- •Foreword
- •Preface
- •Contents
- •1.1 Introduction
- •1.2 Method
- •1.2.1 Databases
- •1.2.2 Dates
- •1.2.3 Keywords
- •1.2.4 Criteria for Inclusion
- •1.2.5 Criteria for Exclusion
- •1.2.6 Selection of Papers
- •1.3 Results
- •1.3.1 Subspecialty
- •1.3.2 Type of Telemedicine
- •1.3.3 Study Design
- •1.3.4 Final Conclusions of Papers
- •1.4 Discussion
- •References
- •2.1 Introduction
- •2.2 The Need for Diabetic Retinopathy Screening Programs
- •2.4 Guidelines for Referring Patients
- •2.7 Program Models for Diabetic Retinopathy Screening
- •2.9 Program Personnel and Operations
- •2.9.1 Primary Care Providers
- •2.9.2 Photographers
- •2.9.3 Clinical Consultants
- •2.9.4 Administrators
- •2.9.5 A Note to CEOs, Operations Directors, and Clinic Managers
- •2.10 Policies and Procedures
- •2.10.1 Sample Protocol 1
- •2.10.1.1 Diabetic Retinopathy Screening Services
- •Policy
- •Background
- •Procedure
- •2.10.2 Sample Protocol 2
- •2.10.2.1 Pupil Dilation Before Diabetic Retinopathy Photography
- •Policy
- •Background
- •Procedure
- •2.10.3 Sample Protocol 3
- •2.10.3.1 Diabetic Retinopathy Photography Review
- •Policy
- •Background
- •Procedure
- •2.11 Technical Requirements
- •2.11.1 Connectivity
- •2.11.2 Resolution
- •2.11.3 Color
- •2.11.4 Stereopsis
- •2.11.5 Compression
- •2.11.6 Enhancement
- •2.11.7 Pupil Dilation
- •2.11.8 Early California Telemedicine Initiatives Diabetic Retinopathy Screening
- •2.11.9 The American Indian Diabetes Teleophthalmology Grant Program
- •2.11.10 Central Valley EyePACS Diabetic Retinopathy Screening Project
- •2.12.1 Diabetic Retinopathy
- •2.12.1.1 ADA Guidelines Terms
- •2.12.1.2 Vitrectomy
- •References
- •3: Stereopsis and Teleophthalmology
- •3.1 Introduction
- •3.2 History of Stereopsis and Stereopsis in Ophthalmology
- •3.3 Technology and Photography
- •3.3.3 Imaging Fields
- •3.3.4 Image Viewing Techniques
- •3.3.5 Image Compression
- •3.4 Stereoscopic Teleophthalmology Systems
- •3.4.1 University of Alberta
- •3.4.4 Joslin Vision Network
- •3.5 Conclusion
- •References
- •4.1 Introduction
- •4.2 Methods
- •4.2.1 Main Outcome Measures
- •4.3 Results
- •4.3.1 Retinal Video Recording Versus Retinal Still Photography
- •4.3.2 Video Compression Analysis
- •4.4 Discussion
- •References
- •5.1 Introduction
- •5.1.1 Automated, Remote Image Analysis of Retinal Diseases
- •5.1.2 Telehealth
- •5.2 Design Requirements
- •5.2.1 Telehealth Network Architecture
- •5.2.2 Work Flow
- •5.2.3 Performance Evaluation of the Network
- •5.3 Automated Image Analysis Overview
- •5.3.1 Quality Assessment Module
- •5.3.2 Vascular Tree Segmentation
- •5.3.3 Quality Evaluation
- •5.4 Anatomic Structure Segmentation
- •5.4.1 Optic Nerve Detection
- •5.4.2 Macula
- •5.4.3 Lesion Segmentation
- •5.4.4 Lesion Population Description
- •5.4.5 Image Query
- •5.5 Summary
- •References
- •6.1 Introduction
- •6.3 Optical Coherence Tomography to Detect Leakage
- •References
- •7.1 Introduction
- •7.2 Patients and Methods
- •7.2.1 Participants
- •7.2.2 Methods
- •7.2.3 Statistics
- •7.3 Results
- •7.3.1 Reliability of Image Evaluation
