Keywords
diabetic neuropathy - sudoscan - autonomic dysfunction - diabetes screening - electrochemical
skin conductance - small fiber neuropathy
Introduction
Diabetic peripheral neuropathy (DPN) is length-dependent peripheral nerve damage arising
as a complication of type 1 diabetes (T1D) or type 2 diabetes (T2D) in up to 50% of
patients. DPN poses a substantial burden on patients, who can experience impaired
gait and loss of balance, predisposing them to falls and fractures, and neuropathic
pain, which is frequently difficult to treat and reduces quality of life. Advanced
DPN can lead to diabetic foot ulcers and nonhealing wounds that often necessitate
lower-limb amputation.[1]
Periodic screening for microvascular complications is key in diabetes care. These
include screening for neuropathy, nephropathy, and retinopathy in T2D from the time
of recognition, and in T1D, a few years after diagnosis.[2] Screening for neuropathy remains less advanced compared with nephropathy and retinopathy
due to a lack of standard tools and variability in symptoms.[3]
Sudoscan is a noninvasive device used in diabetes clinics to assess sudomotor function.
It measures the electrochemical skin conductance (ESC) of the hands and feet through
reverse iontophoresis and chronoamperometry. The test does not require any special
preparation. Patients place their hands and feet on electrodes, and the device measures
the ESC, which reflects the function of small nerve fibers responsible for sweat gland
activity.[4]
[5]
[6]
[7] This technology is particularly useful for detecting DPN and cardiovascular autonomic
neuropathy (CAN).[8]
[9]
Sudoscan is now more frequently adopted in internal medicine and diabetes clinics.
Most health insurance funders in many parts of the world generously reimburse the
cost of autonomic function testing procedures (CPT Code 95923). Understanding the
principles and practical considerations is essential for the effective clinical integration
of this tool.
Materials and Methods
This practice point aims to provide a concise, descriptive, practical account to optimize
its utility in the clinical setting. A narrative review approach was employed using
the PubMed database, focusing on literature published from 2011 to 2025. No specific
exclusions were applied. A total of 181 articles were retrieved in response to the
search term (Sudoscan). The search was expanded to make comparisons with other methods
and other contextual arguments. Articles were selected based on their relevance to
a predefined set of clinical questions. The questions covered indications, contraindications,
practical utility, clinical effectiveness, cost-effectiveness, interpretations, the
impact of age and ethnicity, the impact on treatment decisions, and limitations. The
practical aims of the article may have led to an overreliance on review articles and
guidelines and avoidance of detailed data presentations. To illustrate the interpretation
of Sudoscan findings in clinical practice, a series of enhanced, anonymized, real
clinical cases with normal, moderate, and advanced sudomotor dysfunction was used.
Emerging Themes
Indications and Contraindications
Sudoscan is indicated for assessing sudomotor function, which is particularly useful
in the early detection of DPN and diabetic autonomic neuropathy (DAN).[6]
[7] It is also used to screen for CAN in patients with diabetes.[8]
[9]
Sudoscan use is inappropriate in the presence of skin lesions, wounds, or dermatological
conditions (such as ulcers, severe eczema, or infections) on the palms or soles, as
these may interfere with electrode contact and the accuracy of ESC measurements. Additionally,
patients with implanted electronic medical devices should avoid Sudoscan, as the device
uses low-voltage electrical currents for reverse iontophoresis, and safety in this
population has not been established. The test is also inappropriate in individuals
unable to maintain adequate contact with the electrodes due to severe motor impairment,
cognitive dysfunction, or major foot deformities. The diagnostic utility of Sudoscan
in acute neuropathic conditions or the presence of significant peripheral edema is
uncertain. These limitations are consistent with the technical and clinical descriptions
in the medical literature, which emphasize the need for intact, clean, and dry skin
for reliable results.[4]
[5]
[10]
Practical Utility
Sudoscan can quickly and objectively screen for DPN.[6]
[7] It can also assess CAN, with the CAN risk score correlating with traditional cardiovascular
