating
Scale, 0-10). Special inclusion criteria for each diagnosis and type
of pain were as follows:
polyneuropathy: pathological nerve conduction studies or
pathologically decreased vibration detection threshold (VDT) at
2 of 4 sites (,5/8) at the lower limb,
33,42
which could not be
explained by another disease or pain with PNP-type of location
and evidence of small fiber neuropathy based on skin punch
biopsy, laser-evoked potentials, or bedside thermal testing,
which could not be explained by another disease.
Peripheral nerve injury: history of traumatic nerve injury of the
distal upper or lower limb and sensory motor abnormalities
confined to the innervation territory of the injured nervous
structure.
Postherpetic neuralgia: unilateral zoster rash in the facial or
thoracic area with postzoster scarring, hypopigmentation, or
hyperpigmentation in the affected dermatome or sensory deficit
in the area of the previous zoster rash determined by bedside
testing.
Radiculopathy: pain in the L5 and/or S1 dermatome and
positive straight leg raising test or sensory deficit within the
matching dermatome or diminished Achilles tendon reflex for S1
lesions and magnetic resonance imaging of the lumbar spine
confirming nerve root impairment by a herniated intervertebral
disk or electromyography showing denervation in the L5 or S1
territory.
2.3. Exclusion criteria
Patients with trigeminal neuralgia, central neuropathic pain, and
complex regional pain syndromes were excluded because it is
believed that the underlying pathophysiological mechanisms are
distinct from classical peripheral neuropathic pain etiologies.
Further exclusion criteria were age ,18 years, missing informed
consent, communication problems, pain treatment by topical local
anaesthetics for $7 days in the last 4 months or by topical
capsaicin in the last 6 months, other pain locations with pain
intensities $6on$15 d/mo, other severe systemic or focal
diseases of the central nervous system, spinal canal stenosis,
peripheral vascular disease, pending litigation, major cognitive or
psychiatric disorders, and treatment with an effect on neuropathic
pain for any conditions except the inclusion criterion. By the latter
criterion, we intended to assure that pain was the leading
diagnosis and not depression. Because patient selection was
done by each individual center, we do not know how many
patients were excluded for this reason. Data sets were excluded in
case of incomplete records (eg, no precise diagnosis docu-
mented, more than one QST variable missing in the affected area,
no information about age, sex, or other demographic data) (Fig. 1).
All subjects signed written informed consent according to the
Declaration of Helsinki for participation in the respective study and
for transfer of the study records into the central database. The
ethics committee of each center approved the study protocol
individually. The study is reported according to the STROBE
statement. Several centers contributed to more than one
consortium, which contributed to uniform clinical standards
across consortia.
2.4. Quantitative sensory testing and questionnaires
To assure process quality of QST, the investigators of each center
underwent standardized training courses for the performance of
QST.
63
The standardized protocol of DFNS was used for QST as
described in detail previously.
51,62
Quantitative sensory testing was conducted at the most painful
site within the affected body area (test area) and the mirror-image
contralateral area (control area). In cases of PNP, the cheek was
assessed as the control area. The procedure started with a brief
demonstration of each test in an area not to be included in the
actual QST assessment, followed by QST of the control area and
then QST of the test areas.
4
The QST assessed the function of small and large afferent
fibers. The standardized assessment contained 13 different
thermal and mechanical tests. The following parameters were
tested: thermal detection thresholds for the perception of cold
(cold detection threshold [CDT]) and warmth (warm detection
threshold [WDT]), paradoxical heat sensation (PHS) during the
procedure of alternating warm and cold stimuli (TSL), thermal pain
thresholds for cold (cold pain threshold [CPT]) and hot stimuli
(heat pain threshold [HPT]), mechanical detection thresholds
(MDT) for touch and vibration (VDT), mechanical pain sensitivity
(MPS) including thresholds for pinprick (mechanical pain thresh-
old [MPT]) and blunt pressure (pressure pain threshold [PPT]),
a stimulus–response–function for pinprick sensitivity (MPS) and
dynamic mechanical allodynia (dynamic mechanical allodynia
[DMA]), and pain summation to repetitive pinprick stimuli (wind-up
ratio [WUR]). For all parameters, negative (loss of function) and
positive (gain of function) phenomena were assessed.
In the DFNS, the German version of the Center for Epidemi-
ological Studies—Depression (CES-D
48
) was used for assess-
ment of depression, in Neuropain, the Hospital Anxiety and
Depression Scale (HADS
71
). Within the DFNS, the Neuropathic
Pain Scale (NPS
25
) was used, in Europain and Neuropain, the
Neuropathic Pain Symptom Inventory (NPSI
9
). Two items are
highly comparable in these questionnaires, describing the
stabbing and burning quality of spontaneous pain.
