Small Fiber NeuropathyNCT04611048
Establishing Normative Values for Thermal Detection and Pain Threshold Established by the Psi Method
A study testing Thermal QST (CDT, WDT, CPT, HPT) with the method of limits, Thermal QST (cold and warm detection, heat pain) with the psi method and Neurological examination in people with Small Fiber Neuropathy, run by Université Catholique de Louvain at 1 site in Belgium.
Trial summary
The study aims to compare different methods to assess thermal detection ability in diabetic patients, as a way to monitor and diagnose neurological complications of diabetes mellitus. Read more about this study
- Who is it for?
- Ages 40 to 79 yearsHealthy volunteers can take part
- Conditions the study is for
- Small Fiber Neuropathy
- What is being tested?
- Thermal QST (CDT, WDT, CPT, HPT) with the method of limits, Thermal QST (cold and warm detection, heat pain) with the psi method and Neurological examination
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 80 participants
- Where
- 1 siteBelgium
- Study dates
- 10 Oct 2020to 1 Jan 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 79 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- -
Who cannot join
- Alcohol beverage intake >3 units/day
- Habitual substance abuse
- History of chemotherapy
- Scar or dermatological condition at the site of stimulation (forearm and hands, leg and foot)
- History of neurological, psychiatric or metabolic disorder other than Diabetes Mellitus (screening will be performed with the patient)
- Currently taking drugs that could induce neuropathy (screening will be performed with the patient)
- For healthy controls: Suffering of chronic pain
Trial locations and contacts
1 site in 1 country.
About this study
The study aims to compare different methods to assess thermal detection ability in diabetic patients, as a way to monitor and diagnose neurological complications of diabetes mellitus.
Read the detailed description
Diabetic polyneuropathy is a frequent complication of diabetes mellitus. The impairment of peripheral nerve fibre function can be very variable, predominantly affecting large-diameter fibres (subserving touch), small-diameter fibres (subserving thermonociception), or both.
Thermal detection threshold evaluation can be used to quantify the extent of function loss (hypoesthesia) and, to a lesser extent, gain (hyperesthesia) in patients with thermonociceptive impairments. They are important features of quantitative sensory testing (QST) protocols (Rolke, Baron, et al., 2006; Rolke, Magerl, et al., 2006) and are pivotal to the determination of sensory phenotypes (Baron et al., 2017; Raputova et al., 2017). Their role is particularly important in the diagnostic workup of neuropathies affecting small fibers (i.e., the subgroup of primary afferents responsible for thermonociception and autonomic functions) such as painful diabetic neuropathies (Terkelsen et al., 2017; Tesfaye et al., 2010).
Currently, clinical measurements of thermal detection thresholds are mainly performed using the method of limits (Fruhstorfer, Lindblom, \& Schmidt, 1976), in which a continuous heating or cooling ramp (usually at a slow rate, 1°C/s in the case of the DFNS QST protocol (Rolke, Magerl, et al., 2006)) is applied to the skin of the patient who is instructed to press a button as soon as he/she feels a warm or cold sensation. The detection threshold is then considered to be the temperature reached at the moment the patient pressed the button. The method of limits has been known for a long time to be methodologically biased due to its reliance on the reaction time (Yarnitsky \& Ochoa, 1991), which lead to an overestimation of the threshold value corresponding to the temperature change that occurred between detection and it's signalling by a motor response. This is problematic as reaction times are under the influence of decision and motor reaction response speeds which may be affected by factors irrelevant to the assessment of sensory discrimination, such as cognitive or motor impairments.
A methodologically sounder approach for threshold measurement is the method of levels or constant stimuli (Kingdom \& Prins, 2010). A number of preselected stimulus intensities are presented a number of times in random order and the subject is asked whether he/she felt each stimulus. Unlike the method of limits, this approach is not biased by decision speed and motor function. Furthermore, this method enables the fitting of a psychometric function (probability of detection as a function of stimulus intensity) to the results, therefore moving thermal detection performance assessments from the outdated High Threshold Theory framework to that of the currently leading Signal Detection Theory (Kingdom \& Prins, 2010). Whereas High Threshold Theory conceptualized detection as an ON/OFF process (below threshold, no detection occurs, above threshold detection always occurs), Signal Detection Theory sees detection as a probabilistic process (each stimulus intensity is associated with a probability of detection). This theoretical framework implies to redefine the threshold as the stimulus intensity for which detection probability equates 0.5. In addition to the threshold, the psychometric function is also defined by its slope, i.e. the rate at which detection probability changes around the value of the threshold. . Unfortunately, the method of levels has some important drawbacks. First, it is time consuming as it requires collecting responses to a large amount of stimuli (usually several hundreds) (Gescheider, 1997). Second, the range of stimulus intensities must be approximately centered on the actual threshold value and cover the transition range of detection probability.
