Evaluation of glucose-lowering medications in older people: a comprehensive systematic review and network meta-analysis of randomized controlled trials.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is increasingly being diagnosed in older adults. Our objective is to assess the advantages and potential drawbacks of different glucose-lowering medications in this specific population. METHODS: A network meta-analysis was conducted to identify randomized controlled trials that examined patient-centered outcomes in adults aged ≥65 years with T2DM. We searched PubMed, Cochrane CENTRAL, and Embase up to September 23, 2023. Quality of eligible studies were assessed using the Cochrane RoB 2.0 tool. RESULTS: A total of 22 trials that involved 41 654 participants were included, incorporating sodium-glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RAs), dipeptidyl peptidase-4 (DPP-4) inhibitors, metformin, sulfonylureas (SU) and acarbose. Our findings reveal that GLP-1RAs reduce the risk of major adverse cardiovascular events (risk ratio [RR], 0.83; 95% confidence interval [CI], 0.71 to 0.97) and body weight (mean difference [MD], -3.87 kg; 95% CI, -5.54 to -2.21). SGLT2 inhibitors prevent hospitalization for heart failure (RR, 0.66; 95% CI, 0.57 to 0.77), renal composite outcome (RR, 0.69; 95% CI, 0.53 to 0.89), and reduce body weights (MD, -1.85 kg; 95% CI, -2.42 to -1.27). SU treatment increases the risk of any hypoglycaemia (RR, 4.19; 95% CI, 3.52 to 4.99) and severe hypoglycaemia (RR, 7.06; 95% CI, 3.03 to 16.43). GLP-1RAs, SGLT2 inhibitors, metformin, SU and DPP-4 inhibitors are effective in reducing glycaemic parameters. Notably, the number of treatments needed decreases in most cases as age increases. CONCLUSIONS: Novel glucose-lowering medications with benefits that outweigh risks should be prioritized for older patients with diabetes.

A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery.

Esophageal cancer is a deadly disease, and neoadjuvant chemoradiotherapy can improve patient survival, particularly for patients achieving a pathological complete response (ypCR). However, existing imaging methods struggle to accurately predict ypCR. This study explores computer-aided detection methods, considering both imaging data and radiotherapy dose variations to enhance prediction accuracy. It involved patients with node-positive esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy and surgery, with data collected from 2014 to 2017, randomly split into five subsets for 5-fold cross-validation. The algorithm DCRNet, an advanced version of OCRNet, integrates RT dose distribution into dose contextual representations (DCR), combining dose and pixel representation with ten soft regions. Among the 80 enrolled patients (mean age 55.68 years, primarily male, with stage III disease and middle-part lesions), the ypCR rate was 28.75%, showing no significant demographic or disease differences between the ypCR and non-ypCR groups. Among the three summarization methods, the maximum value across the CTV method produced the best results with an AUC of 0.928. The HRNetV2p model with DCR performed the best among the four backbone models tested, with an AUC of 0.928 (95% CI, 0.884-0.972) based on 5-fold cross-validation, showing significant improvement compared to other models. This underscores DCR-equipped models' superior AUC outcomes. The study highlights the potential of dose-guided deep learning in ypCR prediction, necessitating larger, multicenter studies to validate the results.