Outcomes Associated with Nonconcordance to National Kidney Foundation Guideline Recommendations for Oral Antidiabetic Drug Treatments in Patients with Concomitant Type 2 Diabetes and Chronic Kidney Disease

Chen, Shih Yin; Lee, Yuan Chi; Alas, Veronica; Greene, Mallik; Brixner, Diana I.

doi:10.4158/ep13269.or

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…Some previous study found guideline-concordant treatment was associated with better clinical outcomes. 15 24 Similarly, we found that adherence to Chinese guideline was associated with a decrease in ESKD risk for antihyperglycemic therapy, hypertension control and lipid management among Chinese patients with DN. However, it seems that no significant association existed between adherence to ADA guideline and ESKD risk for hypertension control and lipid management in our study.…”

Section: Discussionmentioning

confidence: 66%

“…Some published studies assessed the adherence to guidelines for patients with type 2 diabetes with renal disease and the guideline was interpreted relatively simply. [12][13][14][15] For example, Eder et al 12 assessed the guideline adherence with respect to metabolic and blood pressure control, use of renin-angiotensin system-blocking agents, statins and acetylsalicylic acid for patients with type 2 diabetes with and without renal disease. Chen et al 13 only assessed whether patients adjusted the dose of oral antidiabetic agents or avoided using them in patients with type 2 diabetes with moderate to severe CKD.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, neither of the studies elaborated on the guideline-recommended medication regimen, namely which drug therapy should be chosen and how to combine drugs for precise patients grouped by their clinical conditions and medication history. Chen et al 15 assessed the association between oral antidiabetic drug treatment concordance with guidelines and related economic and clinical outcomes in patients with type 2 diabetes and stage 3 to 5 CKD. However, there was no evidence for patients with DN diagnosed by renal biopsy.…”

Section: Introductionmentioning

confidence: 99%

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Influence of guideline adherence and parameter control on the clinical outcomes in patients with diabetic nephropathy

Zhao²,

Chen

et al. 2020

BMJ Open Diab Res Care

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IntroductionWe assessed the association between guideline adherence and outcomes of clinical parameter control and end-stage kidney disease (ESKD), and further studied the effect of parameter control on ESKD for Chinese patients with diabetic nephropathy (DN).Research design and methodsIn this retrospective study, 1128 patients with DN (15,374 patient-visit samples) diagnosed by renal biopsy were enrolled. Samples were classified as adherence and nonadherence based on whether prescribed drugs conformed to medication regimen and drug contraindication recommended by guidelines, including American Diabetes Association (ADA) and Chinese guidelines. Guideline adherence rate was calculated on all samples for antihyperglycemic, antihypertensive and lipid-lowering treatments. Clinical parameter control was compared after 3–6 months’ therapy between two groups by generalized estimating equation models. Time-dependent Cox models were applied to evaluate the influence of guideline adherence on ESKD. Latent class mixed model was used to identify distinct trajectories for parameters and their ESKD risks were compared using Cox proportional-hazards models.ResultsGuideline adherence rate of antihyperglycemic therapy was the highest, with 72.87% and 68.15% of samples meeting ADA and Chinese guidelines, respectively. Adherence was more likely to have good glycated hemoglobin A1c (HbA1c) control (ADA: OR 1.46, 95% CI 1.12 to 1.88; Chinese guideline: OR 1.42, 95% CI 1.09 to 1.85) and good blood pressure control (ADA: OR 1.35, 95% CI 1.03 to 1.78; Chinese guideline: OR 1.39, 95% CI 1.08 to 1.79) compared with nonadherence. The improvement of patient’s adherence showed the potential to reduce ESKD risk. For proteinuria, low-density lipoprotein cholesterol (LDL-C), systolic blood pressure and uric acid, patients in higher-value trajectory group had higher ESKD risk. Proteinuria and LDL-C trajectories were most closely related to ESKD risk, while the risk was not significantly different in HbA1c trajectories.ConclusionsGuideline adherence and good control of proteinuria and LDL-C in clinical practice are important and in need for improving clinical outcomes in patients with DN.

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Section: Discussionmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of guideline adherence and parameter control on the clinical outcomes in patients with diabetic nephropathy

Zhao²,

Chen

et al. 2020

BMJ Open Diab Res Care

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“…If the covariates change over time and are affected by the previous treatment, the use of marginal structural models [24][25][26] was proposed to control the confounders. In relation to diabetes treatment, Chen et al [27] assessed the association between treatment concordance with clinical guidelines and related clinical outcomes in patients with T2DM by comparing guideline-concordant and guideline-nonconcordant cohorts. This work is closely related to treatment model evaluation as the treatment groups to be compared are defined based on a given guideline, which can be regarded as a special treatment model that has been verified and commonly accepted.…”

Section: Introductionmentioning

confidence: 99%

“…This work is closely related to treatment model evaluation as the treatment groups to be compared are defined based on a given guideline, which can be regarded as a special treatment model that has been verified and commonly accepted. Chen et al [ 27 ] considered hospital admission and severe hypoglycemic events as the clinical outcomes of interest. Logistic regressions were used to examine factors associated with the likelihood of having at least one hospital admission and Cox proportional hazard regressions were used to model time to hypoglycemic events.…”

Section: Introductionmentioning

confidence: 99%

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

Sun¹,

Bee²,

Lam³

et al. 2021

J Med Internet Res

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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.

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Should we care to adhere? Guideline adherence rates, glycemic control and physician perspective on adherence for type-2 diabetes

Brenner

Oberaigner

Stummer

2020

J Public Health (Berl.)

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Outcomes Associated with Nonconcordance to National Kidney Foundation Guideline Recommendations for Oral Antidiabetic Drug Treatments in Patients with Concomitant Type 2 Diabetes and Chronic Kidney Disease

Cited by 12 publications

References 14 publications

Influence of guideline adherence and parameter control on the clinical outcomes in patients with diabetic nephropathy

Influence of guideline adherence and parameter control on the clinical outcomes in patients with diabetic nephropathy

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

Should we care to adhere? Guideline adherence rates, glycemic control and physician perspective on adherence for type-2 diabetes

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