scholarly journals An online diabetic retinopathy screening tool for patients with type 2 diabetes

2021 ◽  
Vol 14 (11) ◽  
pp. 1748-1755
Author(s):  
Wan-Yue Li ◽  
◽  
Ya-Nan Song ◽  
Ling Luo ◽  
Chuang Nie ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Screening Tool ◽  
External Validation ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Internal Validation ◽  
Diabetic Retinopathy Screening

AIM: To develop a useful diabetic retinopathy (DR) screening tool for patients with type 2 diabetes mellitus (T2DM). METHODS: A DR prediction model based on the Logistic regression algorithm was established on the development dataset containing 778 samples (randomly assigned to the training dataset and the internal validation dataset at a ratio of 7:3). The generalization capability of the model was assessed using an external validation dataset containing 128 samples. The DR risk calculator was developed through WeChat Developer Tools using JavaScript, which was embedded in the WeChat Mini Program. RESULTS: The model revealed risk factors (duration of diabetes, diabetic nephropathy, and creatinine level) and protective factors (annual DR screening and hyperlipidemia) for DR. In the internal and external validation, the recall ratios of the model were 0.92 and 0.89, respectively, and the area under the curve values were 0.82 and 0.70, respectively. CONCLUSION: The DR screening tool integrates education, risk prediction, and medical advice function, which could help clinicians in conducting DR risk assessments and providing recommendations for ophthalmic referral to increase the DR screening rate among patients with T2DM.

scholarly journals Plasma microRNA signature associated with retinopathy in patients with type 2 diabetes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Donato Santovito ◽  
Lisa Toto ◽  
Velia De Nardis ◽  
Pamela Marcantonio ◽  
Rossella D’Aloisio ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
External Validation ◽  
Diabetic Patients ◽  
Disease Biomarkers ◽  
Response To Stress ◽  
Severity Of The Disease ◽  
Plasma Microrna

AbstractDiabetic retinopathy (DR) is a leading cause of vision loss and disability. Effective management of DR depends on prompt treatment and would benefit from biomarkers for screening and pre-symptomatic detection of retinopathy in diabetic patients. MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression which are released in the bloodstream and may serve as biomarkers. Little is known on circulating miRNAs in patients with type 2 diabetes (T2DM) and DR. Here we show that DR is associated with higher circulating miR-25-3p (P = 0.004) and miR-320b (P = 0.011) and lower levels of miR-495-3p (P < 0.001) in a cohort of patients with T2DM with DR (n = 20), compared with diabetic subjects without DR (n = 10) and healthy individuals (n = 10). These associations persisted significant after adjustment for age, gender, and HbA1c. The circulating levels of these miRNAs correlated with severity of the disease and their concomitant evaluation showed high accuracy for identifying DR (AUROC = 0.93; P < 0.001). Gene ontology analysis of validated targets revealed enrichment in pathways such as regulation of metabolic process (P = 1.5 × 10–20), of cell response to stress (P = 1.9 × 10–14), and development of blood vessels (P = 2.7 × 10–14). Pending external validation, we anticipate that these miRNAs may serve as putative disease biomarkers and highlight novel molecular targets for improving care of patients with diabetic retinopathy.

P–673 A visualized clinical model predicting cumulative pregnancy rate after IVF-ET: a real-world study

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
M Zhang
Keyword(s):  
External Validation ◽  
Training Dataset ◽  
Validation Dataset ◽  
Single Center ◽  
Threshold Probability ◽  
Internal Validation ◽  
Female Age ◽  
Clinical Model ◽  
Temporal Validation ◽  
Ivf Et

Abstract Study question How is the cumulative pregnancy probability of individual patients after IVF-ET,could we develop a visualized clinical model to predict it based on patient’s characteristics? Summary answer The visualized clinical mode incorporates five items of female age, number of oocytes, antral follicle count, endometrium thickness and basal FSH level. What is known already Many factors can result in infertility, prognosis prediction is clinically relevant for making the right therapeutic strategy while avoiding overtreatment. It is also helpful in counselling, making the patient aware of possible treatment duration and estimated expense and managing patient’s expectation. Visualized clinical mode and accurate prediction would also be helpful in designing clinical trials to evaluate new treatments. Study design, size, duration We conducted a retrospective analysis of a single-center database using prospectively collected data from women who underwent IVF/ICSI treatment from January 2013 to December 2015, All the participants were followed up for at least 2 years, 3538 IVF-ET cycles were included in the study.A total of 3538 IVF/ICSI cycles were included in the study. Participants/materials, setting, methods Data from a total of 2312 IVF/ICSI cycles from January 2013 to December 2014 were randomly split into training dataset (1550, 67%) and internal validation dataset (762, 33%). A total of 1226 IVF/ICSI cycles in 2015 was applied to external validation dataset (temporal validation) Main results and the role of chance Multivariable logistic regression model combined with restricted cubic splines function was used to test independent prognostic factors and estimate their effects on treatment outcome for patients treated with IVF/ICSI. Female age, number of oocytes retrieved, AFC, endometrium thickness and basal FSH were included the final model. The above model was used to calculate prediction scores for all women in the training and validation datasets. The C-index was 0.693 (95% CI: 0.692∼0.695) in training sets, 0.689 in internal validation sets and 0.710 in external validation sets, which denotes a good performance. Calibration curves suggest excellent model calibration, with an ideal agreement between the prediction and actual observation . The DCA showed that if the threshold probability is between 0 and 0.7, using the nomogram derived in the present study to predict cumulative pregnancy provided a greater benefit than either thetreat-all or the treat-none strategy. Limitations, reasons for caution it was a retrospective, single-center study.In the future, prospective, randomized controlled, multicenter clinical studies will be designed. Wider implications of the findings: The visualized nomogram model provides great predictive value for infertility patients in their first IVF/ICSI cycle, and predicts the pregnancy probability of individuals ,and could help clinicians improving clinical counselling. Trial registration number Not applicable

scholarly journals Do gliosarcomas have distinct imaging features on routine MRI?