2.5. Statistical analyses
2.5.1. Z transformation and quantitative sensory testing
profiles
In a control group of normal volunteers,
39,47,51
cold pain, HPTs,
and VDTs as well a s the numbers of PHSs duri ng the TSL
procedure were normally distributed. All other parameters were
normally distributed in log space and were transformed
logarithmically before statistical analysis. To compare individ ual
QST data of patients or of a group of patients with age- and sex-
matched control data, standard normal distributio ns of the
patient data were calculated for each individual QST variable
(z transformation, exception PHS and DMA). The calculation
was based on measurements in 180 healthy controls.
51
Z
scores o f zero represent a value corresponding precisely to the
mean of the healthy control cohort, z scores above “0” indicate
a gain of func tion when the patient was more sensitive to the test
stimuli compared with controls (hyperaesthesia or hype ralge-
sia), whereas z scores b elow “0” indicate a loss of function
referring to a lower sensitivity of the patient (hypoaesthesia or
hypoalgesia). Paradoxical heat sensation and DMA normally do
not o ccur in hea lthy subjects. Thus, z transformation was not
possible for these parameters because one would divide by
zero. For PHS and DMA percentages are plotted against o riginal
data: occurrences of PHS (0-3), log numerical ratings scale for
DMA (0-100), and are inserted on the right side of the sensory
profile (Fi g. 2).
By this procedure, sensory profiles of an individual patient or
a group of patients can be displayed graphically on one common
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scale of sensory gain or loss as well as the 95% confidence
interval for healthy subjects.
2.5.2. Subgrouping of patients by cluster analysis
A cluster analysis was performed to unravel different and
distinguishable subgroups of patients who are characterized by
typical QST profiles. The 11 z-transformed QST variables (WDT,
TSL, CPT, HPT, PPT, MPT, MPS, WUR, MDT, CDT, and VDT)
were the primary basis for the analysis. In addition, PHS was
transformed to a binary 0/2-variable showing absence (coded as
0) or presence (coded as 12) of pathological values; this puts
PHS into similar metrics as the 11 z-transformed variables where
1.96 SD above or below the reference data mean of z 5 0is
considered abnormal, and PHS is abnormal except for the lower
extremity in older males. Dynamic mechanical allodynia occurred
in a wide range of intensity values. By comparing the log-intensity
scores with the impact of DMA on the quality of life of the patients,
it was useful to use 3 different intensity levels. According to these
observations, DMA was transformed to a 0/2/3-variable repre-
senting no DMA (coded as 0), DMA with average pain ratings
below 1 (coded as 12), and DMA with average pain ratings
between 1 and 100 (coded as 13). Accordingly, all 13 variables
had a similar metric of means and variances, and we could use
the squared Euclidian distance as the distance measure giving
equal weight to all QST variables.
Because our data set is not computationally challenging, we
used the widely known clustering algorithm k-means as the
primary hypothesis-free analysis tool that divides the data set into
a predetermined number of k clusters.
38
The transformed DMA
and PHS variables were included into this procedure, because
the Euclidian distance is a meaningful distance measure for
a dichotomous or trichotomous variable. To make the cluster
analysis completely hypothesis-free, we did not make any a-priori
assumptions about the expected number of clusters. Instead, we
performed k-means analyses for k ranging from 2 to 10 and used
a series of well-established quality criteria from differing
mathematical background to determine the optimum number of
clusters:
(1) As a measure of fragmentation of the k-means solution for
a given number of k clusters, mean silhouette width per cluster
and the number of negative silhouette widths were used to
Figure 1. CONSORT flowchart for test data set. For cluster analysis of sensory profiles in patients with peripheral neuropathic pain, databases from 3 consortia
were combined: German Research Network on Neuropathic Pain (DFNS) (shaded in red), IMI-Europain, and Neuropain (shaded in blue). CRPS, complex regional
pain syndrome; DB, database.
264 R. Baron et al.
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exclude solutions that are likely to be artificial. Silhouette widths
that range between 21 and 11 for each patient in the analysis
may indicate that clusters overlap by a small degree of negative
values.
52
A high count of negative silhouettes or a cluster with
a mean silhouette width below zero indicates a cluster solution
that is highly fragmented. Thus, we excluded all solutions with
at least 1 cluster with a negative mean silhouette width, or over
10% negative silhouette widths.