reflex tests.[8]
[9]
[11] While Sudoscan is a valuable screening tool, it has limitations. The specificity
of Sudoscan for diagnosing CAN is moderate, and it should be used in conjunction with
other diagnostic modalities for a comprehensive assessment.[5]
[9]
[11]
Clinical Effectiveness
Sudoscan measures the ESC of the hands and feet, which reflects the function of small
nerve fibers responsible for sweat gland activity. Studies have shown that Sudoscan
is effective in detecting DPN and DAN. For instance, foot ESC values are significantly
lower in patients with DPN than in those without DPN and healthy controls. Similarly,
patients with DAN exhibit lower ESC values than those without DAN.[7]
[8]
[9]
[10]
[11]
The sensitivity and specificity of Sudoscan for detecting DPN and small fiber neuropathy
(SFN) have been evaluated in multiple clinical contexts ([Table 1]). In summary, sensitivity ranges widely from 53 to 92%, which is high, especially
for SFN and autonomic neuropathy. Specificity varies equally widely (49–99%), often
depending on ESC thresholds and population. Area under the curve values suggest good
overall diagnostic accuracy (≥0.75) in most contexts. Finally, performance is strongest
in moderate-to-severe SFN and CAN.[5]
[6]
[7]
[8]
[10]
[11]
Table 1
Sudoscan diagnostic performance across studies
|
Population/context
|
Diagnostic target
|
Sensitivity
|
Specificity
|
Study/source
|
|
People with diabetes
|
Diabetic peripheral neuropathy
|
87.5%
|
76.2%
|
Selvarajah et al (2015)
|
|
Mixed diabetic and nondiabetic neuropathies
|
Moderate SFN (ESC ≤70 μS)
|
91.0%
|
97.0%
|
Riveline et al (2023)
|
|
Severe SFN (ESC ≤50 μS)
|
91.0%
|
99.0%
|
|
People with type 2 diabetes
|
Foot ESC for DPN
|
70.0%
|
85.0%
|
Krieger et al (2018)
|
|
Hand ESC for DPN
|
53.0%
|
50.0%
|
|
Chinese population
|
Cardiovascular autonomic neuropathy
|
85.6%
|
76.1%
|
Jin et al (2018)
|
|
Diabetes cohort (United States)
|
Diabetic neuropathy
|
78.0%
|
92.0%
|
Casellini et al (2013)
|
|
People with diabetes
|
CAN risk score-based
|
92.0%
|
49.0%
|
Yajnik et al (2013)
|
Abbreviations: CAN, cardiovascular autonomic neuropathy; DPN, diabetic peripheral
neuropathy; ESC, electrochemical skin conductance; SFN, small fiber neuropathy.
Sudoscan is a noninvasive test that can be performed quickly, making it highly efficient
for routine clinical use. Traditional methods, such as nerve conduction studies (NCS)
and skin biopsies, are more time-consuming and invasive, increasing costs related
to patient discomfort and procedural complications.[5]
[6]
[7] The operational costs of Sudoscan are relatively low compared with traditional methods.
NCS and skin biopsies require specialized equipment and trained personnel. Sudoscan,
on the other hand, can be operated by nonspecialized staff, reducing labor costs.[4]
[5]
[6] Sudoscan's ease of use and quick turnaround time make it feasible for integration
into routine diabetes care, ensuring adherence to guidelines such as those from the
American Diabetes Association.[2] This service can improve patient compliance and reduce the need for multiple clinic
visits.[3]
[4]
[5]
[6]
Interpretation
Lower ESC values in the hands and feet indicate impaired sudomotor function, which
is associated with DPN and CAN. Specific cutoff values have been established to aid
diagnosis.[12] The reports use three color codes for normal (green), moderate impairment (amber),
and severe impairment (red) sudomotor functions. The provided illustrations are real
anonymized clinical cases assessed by the same instrument. [Figs. 1]
[2] to [3] show the illustrations from three technical reports. [Fig. 1] depicts an example of the illustration from a report of a person with normal sudomotor
function. [Fig. 2] shows a report of a patient with moderate sudomotor dysfunction. [Fig. 3] demonstrates a report of a patient with markedly impaired sudomotor function. For
educational purposes, patients are provided with a simplified illustrated report.
[Fig. 4] illustrates the visual components of three reports, including one for patients with
normal findings and another for those with significantly abnormal results. The software
provides reports with spaces for comments and plans of further action, a signature,
and a doctor's seal. Example actions may include intensifying glycemic control, investigating
other causes of neuropathy, or referral for specialized neurological evaluations.
The documentation can be linked directly to the electronic medical records or manually
scanned and uploaded as ancillary tests.
