RESUMEN
PURPOSE: To examine the real-world performance of a support vector machine learning software (RetinaLyze) in order to identify the possible presence of diabetic retinopathy (DR) in patients with diabetes via software implementation in clinical practice. METHODS: 1001 eyes from 1001 patients-one eye per patient-participating in the Danish National Screening Programme were included. Three independent ophthalmologists graded all eyes according to the International Clinical Diabetic Retinopathy Disease Severity Scale with the exact level of disease being determined by majority decision. The software detected DR and no DR and was compared to the ophthalmologists' gradings. RESULTS: At a clinical chosen threshold, the software showed a sensitivity, specificity, positive predictive value and negative predictive value of 84.9% (95% CI: 81.8-87.9), 89.9% (95% CI: 86.8-92.7), 92.1% (95% CI: 89.7-94.4), and 81.0% (95% CI: 77.2-84.7), respectively, when compared to human grading. The results from the routine screening were 87.0% (95% CI: 84.2-89.7), 85.3% (95% CI: 81.8-88.6), 89.2% (95% CI: 86.3-91.7), and 82.5% (95% CI: 78.5-86.0), respectively. AUC was 93.4%. The reference graders Conger's Exact Kappa was 0.827. CONCLUSION: The software performed similarly to routine grading with overlapping confidence intervals, indicating comparable performance between the two groups. The intergrader agreement was satisfactory. However, evaluating the updated software alongside updated clinical procedures is crucial. It is therefore recommended that further clinical testing before implementation of the software as a decision support tool is conducted.
RESUMEN
BACKGROUND: Lowering glucose levels is a complex task for patients with type 1 diabetes, and they often lack contact with health care professionals. Intermittently scanned continuous glucose monitoring (isCGM) has the potential to aid them with blood glucose management at home. The aim of this study was to investigate the long-term effect of isCGM on HbA1c in type 1 diabetes patients with poor glycaemic control in a region-wide real-world setting. METHODS: All patients with type 1 diabetes receiving an isCGM due to poor glycaemic control (≥70 mmol/mol [≥8.6%]) in the period of 2020-21 in Region North Denmark ("T1D-CGM") were compared with all type 1 diabetes patients without isCGM ("T1D-NOCGM") in the same period. A multiple linear regression model adjusted for age, sex, diabetes duration and use of continuous subcutaneous insulin infusion was constructed to estimate the difference in change from baseline HbA1c between the two groups and within subgroups of T1D-CGM. RESULTS: A total of 2,527 patients (T1D-CGM: 897; T1D-NOCGM: 1,630) were included in the study. The estimated adjusted difference in change from baseline HbA1c between T1D-CGM vs T1D-NOCGM was -5.68 mmol/mol (95% CI: (-6.69 to -4.67 mmol/mol; p<0.0001)). Older patients using isCGM dropped less in HbA1c. CONCLUSIONS: Our results indicate that patients with type 1 diabetes in poor glycaemic control from Region North Denmark in general benefit from using isCGM with a sustained 24-month improvement in HbA1c, but the effect on HbA1c may be less pronounced for older patients.