To overcome these limitations, several adaptive procedures have been proposed. These procedures actively adjust the intensity of the presented stimuli depending on the previous responses of the subject (Kingdom \& Prins, 2010). In the present study, we implemented for the first time the Psi method (a Bayesian adaptive algorithm proposed by Kontsevich and Tyler (1999)) to estimate the thresholds and slopes of the psychometric function for heat and cold detection. This algorithm associates each potential values of slope and threshold with a probability, updates this probability distribution based on the response recorded after each stimulus presentation (detected/not detected), and selects the next stimulus intensity so that the response to its presentation maximizes the entropy (i.e. the uncertainty around the values of slope and threshold) reduction.
In this study, we will test healthy controls with the conventional method of limit and the new psi method, in order to establish normative values for the new test.
- Conditions
- Small Fiber Neuropathy
- Being tested
- Thermal QST (CDT, WDT, CPT, HPT) with the method of limits, Thermal QST (cold and warm detection, heat pain) with the psi method, Neurological examination
- Medical subject headings
- Small Fiber Neuropathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Thermal QST (CDT, WDT, CPT, HPT) with the method of limitsDiagnostic test
The testing will be carried out using the classical method of limits, i.e. a continuous heating/cooling stimulus will be applied to the skin at a rate of 1°C/s, until the subject signals that he/she felt the targeted sensation by pressing a button (Rolke, Baron, et al., 2006). The temperature reached by the time the subject pressed the button will be considered the threshold. Baseline temperature will be set at 32°C. The stimuli will not go lower than 0°C and higher than 50°C.
Thermal QST (cold and warm detection, heat pain) with the psi methodDiagnostic test
The Psi algorithm is a method using Bayesian statistics to determine not only the threshold but also the slope of the psychometric function (relationship between the intensity of a stimulus and its detection probability; Kingdom \& Prins, 2010). The algorithm selects the next stimulus intensity to be the most informative on the parameters of interest, based on the prior probability density. After each stimulation, the subject will be asked if he/she felt it (as painful) or not. Based on that answer, a posterior probability density is computed. This posterior is then used as prior for the next stimulation. The stimuli will last maximum 1 s and temperature will be kept between 0°C and 60°C.
Neurological examinationDiagnostic test
A standardized neurological examination will be performed to assess all the items included in the Utah Early Neuropathy Scale (UENS ) (Singleton et al., 2008) and the Toronto Clinical Neuropathy Score (TCNS)(Perkins, Olaleye, Zinman, \& Bril, 2001). This will include: questions about symptoms (presence or absence of foot pain, numbness, tingling, weakness, imbalance and upper limb symptoms); evaluation of knees and ankles deep tendon reflexes; evaluation of pinprick, temperature, light touch, vibration (128 Hz tuning fork) compared to that of an unaffected sites (e.g., sternum); position sensation in the big toes; mapping of pinprick sensitivity in the lower leg; and evaluation of the active extension of the big toes. Pinprick evaluation will be performed using a disposable pin designed for that purpose (Medipin, The United-Kingdom).
| Study group | Role | What they receive |
|---|---|---|
| Main study | Experimental | Several electrophysiological and behavioural tests will be performed to properly diagnose the patients/check that the healthy controls do not suffer of neuropathy. Receives: Diagnostic Test: Thermal QST (CDT, WDT, CPT, HPT) with the method of limits; Diagnostic Test: Thermal QST (cold and warm detection, heat pain) with the psi method; Diagnostic Test: Neurological examination |
What is being measured
The primary outcome is the main question the study is designed to answer.