Fig. 1 Professional report of the mean ESC scores, regional conductance, and symmetry of
a patient with normal sudomotor function in the feet (upper) and the hands (lower).
The reports use three color codes for normal (green), moderate impairment (amber),
and severe impairment (red) sudomotor functions. ESC, electrochemical skin conductance.
Fig. 2 Professional report of the mean ESC scores, regional conductance, and symmetry of
a patient with moderate sudomotor dysfunction. The reports use three color codes for
normal (green), moderate impairment (amber), and severe impairment (red) sudomotor
functions. ESC, electrochemical skin conductance.
Fig. 3 Professional report of the mean ESC scores, regional conductance, and symmetry of
a patient with advanced sudomotor dysfunction. The reports use three color codes for
normal (green), moderate impairment (amber), and severe impairment (red) sudomotor
functions. ESC, electrochemical skin conductance.
Fig. 4 Illustrations from reports given to patients: The upper panel shows a report of a
patient with normal sudomotor function, the middle panel shows a moderately impaired
sudomotor function, and the lower panel shows a report of a patient with a severely
impaired sudomotor function. The reports use three color codes for normal (green),
moderate impairment (amber), and severe impairment (red) sudomotor functions.
Impact of Age and Ethnicity
Additionally, factors such as age and ethnicity can influence ESC values, which should
be taken into account when interpreting the results.[11]
[12]
[13]
[14] Age has a statistically significant but weak negative correlation with Sudoscan
ESC; the values decrease slightly with increasing age, but the effect size is small
and unlikely to be clinically significant in most cases. Ethnicity has a notable impact:
African American, Indian, and Chinese individuals have lower mean ESC values compared
with white populations, indicating that ethnicity-specific reference ranges may be
necessary for accurate interpretation. Sex does not significantly affect ESC values;
studies have shown no meaningful difference in ESC between men and women at either
the hands or feet.[13]
[14]
[15] Hydration status was not directly addressed in the literature. However, as Sudoscan
measures sweat gland function, severe dehydration could theoretically reduce sweat
production and thus ESC, but this has not been established.[15]
Comparison of Diagnostic Tools for Peripheral Neuropathy
Several methods are available for diagnosing DPN. [Table 2] highlights the differences between the characteristics of Sudoscan, quantitative
sensory testing, NCS, and skin biopsies.[5]
[12]
[13]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35] Sudoscan, NCS, skin biopsy, and corneal confocal microscopy (CCM) are complementary
tools for evaluating peripheral neuropathy, each with distinct strengths and limitations.
A detailed discussion of the issue is out of the scope of this practice point. However,
NCS is best suited for large fiber neuropathy. In contrast, skin biopsy is the gold
standard for SFN. Sudoscan is a rapid screening tool for autonomic small fiber dysfunction,
and CCM is a noninvasive method for assessing small fibers. Selection depends on the
clinical context and the type of fiber of interest and the local access.
Table 2
Comparison of the various attributes of Sudoscan, QST, CCM, NCS, and skin biopsy as
diagnostic tools for peripheral neuropathy
|
Feature
|
Sudoscan[5]
[7]
[10]
|
QST[24]
[25]
|
CCM[27]
[28]
[29]
[30]
[31]
[32]
[33]
|
NCS[21]
[22]
|
Skin biopsy[23]
[34]
[35]
|
|
Primary assessment
|
Sudomotor/autonomic small fiber function
|
Sensory thresholds (thermal, vibratory)
|
Corneal nerve fiber density and morphology
|
Large fiber function (motor and sensory nerves)
|
Small fiber nerve density in skin
|
|
Invasiveness
|
Noninvasive
|
Noninvasive
|
Noninvasive
|
Noninvasive
|
Minimally invasive
|
|
Subjectivity
|
Objective
|
Subjective (patient-dependent)
|
Objective
|
Objective
|
Objective
|
|
Sensitivity
|
70–87% (for DPN)
|
69–78% (small fiber nerve)
|
14–88%
|
36–85% (large fiber nerve)
|
Up to 91%
|
|
Specificity
|
76–92%
|
70–84%
|
75–96%
|
High for large fiber neuropathy
|
Up to 99%
|
|
Clinical utility
|
Screening and monitoring (especially DPN)
|
Detects both large and small fiber abnormalities
|
Research and longitudinal tracking
|
Gold standard for large fiber involvement
|
Gold standard for small fiber neuropathy
|
|
Cost
|
Moderate
|
Low
|
High
|
Moderate
|
Moderate
|
|
Accessibility
|
High (outpatient clinics)
|
Moderate–high (neurology clinics)
|
Low (research centers)
|
High (widely available)
|
Moderate (requires laboratory access)
