Effectiveness of quality of care for patients with type 2 diabetes in China: findings from the Shanghai Integration Model (SIM)

Integrated image-based deep learning and language models for primary diabetes care

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

Benefits of 12-month peer support for diabetes self management sustained at 18 months

AIMS

To evaluate sustainability of peer support (PS) benefits in diabetes management.

METHODS

Supporting a Peer Leader program through Community Health Centers (CHCs) included trainings and consultations from baseline to 12 months. Evaluation at baseline, 12-month, and 18-month follow-up included primary outcome, HbA1c, and other outcomes of SBP, DBP, LDLc, PHQ-8, diabetes distress, and EQ-5D.

RESULTS

1284 participants with type 2 diabetes mellitus were recruited from 9 CHCs. Mean (SD) for age = 68.00 (7.55) years, 43.07 % male, mean (SD) for diabetes duration = 11.79 (7.34) years. Across 18-months, linear mixed model analyses controlling for confounders found the least square mean (SE) of HbA1c improved significantly from 7.62 % (0.06 %) to 7.53 % (0.06 %) for all, and from 9.25 % (0.09 %) to 8.52 % (0.11 %) among those ≥8 % at baseline. Parallel improvements were found among all for SBP, DBP, PHQ-8, diabetes distress, and, among those elevated at baseline for all outcomes. EQ-5D showed significant but modest increase from baseline to 18 months. No significant reversals between 12 and 18 months were found except for LDLc. Supporting robustness of findings, patterns were similar across age, diabetes duration, and gender.

CONCLUSIONS

Relative to the fundamentally progressive nature of diabetes, it is striking that improvements associated with PS were generally sustained after program support ended.

A deep learning system for predicting time to progression of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Research progress on ocular complications caused by type 2 diabetes mellitus and the function of tears and blepharons

The purpose of this study was to explore the related research progress of ocular complications (OCs) caused by type 2 diabetes mellitus (T2DM), tear and tarsal function, and the application of deep learning (DL) in the diagnosis of diabetes and OCs caused by it, to provide reference for the prevention and control of OCs in T2DM patients. This study reviewed the pathogenesis and treatment of diabetes retinopathy, keratopathy, dry eye disease, glaucoma, and cataract, analyzed the relationship between OCs and tear function and tarsal function, and discussed the application value of DL in the diagnosis of diabetes and OCs. Diabetes retinopathy is related to hyperglycemia, angiogenic factors, oxidative stress, hypertension, hyperlipidemia, and other factors. The increase in water content in the corneal stroma leads to corneal relaxation, loss of transparency, and elasticity, and can lead to the occurrence of corneal lesions. Dry eye syndrome is related to abnormal stability of the tear film and imbalance in neural and immune regulation. Elevated intraocular pressure, inflammatory reactions, atrophy of the optic nerve head, and damage to optic nerve fibers are the causes of glaucoma. Cataract is a common eye disease in the elderly, which is a visual disorder caused by lens opacity. Oxidative stress is an important factor in the occurrence of cataracts. In clinical practice, blood sugar control, laser therapy, and drug therapy are used to control the above eye complications. The function of tear and tarsal plate will be affected by eye diseases. Retinopathy and dry eye disease caused by diabetes will cause dysfunction of tear and tarsal plate, which will affect the eye function of patients. Furthermore, DL can automatically diagnose and classify eye diseases, automatically analyze fundus images, and accurately diagnose diabetes retinopathy, macular degeneration, and other diseases by analyzing and processing eye images and data. The treatment of T2DM is difficult and prone to OCs, which seriously threatens the normal life of patients. The occurrence of OCs is closely related to abnormal tear and tarsal function. Based on DL, clinical diagnosis and treatment of diabetes and its OCs can be carried out, which has positive application value.

Effects of pharmacist-led interventions on glycaemic control, adherence, disease management and health-related quality of life in patients with type 2 diabetes: a protocol for a network meta-analysis

INTRODUCTION

The increase in the number of patients with uncontrolled type 2 diabetes mellitus (T2DM) is in need of effective management interventions. However, research to date has been limited to the evaluation of the outcomes of community pharmacists alone. Therefore, the aim of the study protocol is to compare the effects of clinical pharmacist-led intervention strategies for the management of T2DM in the outpatient settings.

METHOD AND ANALYSIS

The study will collect and analyse data applying standard Cochrane methodological procedures. A search for eligible studies and ongoing trials will be conducted using PubMed, Embase, Medline (via Ovid), EBSCO (via Ovid), Lippincott Williams & Wilkins (LWW) Journals (via Ovid), ProQuest Health and Medical Complete, and ClinicalTrials.gov (clinicaltrials.gov) from database inception to December 2023. Clinical and health outcomes will be measured using both glycaemic control related indicators (eg, glycated haemoglobin, fasting blood glucose, postprandial glucose) and general indicators (eg, adherence, disease management and health-related quality of life). The meta-analysis will conduct pairwise meta-analysis using random effects models and network meta-analysis (NMA) employing the Bayesian hierarchical model. The visualisation and statistical analysis will be carried out using RevMan, R Studio and ADDIS. Additionally, we will evaluate the certainty of the evidence by using Grading of Recommendations Assessment, Development and Evaluation system.

ETHICS AND DISSEMINATION

There will be no primary data collection from NMA participants, and there is no requirement for formal ethical review. Our aim is to present the results of this NMA in a peer-reviewed scientific journal, at conferences, and in the mainstream media.

PROSPERO REGISTRATION NUMBER

CRD42022355368.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Application and prospect of artificial intellingence in diabetes care

Diabetes is one of the fastest-growing non-communicable diseases, becoming an important public health concern worldwide as well as in China. Currently, China has the largest population living with diabetes. Artificial intelligence (AI) is a fast-growing field and its applications to diabetes could enable the delivery of better management services for people with diabetes. This perspective summarized the latest findings of digital technologies and AI use in the following areas of diabetes care, mainly including screening and risk predictions of diabetes and diabetic complications, precise monitoring and intervention combined with new technologies, and mobile health application in self-management support for people with diabetes. Challenges to promote further use of AI in diabetes care included data standardization and integration, performance of AI-based medical devices, motivation of patients, and sensitivity to privacy. In summary, although the AI applications in clinical practice is still at an early stage, we are moving toward a new paradigm for diabetes care with the rapid development and emerging application of AI.