Fitted psychometric functions for cold detection obtained with the psi method
The alpha (threshold; °C) and beta (slope, °C^-1) terms of the fitted logistic equation
Measured: baseline
Fitted psychometric functions for warm detection obtained with the psi method
The alpha (threshold; °C) and beta (slope, °C^-1) terms of the fitted logistic equation
Measured: baseline
Fitted psychometric functions for cold pain obtained with the psi method
The alpha (threshold; °C) and beta (slope, °C^-1) terms of the fitted logistic equation
Measured: baseline
Fitted psychometric functions for heat pain obtained with the psi method
The alpha (threshold; °C) and beta (slope, °C^-1) terms of the fitted logistic equation
Measured: baseline
Cold detection threshold (protocol of the German Research Network on Neuropathic Pain - DFNS)
average of 3 measurement of the threshold with the method of limits, as described in the protocol of the German Research Network on Neuropathic Pain - DFNS
Measured: baseline
Cold pain threshold (protocol of the German Research Network on Neuropathic Pain - DFNS)
average of 3 measurement of the threshold with the method of limits, as described in the protocol of the German Research Network on Neuropathic Pain - DFNS
Measured: baseline
Warm detection threshold (protocol of the German Research Network on Neuropathic Pain - DFNS)
average of 3 measurement of the threshold with the method of limits, as described in the protocol of the German Research Network on Neuropathic Pain - DFNS
Measured: baseline
Heat Pain threshold (protocol of the German Research Network on Neuropathic Pain - DFNS)
average of 3 measurement of the threshold with the method of limits, as described in the protocol of the German Research Network on Neuropathic Pain - DFNS
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Nov 2020
- Study start10 Oct 2020
- Primary completion1 Jul 2022 (estimated)
- Study completion1 Jan 2023 (estimated)
Why it stopped: Due to covid, the recruitment plan was disrupted and recruitment is on hold for a undetermined period of time.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Small Fiber NeuropathyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- Healthy volunteers will be tested in this study, in order to establish normative values for the new procedure (psi method)
- Masking
- None (open label)
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Université Catholique de Louvain
- Sponsor type
- University, hospital or other
- Collaborators
- Cliniques universitaires Saint-Luc- Université Catholique de Louvain
- Responsible party
- Sponsor
- Investigators
- André Mouraux, MD, PhD, Principal investigator, Université Catholique de Louvain
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04611048
- Sponsor's protocol ID
- PsiNorm
- Status last verified
- Sep 2021
- First submitted
- 23 Jul 2020
- Last updated
- 8 Oct 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: SAP will be available on the OSF repository by January 2021. Anonymized data will be freely available (stored on an OSF repository) as supporting material for publication.
Study at a glance
From the registry record, last updated by the sponsor on 8 Oct 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Establishing Normative Values for Thermal Detection and Pain Threshold Established by the Psi Method
- ClinicalTrials.gov ID
- NCT04611048
- Acronym / sponsor ID
- PsiNorm
- Conditions
- Small Fiber Neuropathy
- Study type
- Interventional
- Recruitment status
- SuspendedReason stopped: Due to covid, the recruitment plan was disrupted and recruitment is on hold for a undetermined period of time.
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- Université Catholique de LouvainUniversity, hospital or other
- Collaborators
- Cliniques universitaires Saint-Luc- Université Catholique de Louvain
- Study start
- 10 Oct 2020 (actual)
- Primary completion
- 1 Jul 2022 (estimated)
- Study completion
- 1 Jan 2023 (estimated)
- First posted
- 2 Nov 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The study aims to compare different methods to assess thermal detection ability in diabetic patients, as a way to monitor and diagnose neurological complications of diabetes mellitus.
Detailed description
Diabetic polyneuropathy is a frequent complication of diabetes mellitus. The impairment of peripheral nerve fibre function can be very variable, predominantly affecting large-diameter fibres (subserving touch), small-diameter fibres (subserving thermonociception), or both.
Thermal detection threshold evaluation can be used to quantify the extent of function loss (hypoesthesia) and, to a lesser extent, gain (hyperesthesia) in patients with thermonociceptive impairments. They are important features of quantitative sensory testing (QST) protocols (Rolke, Baron, et al., 2006; Rolke, Magerl, et al., 2006) and are pivotal to the determination of sensory phenotypes (Baron et al., 2017; Raputova et al., 2017). Their role is particularly important in the diagnostic workup of neuropathies affecting small fibers (i.e., the subgroup of primary afferents responsible for thermonociception and autonomic functions) such as painful diabetic neuropathies (Terkelsen et al., 2017; Tesfaye et al., 2010).
