(1) Background: It has rarely been studied whether the severity of diabetic retinopathy (DR) could influence renal disease progression in end-stage renal disease (ESRD) and chronic kidney disease (CKD) in patients with type 2 diabetes. The aim of this study was to evaluate renal disease progression in ESRD and CKD according to DR severity in patients with type 2 diabetes. (2) Methods: We included 1329 patients and divided the cohort into two end-points. The first was to trace the incidence of ESRD in all enrolled participants and the other was to follow their progression to CKD. (3) Results: Significantly higher crude hazard ratios (HRs) of ESRD incidence in all enrolled participants were noted, and this ratio increased in a stepwise fashion. However, after adjustment, DR severity was not associated with ESRD events. Therefore, a subgroup of 841 patients without CKD was enrolled to track their progression to CKD. Compared with no diabetic retinopathy, the progression of CKD increased in a stepwise fashion, from mild nonproliferative diabetic retinopathy (NPDR) to moderate NPDR, to severe NPDR and to proliferative diabetic retinopathy (PDR), both in the crude and adjusted models. (4) Conclusions: The severity of retinopathy appeared to be associated with renal lesions and the development of CKD. Our findings suggest that the severity of DR is a risk factor for progression to CKD. Therefore, diabetic retinopathy is useful for prognosticating the clinical course of diabetic kidney disease.

The machine learning-assisted electrocardiogram (ECG) is increasingly recognized for its unprecedented capabilities in diagnosing and predicting cardiovascular diseases. Identifying the need for ECG examination early in emergency department (ED) triage is key to timely artificial intelligence-assisted analysis. We used machine learning to develop and validate a clinical decision support tool to predict ED triage patients’ need for ECG. Data from 301,658 ED visits from August 2017 to November 2020 in a tertiary hospital were divided into a development cohort, validation cohort, and two test cohorts that included admissions before and during the COVID-19 pandemic. Models were developed using logistic regression, decision tree, random forest, and XGBoost methods. Their areas under the receiver operating characteristic curves (AUCs), positive predictive values (PPVs), and negative predictive values (NPVs) were compared and validated. In the validation cohort, the AUCs were 0.887 for the XGBoost model, 0.885 for the logistic regression model, 0.878 for the random forest model, and 0.845 for the decision tree model. The XGBoost model was selected for subsequent application. In test cohort 1, the AUC was 0.891, with sensitivity of 0.812, specificity of 0.814, PPV of 0.708 and NPV of 0.886. In test cohort 2, the AUC was 0.885, with sensitivity of 0.816, specificity of 0.812, PPV of 0.659, and NPV of 0.908. In the cumulative incidence analysis, patients not receiving an ECG yet positively predicted by the model had significantly higher probability of receiving the examination within 48 h compared with those negatively predicted by the model. A machine learning model based on triage datasets was developed to predict ECG acquisition with high accuracy. The ECG recommendation can effectively predict whether patients presenting at ED triage will require an ECG, prompting subsequent analysis and decision-making in the ED.

(1) Background: Diabetic retinopathy (DR) can cause blindness. Current guidelines on diabetic eye care recommend more frequent eye examinations for more severe DR to prevent deterioration. However, close follow-up and early intervention at earlier stages are important for the prevention of disease progression of other diabetes mellitus (DM) complications. The study was designed to investigate the association between different stages of DR in type 2 DM patients and the progression of DR; (2) Methods: A total of 2623 type 2 DM patients were included in this study. In these patients, a total of 14,409 fundus color photographs was obtained. The primary outcome was the progression of DR; (3) Results: The progression of DR was highly associated with the initial grade of DR (p < 0.001). Severe nonproliferative diabetic retinopathy (NPDR) was the most likely to progress to proliferative diabetic retinopathy (PDR), followed by moderate NPDR, mild NPDR, and no retinopathy. However, progression to the next stage of DR showed a different trend. We used no retinopathy as a reference. Mild NPDR showed the highest risk for progression to the next stage [hazard ratio (HR): 2.00 (95% conference interval (CI): 1.72–2.32)] relative to higher initial grades [HR (moderate NPDR): 1.82 (95% CI: 1.58–2.09) and HR (severe NPDR): 0.87 (95% CI: 0.69–1.09)]. The same trend was observed in the multivariate analysis, in which mild NPDR presented the highest risk for progression to the next stage (adjusted HR (mild NPDR): 1.95 (95% CI: 1.68–2.27), adjusted HR (moderate NPDR): 1.73 (95% CI: 1.50–1.99), and adjusted HR (severe NPDR): 0.82 (95% CI: 0.65–1.03)); (4) Conclusions: Type 2 diabetic patients with earlier-grade DR appeared to exhibit more rapid development to the next grade in our study. As these findings show, more frequent fundus color photography follow-up in earlier-grade DR patients is important to slow DR progression and awaken self-perception.