2021 ◽  
pp. 197140092110123
Author(s):  
Christoph J Maurer ◽  
Irina Mader ◽  
Felix Joachimski ◽  
Ori Staszewski ◽  
Bruno Märkl ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Multivariate Analysis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Imaging Features ◽  
Age And Sex

Purpose The aim of this study was the development and external validation of a logistic regression model to differentiate gliosarcoma (GSC) and glioblastoma multiforme (GBM) on standard MR imaging. Methods A univariate and multivariate analysis was carried out of a logistic regression model to discriminate patients histologically diagnosed with primary GSC and an age and sex-matched group of patients with primary GBM on presurgical MRI with external validation. Results In total, 56 patients with GSC and 56 patients with GBM were included. Evidence of haemorrhage suggested the diagnosis of GSC, whereas cystic components and pial as well as ependymal invasion were more commonly observed in GBM patients. The logistic regression model yielded a mean area under the curve (AUC) of 0.919 on the training dataset and of 0.746 on the validation dataset. The accuracy in the validation dataset was 0.67 with a sensitivity of 0.85 and a specificity of 0.5. Conclusions Although some imaging criteria suggest the diagnosis of GSC or GBM, differentiation between these two tumour entities on standard MRI alone is not feasible.

scholarly journals Factors associated with non-attendance in the Irish national diabetic retinopathy screening programme (INDEAR study report no. 2)

2021 ◽  
Author(s):  
Stephen R. Kelly ◽  
Allison R. Loiselle ◽  
Rajiv Pandey ◽  
Andrew Combes ◽  
Colette Murphy ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Screening Programme ◽  
Intervention Strategy ◽  
Cost Effective ◽  
Missed Appointments ◽  
Diabetic Retinopathy Screening ◽  
Patients With Diabetes ◽  
Retinopathy Screening

Abstract Aims We aimed to determine the patient and screening-level factors that are associated with non-attendance in the Irish National Diabetic Retinal screening programme (Diabetic RetinaScreen). To accomplish this, we modelled a selection of predictors derived from the historical screening records of patients with diabetes. Methods In this cohort study, appointment data from the national diabetic retinopathy screening programme (RetinaScreen) were extracted and augmented using publicly available meteorological and geospatial data. A total of 653,969 appointments from 158,655 patients were included for analysis. Mixed-effects models (univariable and multivariable) were used to estimate the influence of several variables on non-attendance to screening appointments. Results All variables considered for analysis were statistically significant. Variables of note, with meaningful effect, were age (OR: 1.23 per decade away from 70; 95% CI: [1.22–1.24]), type 2 diabetes (OR: 1.10; 95% CI: [1.06–1.14]) and socio-economic deprivation (OR: 1.12; 95% CI: [1.09–1.16]). A majority (52%) of missed appointments were from patients who had missed three or more appointments. Conclusions This study is the first to outline factors that are associated with non-attendance within the Irish national diabetic retinopathy screening service. In particular, when corrected for age and other factors, patients with type 2 diabetes had higher rates of non-attendance. Additionally, this is the first study of any diabetic screening programme to demonstrate that weather may influence attendance. This research provides unique insight to guide the implementation of an optimal and cost-effective intervention strategy to improve attendance.

scholarly journals Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort

2021 ◽  
pp. bjophthalmol-2020-318570
Author(s):  
John J Smith ◽  
David M Wright ◽  
Irene M Stratton ◽  
Peter Henry Scanlon ◽  
Noemi Lois
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Risk Prediction ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Risk Prediction Models ◽  
Receiver Operating Characteristic Curves ◽  
Risk Of Progression ◽  
Using Data

Background /AimsTo evaluate the performance of existing prediction models to determine risk of progression to referable diabetic retinopathy (RDR) using data from a prospective Irish cohort of people with type 2 diabetes (T2D).MethodsA cohort of 939 people with T2D followed prospectively was used to test the performance of risk prediction models developed in Gloucester, UK, and Iceland. Observed risk of progression to RDR in the Irish cohort was compared with that derived from each of the prediction models evaluated. Receiver operating characteristic curves assessed models’ performance.ResultsThe cohort was followed for a total of 2929 person years during which 2906 screening episodes occurred. Among 939 individuals followed, there were 40 referrals (4%) for diabetic maculopathy, pre-proliferative DR and proliferative DR. The original Gloucester model, which includes results of two consecutive retinal screenings; a model incorporating, in addition, systemic biomarkers (HbA1c and serum cholesterol); and a model including results of one retinopathy screening, HbA1c, total cholesterol and duration of diabetes, had acceptable discriminatory power (area under the curve (AUC) of 0.69, 0.76 and 0.77, respectively). The Icelandic model, which combined retinopathy grading, duration and type of diabetes, HbA1c and systolic blood pressure, performed very similarly (AUC of 0.74).ConclusionIn an Irish cohort of people with T2D, the prediction models tested had an acceptable performance identifying those at risk of progression to RDR. These risk models would be useful in establishing more personalised screening intervals for people with T2D.

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