Currently, clinical measurements of thermal detection thresholds are mainly performed using the method of limits (Fruhstorfer, Lindblom, \& Schmidt, 1976), in which a continuous heating or cooling ramp (usually at a slow rate, 1°C/s in the case of the DFNS QST protocol (Rolke, Magerl, et al., 2006)) is applied to the skin of the patient who is instructed to press a button as soon as he/she feels a warm or cold sensation. The detection threshold is then considered to be the temperature reached at the moment the patient pressed the button. The method of limits has been known for a long time to be methodologically biased due to its reliance on the reaction time (Yarnitsky \& Ochoa, 1991), which lead to an overestimation of the threshold value corresponding to the temperature change that occurred between detection and it's signalling by a motor response. This is problematic as reaction times are under the influence of decision and motor reaction response speeds which may be affected by factors irrelevant to the assessment of sensory discrimination, such as cognitive or motor impairments.
A methodologically sounder approach for threshold measurement is the method of levels or constant stimuli (Kingdom \& Prins, 2010). A number of preselected stimulus intensities are presented a number of times in random order and the subject is asked whether he/she felt each stimulus. Unlike the method of limits, this approach is not biased by decision speed and motor function. Furthermore, this method enables the fitting of a psychometric function (probability of detection as a function of stimulus intensity) to the results, therefore moving thermal detection performance assessments from the outdated High Threshold Theory framework to that of the currently leading Signal Detection Theory (Kingdom \& Prins, 2010). Whereas High Threshold Theory conceptualized detection as an ON/OFF process (below threshold, no detection occurs, above threshold detection always occurs), Signal Detection Theory sees detection as a probabilistic process (each stimulus intensity is associated with a probability of detection). This theoretical framework implies to redefine the threshold as the stimulus intensity for which detection probability equates 0.5. In addition to the threshold, the psychometric function is also defined by its slope, i.e. the rate at which detection probability changes around the value of the threshold. . Unfortunately, the method of levels has some important drawbacks. First, it is time consuming as it requires collecting responses to a large amount of stimuli (usually several hundreds) (Gescheider, 1997). Second, the range of stimulus intensities must be approximately centered on the actual threshold value and cover the transition range of detection probability.
To overcome these limitations, several adaptive procedures have been proposed. These procedures actively adjust the intensity of the presented stimuli depending on the previous responses of the subject (Kingdom \& Prins, 2010). In the present study, we implemented for the first time the Psi method (a Bayesian adaptive algorithm proposed by Kontsevich and Tyler (1999)) to estimate the thresholds and slopes of the psychometric function for heat and cold detection. This algorithm associates each potential values of slope and threshold with a probability, updates this probability distribution based on the response recorded after each stimulus presentation (detected/not detected), and selects the next stimulus intensity so that the response to its presentation maximizes the entropy (i.e. the uncertainty around the values of slope and threshold) reduction.