|
|
Technical requirements
|
Minimal training, no preparation
|
Trained operator, patient cooperation
|
Specialized equipment and training
|
Electrophysiology laboratory, trained personnel
|
Biopsy skills + histology laboratory
|
|
Time to results
|
3–5 min
|
20–60 min
|
15–30 min (imaging + analysis)
|
30–90 min (including interpretation)
|
Days (for laboratory processing)
|
|
Strengths
|
Rapid, point-of-care, correlates with clinical signs
|
Functional assessment of pain and sensory abnormalities
|
Visualizes nerve regeneration and degeneration
|
Objective, well-validated, reliable for large fiber disease
|
Quantitative, validated, high sensitivity/specificity
|
|
Limitations
|
Less established for nondiabetic neuropathy
|
Patient-dependent, not a standalone diagnostic
|
High cost, access
|
Cannot detect small fiber neuropathy
|
Invasive, sampling error possible
|
Abbreviations: CCM, corneal confocal microscopy; DPN, diabetic peripheral neuropathy;
NCS, nerve conduction studies; QST, quantitative sensory testing.
Impact on Treatment Decisions
Management decisions may be supported by providing a quick, noninvasive, and objective
assessment of sudomotor function, which is crucial for detecting DPN and CAN. Given
its high sensitivity and moderate specificity, Sudoscan can be particularly useful
in the following ways. Early detection of neuropathic complications allows for timely
intervention. Early identification of DPN and CAN should prompt clinicians to intensify
glycemic control and implement lifestyle modifications to prevent progression.[4]
[6]
[13] Sudoscan may monitor the progression of neuropathy over time. Regular assessments
can help evaluate the effectiveness of therapeutic interventions and adjust treatment
plans accordingly.[3]
[10] Sudoscan facilitates adherence to guidelines that recommend annual neuropathy assessments
for patients with diabetes.[4]
[5] Incorporation of the reports in patients' electronic health records should help
longitudinal monitoring. However, no evidence supports a role for monitoring change
in therapeutic trials.[35] The CAN risk score provided by Sudoscan helps stratify patients based on their risk
of autonomic dysfunction. This information can help clinicians in prioritizing patients
for more detailed autonomic testing and tailored management strategies.[4]
[11]
Limitations
For a practice point article, a narrative review was considered the most suitable
approach. However, the nature of the narrative review could limit its scientific rigor.
The research is limited by its relatively small literature volume (n = 181), restricted study populations, and limited ethnic diversity, among other potential
confounders. Additionally, this article focused on practical applications to help
better utilize this technology, rather than providing exhaustive coverage of the subject.
However, available data on sensitivity and specificity in different contexts are included
in [Table 1]. Also, qualitative comparisons were made with other diagnostic tools in [Table 2] for completeness.
Conclusion
Sudoscan represents a practical and noninvasive tool for the early detection and monitoring
of DPN and DAN. Its ease of use makes it well-suited for integration into routine
diabetes care, especially in primary and outpatient clinical settings. By enabling
the rapid and objective assessment of sudomotor function, Sudoscan supports timely
interventions that may help mitigate disease progression and improve long-term outcomes
for individuals with diabetes. However, while Sudoscan offers significant advantages
in terms of efficiency and accessibility, it should not be used in isolation. Its
moderate specificity and susceptibility to confounding variables—such as age and ethnicity—necessitate
its use in conjunction with other established diagnostic modalities, including NCS
and cardiovascular reflex tests, particularly when definitive diagnosis or treatment
decisions are being made.
Future research should standardize ESC thresholds across diverse populations and clinical
settings, and further explore the role of Sudoscan in longitudinal disease monitoring.
In addition, studies evaluating the impact of Sudoscan-guided interventions on patient
outcomes and health care resource utilization would strengthen its position in clinical
algorithms. Studies should assess how integrating Sudoscan into diabetes clinics affects
long-term morbidity, adherence, and cost savings. Overall, Sudoscan enhances the clinician's
ability to detect neuropathic complications early and tailor care more precisely,
advancing personalized diabetes management in an increasingly complex health care
landscape.