- •7.3.2 Prevalence of Glaucomatous Optic Nerve Atrophy
- •7.4 Discussion
- •7.5 Perspectives
- •References
- •8.1 Introduction
- •8.1.2 Homology Between Retinal and Systemic Microvasculature
- •8.1.3 Need for More Precise CVD Risk Prediction
- •8.2.1 Retinal Microvascular Signs
- •8.2.2 Retinal Vessel Biometry
- •8.2.3 Newer Retinal Imaging for Morphologic Features of Retinal Vasculature
- •8.3 Associations of Retinal Imaging and CVD Risk
- •8.3.1.1 Risk of Pre-clinical CVD
- •8.3.1.2 Risk of Stroke
- •8.3.1.3 Risk of Coronary Heart Disease
- •8.3.2.1 Risk of Hypertension
- •8.3.2.2 Risk of Stroke
- •8.3.2.3 Risk of Coronary Heart Disease
- •8.3.2.4 Risk of Peripheral Artery Disease
- •8.3.3 Newer Morphologic Features of Retinal Vasculature
- •8.4 Retinal Imaging and Its Potential as a Tool for CVD Risk Prediction
- •References
- •9.1 Alzheimer’s Disease
- •9.2 Treatments
- •9.3 Diagnosis
- •9.6 Conclusions
- •References
- •10.1 Introduction
- •10.1.1 Stroke
- •10.1.2 Heart Disease
- •10.1.3 Arteriovenous Ratio
- •10.2 Purpose
- •10.3 Method
- •10.3.1 Medical Approach
- •10.3.2 Technical Approach
- •10.3.3 Output of Medical Data
- •10.4 Patients
- •10.5 Results
- •10.5.1 Medical History
- •10.5.2 Telemedical Evaluation of Retinal Vessels
- •10.5.2.1 Prevalence of Retinal Microangiopathy
- •10.5.2.2 Arteriovenous Ratio
- •10.5.2.3 PROCAM-Index
- •10.6 Discussion and Perceptive
- •10.6.1 Estimation of “Stroke Risk” Estimated by the Stage of Retinal Microangiopathy
- •References
- •11.1 Introduction
- •11.2 System Requirements
- •11.3 Fundus Camera
- •11.4 Imaging Procedure
- •11.4.1 Reading Center Procedure
- •11.5 Detection of Macular Edema
- •11.6 Implementation
- •11.7 Unreadable Images
- •11.7.1 Impact on Overall Diabetic Retinopathy Assessment Rates
- •11.7.2 Compliance with Recommendations
- •11.7.3 Challenges
- •11.7.4 Summary
- •References
- •12.1 Screening
- •12.2 Background
- •12.3 Historical Perspective in England
- •12.4 Methodology
- •12.4.1 The Aim of the Programme
- •12.5 Systematic DR Screening
- •12.6 Cameras for Use in the English Screening Programme
- •12.7 Software for Use in the English Screening Programme
- •12.9 Implementation in England
- •12.11 Quality Assurance
- •12.12 The Development of External Quality Assurance in the English Screening Programme
- •12.13 Information Technology (IT) Developments for the English Screening Programme
- •12.14 Dataset Development
- •12.15 The Development of External Quality Assurance Test Set for the English Screening Programme
- •12.16 Failsafe
- •12.17 The Epidemic of Diabetes
- •References
- •13.1 Introduction
- •13.2 Burden of Diabetes and Diabetic Retinopathy in India
- •13.3 Diabetic Retinopathy Screening Models
- •13.4 Need for Telescreening
- •13.5 Guidelines for Telescreening
- •13.6 ATA Categories of DR Telescreening Validation
- •13.7 Yield of Diabetic Retinopathy in a Telescreening Model
- •13.8 How Are Images Transferred
- •13.10 How Many Fields Are Enough for Diabetic Retinopathy Screening
- •13.11 Is Mydriasis Needed While Using Nonmydriatic Camera?