In this study, we will test healthy controls with the conventional method of limit and the new psi method, in order to establish normative values for the new test.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- Healthy volunteers will be tested in this study, in order to establish normative values for the new procedure (psi method)
| Arm | Type | Receives |
|---|---|---|
| Main study | Experimental | Diagnostic Test: Thermal QST (CDT, WDT, CPT, HPT) with the method of limits, Diagnostic Test: Thermal QST (cold and warm detection, heat pain) with the psi method, Diagnostic Test: Neurological examinationSeveral electrophysiological and behavioural tests will be performed to properly diagnose the patients/check that the healthy controls do not suffer of neuropathy. |
Who may qualify
- Ages
- 40 to 79 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- -
Key exclusion criteria (7 listed)
- Alcohol beverage intake >3 units/day
- Habitual substance abuse
- History of chemotherapy
- Scar or dermatological condition at the site of stimulation (forearm and hands, leg and foot)
- History of neurological, psychiatric or metabolic disorder other than Diabetes Mellitus (screening will be performed with the patient)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Thermal QST (CDT, WDT, CPT, HPT) with the method of limitsThe testing will be carried out using the classical method of limits, i.e. a continuous heating/cooling stimulus will be applied to the skin at a rate of 1°C/s, until the subject signals that he/she felt the targeted sensation by pressing a button (Rolke, Baron, et al., 2006). The temperature reached by the time the subject pressed the button will be considered the threshold. Baseline temperature will be set at 32°C. The stimuli will not go lower than 0°C and higher than 50°C. | Diagnostic test | Main study |
| Thermal QST (cold and warm detection, heat pain) with the psi methodThe Psi algorithm is a method using Bayesian statistics to determine not only the threshold but also the slope of the psychometric function (relationship between the intensity of a stimulus and its detection probability; Kingdom \& Prins, 2010). The algorithm selects the next stimulus intensity to be the most informative on the parameters of interest, based on the prior probability density. After each stimulation, the subject will be asked if he/she felt it (as painful) or not. Based on that answer, a posterior probability density is computed. This posterior is then used as prior for the next stimulation. The stimuli will last maximum 1 s and temperature will be kept between 0°C and 60°C. | Diagnostic test | Main study |
| Neurological examinationA standardized neurological examination will be performed to assess all the items included in the Utah Early Neuropathy Scale (UENS ) (Singleton et al., 2008) and the Toronto Clinical Neuropathy Score (TCNS)(Perkins, Olaleye, Zinman, \& Bril, 2001). This will include: questions about symptoms (presence or absence of foot pain, numbness, tingling, weakness, imbalance and upper limb symptoms); evaluation of knees and ankles deep tendon reflexes; evaluation of pinprick, temperature, light touch, vibration (128 Hz tuning fork) compared to that of an unaffected sites (e.g., sternum); position sensation in the big toes; mapping of pinprick sensitivity in the lower leg; and evaluation of the active extension of the big toes. Pinprick evaluation will be performed using a disposable pin designed for that purpose (Medipin, The United-Kingdom). | Diagnostic test | Main study |
Primary outcome
Fitted psychometric functions for cold detection obtained with the psi method
Time frame: baseline
The alpha (threshold; °C) and beta (slope, °C^-1) terms of the fitted logistic equation
Fitted psychometric functions for warm detection obtained with the psi method
Time frame: baseline
The alpha (threshold; °C) and beta (slope, °C^-1) terms of the fitted logistic equation
Fitted psychometric functions for cold pain obtained with the psi method
Time frame: baseline
The alpha (threshold; °C) and beta (slope, °C^-1) terms of the fitted logistic equation
Fitted psychometric functions for heat pain obtained with the psi method
Time frame: baseline
The alpha (threshold; °C) and beta (slope, °C^-1) terms of the fitted logistic equation
Cold detection threshold (protocol of the German Research Network on Neuropathic Pain - DFNS)
Time frame: baseline
average of 3 measurement of the threshold with the method of limits, as described in the protocol of the German Research Network on Neuropathic Pain - DFNS
Cold pain threshold (protocol of the German Research Network on Neuropathic Pain - DFNS)
Time frame: baseline
average of 3 measurement of the threshold with the method of limits, as described in the protocol of the German Research Network on Neuropathic Pain - DFNS
Warm detection threshold (protocol of the German Research Network on Neuropathic Pain - DFNS)
Time frame: baseline
average of 3 measurement of the threshold with the method of limits, as described in the protocol of the German Research Network on Neuropathic Pain - DFNS
Heat Pain threshold (protocol of the German Research Network on Neuropathic Pain - DFNS)
Time frame: baseline
average of 3 measurement of the threshold with the method of limits, as described in the protocol of the German Research Network on Neuropathic Pain - DFNS
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Oct 2021.
- Individual participant dataSharing planned: Yes. SAP will be available on the OSF repository by January 2021. Anonymized data will be freely available (stored on an OSF repository) as supporting material for publication.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
André Mouraux, MD, PhD principal investigator
Université Catholique de Louvain
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Thermal QST (CDT, WDT, CPT, HPT) with the method of limits, Thermal QST (cold and warm detection, heat pain) with the psi method and Neurological examination in people with Small Fiber Neuropathy, run by Université Catholique de Louvain at 1 site in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: paused, as of the sponsor's last update on 8 Oct 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Fitted psychometric functions for cold detection obtained with the psi method (time frame: baseline). The study lists 8 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Oct 2021.