Analysis via Markov decision process to evaluate glycemic control strategies of a large retrospective cohort with type 2 diabetes: the ameliorate study.
通过 Markov 决策过程进行分析，以评估大型 2 型糖尿病回顾性队列的血糖控制策略: 改善研究。
- 作者列表："Meng F","Sun Y","Heng BH","Leow MKS
AIMS:Our aim was to explore optimal treatment decisions for HbA1c control for type 2 diabetes mellitus patients and assess the impact on potential improvements in quality of life compared with current guidelines. METHODS:We analyzed a large dataset of HbA1c levels, diabetes-related key risk factors and medication dispensed to 70,069 patients with type 2 diabetes from polyclinics and a large public hospital in Singapore during January 1, 2008, to December 31, 2015. A Markov decision process (MDP) model was developed to determine the optimal treatment policy concerning medication management for glycemic control over a long-term treatment period. We assessed the model performance by comparing quality-adjusted life years (QALYs) gained by the model with those derived by a conventional Markov model informed by current clinical guidelines. RESULTS:Numerical results showed that optimal treatment strategies derived by the MDP model could increase the total expected QALYs by as much as 0.27 years for patients at higher risk such as old age, high HbA1c levels and smokers. In particular, the improvements in QALYs gained for patients with HbA1c levels of 9% (75 mmol/mol) and above were higher than those with lower HbA1c levels. However, the potential improvements appeared to be marginal for patients at lower risk compared with current guidelines. CONCLUSIONS:Use of data-driven prescriptive analytics would help clinicians make evidence-based treatment decisions for HbA1c control for patients with type 2 diabetes, in particular for those at high risk.
目的: 我们的目的是探索 2 型糖尿病患者 HbA1c 控制的最佳治疗决策，并评估与当前指南相比对生活质量潜在改善的影响。 方法: 我们分析了 2008年1月1日期间来自新加坡综合诊所和一家大型公立医院的 70,069 例 2 型糖尿病患者的 HbA1c 水平、糖尿病相关关键风险因素和药物分配的大型数据集。至 2015年12月31日。开发了马尔可夫决策过程 (MDP) 模型，以确定长期治疗期间血糖控制药物管理的最佳治疗策略。我们通过比较模型获得的质量调整生命年 (QALYs) 与当前临床指南告知的常规 Markov 模型得出的 QALYs 来评估模型性能。 结果: 数值结果表明，MDP 模型得出的最佳治疗策略可以使高危患者如老年患者的总预期 QALYs 增加 0.27 年。高 HbA1c 水平与吸烟者。特别是，HbA1c 水平为 9% (75 mmol/mol) 及以上的患者获得的 QALYs 改善高于 HbA1c 水平较低的患者。然而，与目前的指南相比，对于风险较低的患者，潜在的改善似乎是边际的。 结论: 使用数据驱动的规范性分析将有助于临床医生为 2 型糖尿病患者，特别是高风险患者的 HbA1c 控制做出循证治疗决策。
METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.
METHODS::Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3-/- cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.