- •13.12 Validation Studies on Telescreening
- •13.12.1 Accuracy of Telescreening
- •13.12.2 Patient Satisfaction in Telescreening
- •13.12.3 Cost Effectivity
- •13.12.4 Telescreening for Diabetic Retinopathy: Our Experience
- •13.13 Future of Diabetic Retinopathy Screening
- •References
- •14.1 Introduction
- •14.2 Methods
- •14.3 Discussion
- •14.4 Conclusion
- •References
- •15.1 Introduction
- •15.1.1 Description of the EADRSI
- •15.5 State Support of Screening in the Safety Net
- •15.7 Screening Economics for Providers
- •15.8 Patient Sensitivity to Fees
- •15.9 Conclusion
- •References
- •16.1 Introduction
- •16.2 Setting Up the New Screening Model
- •16.2.1 Phase 1: Training
- •16.2.2 Phase 2: Evaluation of Agreement
- •16.2.3 Phase 3: Implementation of the Screening Model
- •16.3 Technologic Requirements
- •16.3.1 Data Management
- •16.3.2 Data Models
- •16.3.2.1 Data Scheme for Patient-Related Information
- •16.3.2.2 Data Scheme for Images
- •Fundus Camera VISUCAM Pro NM
- •PACS Server
- •ClearCanvas DICOM Visualizer
- •16.4 Results
- •16.4.1 Phase 2: Agreement Evaluation
- •16.4.2 Phase 3: Implementation of the Screening Model
- •16.5 Discussion
- •16.5.1 Evaluation of the Screening Model
- •16.5.2 Prevalence of DR
- •16.5.3 Quality Evaluation
- •16.6 Conclusion
- •References
- •17.1.3 Examination and Treatment
- •17.1.4 Limitations of Current Care
- •17.2 Telemedicine and ROP
- •17.2.2 Accuracy and Reliability of Telemedicine for ROP Diagnosis
- •17.2.3 Operational ROP Telemedicine Systems
- •17.2.4 Potential Barriers
- •17.3 Closing Remarks
- •17.3.1 Future Directions
- •References
- •18.1 Introduction
- •18.2 Neonatal Stress and Pain
- •18.3 ROP Screening Technique
- •18.4 Effect of Different Examination Techniques on Stress
- •18.5 Future of Retinal Imaging in Babies
- •References
- •19.1 Introduction
- •19.2 History of the Program
- •19.3 Telehealth Technologies
- •19.4 Impact of the Program
- •Selected References
- •Preamble
- •Introduction
- •Background
- •The Diabetic Retinopathy Study (DRS)
- •Mission
- •Vision
- •Goals
- •Guiding Principles
- •Ethics
- •Clinical Validation
- •Category 1
- •Category 2
- •Category 3
- •Category 4
- •Communication
- •Medical Care Supervision
- •Patient Care Coordinator
- •Image Acquisition
- •Image Review and Evaluation
- •Information Systems
- •Interoperability
- •Image Acquisition
- •Compression
- •Data Communication and Transmission
- •Computer Display
- •Archiving and Retrieval
- •Security
- •Reliability and Redundancy
- •Documentation
- •Image Analysis
- •Legal Requirements
- •Facility Accreditation
- •Privileging and Credentialing
- •Stark Act and Self-referrals
- •State Medical Practice Acts/Licensure
- •Tort Liability
- •Duty
- •Standards of Care
- •Consent
- •Quality Control
- •Operations
- •Customer Support
- •Originating Site
- •Transmission
- •Distant Site
- •Financial Factors
- •Reimbursement
- •Grants
- •Federal Programs
- •Other Financial Factors
- •Equipment Cost
- •Summary
- •Abbreviations
- •Appendices
- •Appendix A: Interoperability
- •Appendix B: DICOM Metadata
- •Appendix C: Computer-Aided Detection
- •Appendix D: Health Insurance Portability and Accountability Act (HIPAA)
- •Appendix F: Quality Control
- •Appendix H: Customer Support
- •Level 1
- •Level 2
- •Level 3
- •Appendix I: Reimbursement
- •Medicare
- •Medicaid
