Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation.

BACKGROUND: Type 2 diabetes mellitus (T2DM) and its related complications represent a growing economic burden for many countries and health systems. Diabetes complications can be prevented through better disease control, but there is a large gap between the recommended treatment and the treatment that patients actually receive. The treatment of T2DM can be challenging because of different comprehensive therapeutic targets and individual variability of the patients, leading to the need for precise, personalized treatment. OBJECTIVE: The aim of this study was to develop treatment recommendation models for T2DM based on deep reinforcement learning. A retrospective analysis was then performed to evaluate the reliability and effectiveness of the models. METHODS: The data used in our study were collected from the Singapore Health Services Diabetes Registry, encompassing 189,520 patients with T2DM, including 6,407,958 outpatient visits from 2013 to 2018. The treatment recommendation model was built based on 80% of the dataset and its effectiveness was evaluated with the remaining 20% of data. Three treatment recommendation models were developed for antiglycemic, antihypertensive, and lipid-lowering treatments by combining a knowledge-driven model and a data-driven model. The knowledge-driven model, based on clinical guidelines and expert experiences, was first applied to select the candidate medications. The data-driven model, based on deep reinforcement learning, was used to rank the candidates according to the expected clinical outcomes. To evaluate the models, short-term outcomes were compared between the model-concordant treatments and the model-nonconcordant treatments with confounder adjustment by stratification, propensity score weighting, and multivariate regression. For long-term outcomes, model-concordant rates were included as independent variables to evaluate if the combined antiglycemic, antihypertensive, and lipid-lowering treatments had a positive impact on reduction of long-term complication occurrence or death at the patient level via multivariate logistic regression. RESULTS: The test data consisted of 36,993 patients for evaluating the effectiveness of the three treatment recommendation models. In 43.3% of patient visits, the antiglycemic medications recommended by the model were concordant with the actual prescriptions of the physicians. The concordant rates for antihypertensive medications and lipid-lowering medications were 51.3% and 58.9%, respectively. The evaluation results also showed that model-concordant treatments were associated with better glycemic control (odds ratio [OR] 1.73, 95% CI 1.69-1.76), blood pressure control (OR 1.26, 95% CI, 1.23-1.29), and blood lipids control (OR 1.28, 95% CI 1.22-1.35). We also found that patients with more model-concordant treatments were associated with a lower risk of diabetes complications (including 3 macrovascular and 2 microvascular complications) and death, suggesting that the models have the potential of achieving better outcomes in the long term. CONCLUSIONS: Comprehensive management by combining knowledge-driven and data-driven models has good potential to help physicians improve the clinical outcomes of patients with T2DM; achieving good control on blood glucose, blood pressure, and blood lipids; and reducing the risk of diabetes complications in the long term.

Renal ischemic-reperfusion injury following hemorrhagic shock in rats: an experimental study.

OBJECTIVE: To understand the features of renal ischemic-reperfusion injury after hemorrhagic shock in rats. METHODS: Models of hemorrhagic shock were established in 36 Sprague-Dawley rats that were divided into 6 groups (with 6 rats in each group), 5 groups of which received subsequent resuscitation measures. Another 6 untreated normal rats served as normal control. Renal pathomorphology and the distribution of dendritic cells (DCs) were observed to determine their correlation in the resuscitation groups (at 3, 6, 12, 24 and 48 h respectively after resuscitation), the control and shock groups. RESULTS: The blood loss of the rats averaged 60.42% of the total blood at the end of hemorrhagic shock. More severe pathological changes were observed in the rats with shock but without receiving resuscitation measures, as compared with the changes in rats with rescuscitation. The rats in shock group had the fewest DC number of all the groups. Among the groups with reperfusion after shock, the most severe renal pathomorphological changes took place 24 h after the resuscitation when the most significant DC activation was noted in positive correlation with renal tissue injury (P<0.01). CONCLUSIONS: Twenty-four hours after reperfusion, the rats with hemorrhagic shock experience the most severe changes in renal pathomorphology with the most extensive distribution of the DCs, indicating that DCs induce renal tissue injury.