Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy
Neuropad is currently a categorical visual screening test that identifies diabetic patients at risk of foot ulceration. The diagnostic performance of Neuropad was compared between the categorical and continuous (image-analysis (Sudometrics)) outputs to diagnose diabetic peripheral neuropathy (DPN)....
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2015/847854 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551757148848128 |
|---|---|
| author | Georgios Ponirakis Hassan Fadavi Ioannis N. Petropoulos Shazli Azmi Maryam Ferdousi Mohammad A. Dabbah Ahmad Kheyami Uazman Alam Omar Asghar Andrew Marshall Mitra Tavakoli Ahmed Al-Ahmar Saad Javed Maria Jeziorska Rayaz A. Malik |
| author_facet | Georgios Ponirakis Hassan Fadavi Ioannis N. Petropoulos Shazli Azmi Maryam Ferdousi Mohammad A. Dabbah Ahmad Kheyami Uazman Alam Omar Asghar Andrew Marshall Mitra Tavakoli Ahmed Al-Ahmar Saad Javed Maria Jeziorska Rayaz A. Malik |
| author_sort | Georgios Ponirakis |
| collection | DOAJ |
| description | Neuropad is currently a categorical visual screening test that identifies diabetic patients at risk of foot ulceration. The diagnostic performance of Neuropad was compared between the categorical and continuous (image-analysis (Sudometrics)) outputs to diagnose diabetic peripheral neuropathy (DPN). 110 subjects with type 1 and 2 diabetes underwent assessment with Neuropad, Neuropathy Disability Score (NDS), peroneal motor nerve conduction velocity (PMNCV), sural nerve action potential (SNAP), Deep Breathing-Heart Rate Variability (DB-HRV), intraepidermal nerve fibre density (IENFD), and corneal confocal microscopy (CCM). 46/110 patients had DPN according to the Toronto consensus. The continuous output displayed high sensitivity and specificity for DB-HRV (91%, 83%), CNFD (88%, 78%), and SNAP (88%, 83%), whereas the categorical output showed high sensitivity but low specificity. The optimal cut-off points were 90% for the detection of autonomic dysfunction (DB-HRV) and 80% for small fibre neuropathy (CNFD). The diagnostic efficacy of the continuous Neuropad output for abnormal DB-HRV (AUC: 91%, P=0.0003) and CNFD (AUC: 82%, P=0.01) was better than for PMNCV (AUC: 60%). The categorical output showed no significant difference in diagnostic efficacy for these same measures.
An image analysis algorithm generating a continuous output (Sudometrics) improved the diagnostic ability of Neuropad, particularly in detecting autonomic and small fibre neuropathy. |
| format | Article |
| id | doaj-art-b6a01776d40b46a99b5b0ed96ce663f3 |
| institution | Kabale University |
| issn | 2314-6745 2314-6753 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-b6a01776d40b46a99b5b0ed96ce663f32025-02-03T06:00:35ZengWileyJournal of Diabetes Research2314-67452314-67532015-01-01201510.1155/2015/847854847854Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic NeuropathyGeorgios Ponirakis0Hassan Fadavi1Ioannis N. Petropoulos2Shazli Azmi3Maryam Ferdousi4Mohammad A. Dabbah5Ahmad Kheyami6Uazman Alam7Omar Asghar8Andrew Marshall9Mitra Tavakoli10Ahmed Al-Ahmar11Saad Javed12Maria Jeziorska13Rayaz A. Malik14Research Division, Weill Cornell Medical College in Qatar, Qatar Foundation, P.O. Box 24144, Education City, Doha, QatarInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKResearch Division, Weill Cornell Medical College in Qatar, Qatar Foundation, P.O. Box 24144, Education City, Doha, QatarInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKInstitute of Human Development, Centre for Endocrinology & Diabetes, Faculty of Medical and Human Sciences, University of Manchester and NIHR/Wellcome Trust Clinical Research Facility, Manchester M13 9NT, UKResearch Division, Weill Cornell Medical College in Qatar, Qatar Foundation, P.O. Box 24144, Education City, Doha, QatarNeuropad is currently a categorical visual screening test that identifies diabetic patients at risk of foot ulceration. The diagnostic performance of Neuropad was compared between the categorical and continuous (image-analysis (Sudometrics)) outputs to diagnose diabetic peripheral neuropathy (DPN). 110 subjects with type 1 and 2 diabetes underwent assessment with Neuropad, Neuropathy Disability Score (NDS), peroneal motor nerve conduction velocity (PMNCV), sural nerve action potential (SNAP), Deep Breathing-Heart Rate Variability (DB-HRV), intraepidermal nerve fibre density (IENFD), and corneal confocal microscopy (CCM). 46/110 patients had DPN according to the Toronto consensus. The continuous output displayed high sensitivity and specificity for DB-HRV (91%, 83%), CNFD (88%, 78%), and SNAP (88%, 83%), whereas the categorical output showed high sensitivity but low specificity. The optimal cut-off points were 90% for the detection of autonomic dysfunction (DB-HRV) and 80% for small fibre neuropathy (CNFD). The diagnostic efficacy of the continuous Neuropad output for abnormal DB-HRV (AUC: 91%, P=0.0003) and CNFD (AUC: 82%, P=0.01) was better than for PMNCV (AUC: 60%). The categorical output showed no significant difference in diagnostic efficacy for these same measures. An image analysis algorithm generating a continuous output (Sudometrics) improved the diagnostic ability of Neuropad, particularly in detecting autonomic and small fibre neuropathy.http://dx.doi.org/10.1155/2015/847854 |
| spellingShingle | Georgios Ponirakis Hassan Fadavi Ioannis N. Petropoulos Shazli Azmi Maryam Ferdousi Mohammad A. Dabbah Ahmad Kheyami Uazman Alam Omar Asghar Andrew Marshall Mitra Tavakoli Ahmed Al-Ahmar Saad Javed Maria Jeziorska Rayaz A. Malik Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy Journal of Diabetes Research |
| title | Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy |
| title_full | Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy |
| title_fullStr | Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy |
| title_full_unstemmed | Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy |
| title_short | Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy |
| title_sort | automated quantification of neuropad improves its diagnostic ability in patients with diabetic neuropathy |
| url | http://dx.doi.org/10.1155/2015/847854 |
| work_keys_str_mv | AT georgiosponirakis automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT hassanfadavi automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT ioannisnpetropoulos automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT shazliazmi automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT maryamferdousi automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT mohammadadabbah automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT ahmadkheyami automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT uazmanalam automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT omarasghar automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT andrewmarshall automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT mitratavakoli automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT ahmedalahmar automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT saadjaved automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT mariajeziorska automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy AT rayazamalik automatedquantificationofneuropadimprovesitsdiagnosticabilityinpatientswithdiabeticneuropathy |