- •Commercial Insurance Carrier Reimbursement
- •Other Financial Factors
- •Disease Prevention
- •Resource Utilization
- •American Telemedicine Association’s Telehealth Practice Recommendations for Diabetic Retinopathy
- •Conclusion
- •References
- •Contributors
- •Second Edition
- •First Edition
- •Index
16 Diabetic Retinopathy Screening with Nonmydriatic Retinography by General Practitioners |
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163 |
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Table 16.1 Patients with diabetes screened with nonmydriatic retinography by the GPs |
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|
Total |
Referred |
Diabetic |
Unreadable |
False + |
False − |
||
|
|
|
retinopathy |
|
|
|
|
|
Year 1 |
1,223 |
297 (24%) |
85 (7%) |
26 (2%) |
186 (15%) |
11 (9%) |
||
Year 2 |
1,527 |
417 (27%) |
159 |
(10%) |
52 (3%) |
206 (13%) |
6 |
(5%) |
Year 3 |
1,979 |
501 (25%) |
185 |
(9%) |
17 (1%) |
298 (15%) |
2 |
(0.5%) |
Total |
4,729 |
1,215 (25%) |
429 |
(9%) |
95 (2%) |
690 (15%) |
19 (5%) |
|
P value |
NS |
NS |
NS |
|
0,03 |
NS |
<0.001 |
|
Table 16.2 Distribution of patients with diabetic retinopathy |
|
|
||||
|
Total |
Mild NPDR |
Moderate NPDR |
Severe NPDR |
PDR |
|
Year 1 |
85 (7%) |
57 (5%) |
21 (2%) |
4 (0.3%) |
3 (0.2%) |
|
Year 2 |
159 |
(10%) |
117 (8%) |
35 (2%) |
5 (0.3%) |
2 (0.1%) |
Year 3 |
185 |
(9%) |
151 (7%) |
28 (1%) |
6 (0.3%) |
0 |
Total |
429 |
(9%) |
325 (7%) |
84 (2%) |
15 (0.3%) |
5 (0.1%) |
NPDR nonproliferative diabetic retinopathy, PDR proliferative diabetic retinopathy
16.4.2Phase 3: Implementation of the Screening Model
The results corresponded to the period from January 2008 to December 2010, during which time, 4,729 patients with diabetes were referred for screening. In 3,514 (74%) cases, the images were considered normal, and an e-mail report was sent to the referring GPs with a recommendation for a new appointment in 1 year. In the other 1,215 (26%) cases, the images were sent for assessment by ophthalmologists. The ophthalmologists determined that 690 (15%) patients did not have DR (false positives), 429 (9%) had DR, and 96 (2%) patients had unreadable images (Table 16.1). Based on those data, the speciÞcity of the GPs participating in our study for detecting DR by nonmydriatic retinography was 83%.
Among the 429 patients with some degree of DR, 325 (7%) had mild nonproliferative DR (NPDR), 84 (2%) had moderate NPDR, 15 (0.3%) had severe NPDR, and Þve (0.1%) had PDR (Table 16.2).
Of the 690 patients without DR, 297 (6%) had a normal fundus and 393 (8%) had other retinal alterations. Of them, 189 (4%) had drusen, 94 (2%) had nevi, and 47 (1%) had lesions related to myopia. Less frequent causes of false positives were AMD, epiretinal membranes, and retinal vein occlusions.
To assess the percentage of false negatives, the retinal images of 360 patients (30 from each GP each year) whose fundi were considered normal were chosen randomly to be reread by ophthalmologists. Of them, 19 patients (5%) had some degree of DR; 15 patients were classiÞed with mild NPDR, one patient with moderate NPDR, and three patients (1%) had treatable DR with hard exudates in the macular area. Considering these data, the sensitivity of GPs for detecting DR was 95% and the sensitivity of GPs for detecting treatable lesions was 99%.