(2) To validate a solution that is not dependent on the clustering
method, the remaining k-means solutions were compared with
a robust hierarchical agglomerative clustering method (maxi-
mum linkage) and an expectation maximization (EM) algo-
rithm.
15
We compared both solutions with the initial k-means
clustering through the adjusted rand index (ARI) and the
adjusted variation of information (AVI). Although the ARI
measures similarity on a scale from 0 to 1 ( high values are
preferable), the AVI measures dissimilarity on the same scale
(low values are preferable
49
).
(3) The final criterion for the decision between otherwise equally
good k-means solutions with different numbers of clusters was
the Bayesian information criterion (BIC), which captures the
gain of information by an increased number of clusters. The
higher number of clusters is preferable if the difference
between the BICs of both solutions (delta-BIC) is .10.
53
2.6. Validation data set
For external validation, patients with PNP, PNI, and PHN who
were collected either within the DFNS after the database closure
in 2010 (n 5 143) or within the Europain consortia for treatment
studies with oxcarbazepine and lidocaine (n 5 90)
13,14
(not
included in the flowchart, Fig. 1). Inclusion and exclusion criteria
for the patients collected within the DFNS were identical to the
criteria for the test data set. Inclusion and exclusion criteria for the
patients collected within Europain were identical except that
patients did not fill out questionnaires on pain qualities, de-
pression, and pain course over the last 4 weeks. Test and
validation data sets were equal in age, sex, pain duration, and
current pain intensity. After transforming the individual QST
values into z scores, a separate cluster analysis was performed
within this data set.
3. Results
3.1. Patients
In total, 1848 data sets were included into the combined DFNS/
Europain/Neuropain database. After applying the inclusion/
exclusion criteria, we could assess 902 patients with peripheral
neuropathic pain of different etiologies in the test cohort (Fig. 1).
The validation cohort consisted of 233 patients. Demographic
data of the entire patient cohort are shown in Table 1. Most of the
patients had long-lasting chronic pain between 1 and 5 years.
Pain intensity generally was moderate to severe with average
current pain ratings close to 6 on a 0-to-10 Likert scale without
relevant differences between the cohorts. Distributions of
etiologies differed between the 2 cohorts because of the absence
of patients with RAD in the validation cohort. Questionnaires were
available from 724 of the 902 patients in the test cohort, but not
from the validation cohort.
3.2. Cluster analysis
We used a distributive cluster analysis technique (k-means) that
separates data sets for maximal similarity within clusters and
dissimilarity between clusters in a multidimensional space (here:
13 dimensions) for a predetermined number of clusters. Therefore,
the first step was to identify the optimal number of clusters in
a data-driven manner (Table 2). We compared k-means cluster
solutions for 2 to 10 clusters. According to the frequency of
negative silhouette widths, we excluded the solutions with 4 to 10
clusters because they each presented at least 1 cluster with
Figure 2. Sensory profiles of the 3-cluster solution for test and replication
data sets. Sensory profiles of the 3 clusters presented as mean z scores 6 95%
confidence interval for the test data set (n 5 902, A) and the validation data
set (n 5 233, B). Note that z transformation eliminates differences due to test
site, sex, and age. Positive z scores indicate positive sensory signs
(hyperalgesia ), whereas negative z values indicate negative sensory signs
(hypoaesthesia and hypoalg esia) . Da shed lines: 95% confidence interva l fo r
healthy subje cts ( 21.96 , z ,11.96). Note that if th e mea n of a cl uster i s
within the shaded area, this does not imply that it does not differ from
a healthy cohort . Values are significantly different from those of healthy
subjects, if their 95% confidence i nterval does n ot cross the zero line. Insets
show nume ric pain ratings fo r d yna mic mechanical allo dynia (DMA) on
a logarithmic scale ( 0-100) and frequency of paradoxical heat sensation
(PHS) (0-3). Blue symbols: cluster 1 “sensory loss” (42% in A and 53% in B).
Red symbols: cluster 2 “thermal hyperalgesia” (33% in A and B). Yellow
symbols: cl uster 3 “m echani cal hyperalgesia” (24% in A and 14% in B). CDT,
cold detection threshold; CPT, cold pain threshold; HPT, heat pain
threshold; MDT, mechanical dete ct ion thre shold; MPS, mechanical p ain
sensitivity; MPT, mechanical pain threshold; NRS, Numerical Rating Scale;
PPT, pressure pain threshold; QST, quantitative sensory testing; TSL,
thermal sensory limen; VDT, vibratio n detection thresho ld; WDT, warm
detection threshold ; WUR , wind-up ratio.
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