When the results of the 3 years were considered globally to evaluate tendencies, a signiÞcant (p < 0.00) decrease in the percentage of unreadable images was detected. We also found a signiÞcant (p = 0.03) decrease over 3 years in the percentage of false negatives. The other data were not signiÞcant.
16.5Discussion
16.5.1Evaluation of the Screening Model
The use of telemedicine based on nonmydriatic retinography is a reliable screening tool for DR. Trained nurses or technicians obtain digital retinal images that ophthalmologists later assessed
164 |
J. Andonegui et al. |
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to determine which patients require a more exhaustive evaluation. In the current study, we described a different approach in which GPs were the initial readers of the retinographies. The results indicated that trained GPs can perform DR screening with high reliability. The British Diabetic Association recommends a sensitivity of at least 80% and a speciÞcity of at least 95% with respect to the gold standard for that screening technique to be considered adequate [19]. The four GPs in the current study obtained a speciÞcity of 83% and a sensitivity of 95%. The sensitivity for detecting treatable lesions was 99%. The GPs detected most treatable lesions but inappropriately referred 14% of patients. A sensitivity of 95% means that 5% of affected patients are diagnosed as normal. It is important to point out that most false negatives were patients with mild NPDR who had only one or a few posterior pole microaneurysms. Even experienced ophthalmologists can have difÞculty differentiating these cases from those with a normal fundus. Moreover, these false negatives are not clinically relevant because this condition does not require treatment and the fundus will be assessed again in 1 year. In any event, a signiÞcant progressive decrease in the percentage of false negatives was detected over the course of the study. This decrease can be due to the fact that the GPs improved their ability to detect DR.
Most inappropriate referrals in the study were for patients with normal fundi, but a high proportion was due to drusen, nevi, or high myopia. In this group of patients, only a low percentage corresponded to retinal alterations that were treatable, such as epiretinal membranes, retinal vein occlusions, or AMD. There was no decrease in the percentage of inappropriate referrals when the data from the 3 years were compared, indicating that, at least during this period, the GPs did not improve their ability to differentiate DR from the related entities. During the training phase, the four GPs were instructed to differentiate DR from drusen, pigmented retinal lesions, or myopia. However, considering the distribution of the inappropriate referrals obtained in the current study, it seems reasonable that further training in these conditions may reduce the percentage of false
positives and improve the sensitivity of the screening technique.
In 2004, Gill et al. [20] evaluated the accuracy of GPs in screening for DR with a PanOptic ophthalmoscope (Welch Allyn, Skaneateles Falls, NY, USA) and obtained a sensitivity of 87% and a speciÞcity of 57%. They concluded that the technique could not replace routine referral to an ophthalmologist because of the low speciÞcity. In more recent reports, Farley et al. [11], Askew et al. [12], and Romero et al. [14] have suggested an approach similar to the one described in this chapter with the introduction of GPs into the screening process for DR using nonmydriatic retinography. Farley et al. [11] reported a sensitivity of 89.8% for the eight primary care physicians who participated in their study. Askew et al. [12] reported a sensitivity of 87% and a speciÞcity of 95% for the two GPs in that study. In both studies, all retinal images were reread by ophthalmologists, and both studies concluded that GPs can screen DR effectively using nonmydriatic retinography. The difference from the current study was that we did not attempt to evaluate the agreement between GPs and ophthalmologists in the interpretation of the retinographies of patients with diabetes. We reported previously that after adequate training, the agreement was high between ophthalmologists and GPs in evaluating retinographies [13]. We are now evaluating the model of DR screening performed by GPs 3 years after implementation. We found only one recent similar study in the literature by Romero et al. [14], who reported a sensitivity of 95% and a speciÞcity of 98% for the GPs.
16.5.2 Prevalence of DR
The prevalence of DR among the patients of the current study was 9%. Considering that the percentage of false negatives in a sample of 360 patients was 5%, an overall prevalence of DR of about 14% could be considered. The prevalence of DR found in the current study was lower than the prevalence expected among the general population of patients with diabetes. Kempen
