Opportunities for improving use of evidence‐based therapy in patients with type 2 diabetes and cardiovascular disease

Gao, Yumin; Peterson, Eric D.; Pagidipati, Neha J.

doi:10.1002/clc.23252

Cited by 8 publications

(9 citation statements)

References 50 publications

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“…Multifaceted strategies, such as CCDS systems in combination with continuing education and feedback mechanisms, are probably more appropriate in Belgian family medicine at the moment [36,37]. Studies have already demonstrated the benefits of adding patient-oriented strategies to decision support [12,[38][39][40].…”

Section: Interpretation Of Main Resultsmentioning

confidence: 99%

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

et al. 2020

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Background: The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care. Methods: A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre-to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate.Results: Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was − 0.09 (95% CI − 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI − 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI − 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI − 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system. Conclusions: The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system.

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Section: Interpretation Of Main Resultsmentioning

confidence: 99%

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

et al. 2020

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“…The practical intent of risk prediction models is to identify high risk patients such that cost‐efficient provision of advanced management strategies (eg, a novel, efficacious, yet expensive pharmaceutical) can be directed toward those patients most likely to experience untoward events, thus minimizing the number needed to treat for benefit . Ultimately, a prediction model's quantitative output implicitly proposes an action (or not) by separating “high (enough) risk” (take action) from “not high (enough) risk” (do not take action) patients.…”

Section: Discussionmentioning

confidence: 99%

“…28,29 The practical intent of risk prediction models is to identify high risk patients such that cost-efficient provision of advanced management strategies (eg, a novel, efficacious, yet expensive pharmaceutical) can be directed toward those patients most likely to experience untoward events, thus minimizing the number needed to treat for benefit. 30 Ultimately, a prediction model's quantitative output implicitly proposes an action (or not) by separating "high (enough) risk" (take action) from "not high (enough) risk" (do not take action) patients. The current study is the first to our knowledge to propose such an objective "high risk" threshold for HHF among type 2 diabetics: a >5% probability of HHF within the next five years-reflecting the 10% highest risk patients according to model predictors.…”

Section: Discussionmentioning

confidence: 99%

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

Williams

Geba

Cordova

et al. 2019

Clinical Cardiology

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Background Antidiabetic therapies have shown disparate effects on hospitalization for heart failure (HHF) in clinical trials. This study developed a prediction model for HHF in type 2 diabetes mellitus (T2DM) using real world data to identify patients at high risk for HHF. Hypothesis Type 2 diabetics at high risk for HHF can be identified using information generated during usual clinical care. Methods This electronic medical record‐ (EMR‐) based retrospective cohort study included patients with T2DM free of HF receiving healthcare through a single, large integrated healthcare system. The primary endpoint was HHF, defined as a hospital admission with HF as the primary diagnosis. Cox regression identified the strongest predictors of HHF from 80 candidate predictors derived from EMRs. High risk patients were defined according to the 90th percentile of estimated risk. Results Among 54,452 T2DM patients followed on average 6.6 years, estimated HHF rates at 1, 3, and 5 years were 0.3%, 1.1%, and 2.0%. The final 9‐variable model included: age, coronary artery disease, blood urea nitrogen, atrial fibrillation, hemoglobin A1c, blood albumin, systolic blood pressure, chronic kidney disease, and smoking history (c = 0.782). High risk patients identified by the model had a >5% probability of HHF within 5 years. Conclusions The proposed model for HHF among T2DM demonstrated strong predictive capacity and may help guide therapeutic decisions.

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“…8 Nonadherence can be unintentional; patients may forget to take medications, forget to refill a prescription, or may not take a medication correctly. 9 However, some patients may intentionally choose to not adhere to treatment due to lack of motivation or autonomy, adverse side effects, desire for control, or perceived stigmas associated with taking medication. 10 These issues may be exacerbated for patients requiring polypharmacy (40-50% of elderly patients in high-income countries) where >25% of elderly patients have a chronic intake of 10+ medicines.…”

Section: Introductionmentioning

confidence: 99%

<p>Moving the Dial on Heart Failure Patient Adherence Rates</p>

Makris¹,

Hu²,

Jones³

et al. 2020

PPA

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Introduction: Heart failure remains a substantive contributor to patient morbidity and mortality rates worldwide and represents a significant burden on the healthcare ecosystem. Faced with persistent physical symptoms and debilitating social consequences, patients follow complex treatment regimens and often have difficulty adhering to them. Purpose: In this manuscript, we review factors which contribute to low adherence rates and advance potential single-and multi-factor-based interventions. It is hoped that these observations can lead to improvements in managed care of this vulnerable population of patients. Methods: A narrative review of the primary literature was performed on contributing factors with primary focus on the period 2015-2020 using available databases and search engines. Adherence pain points identified were mapped against a series of potential solutions which are presented. Results: Enhancement of treatment adherence relies on two approaches viz. single-factor and multi-factor solutions. Single factors identified include electronic reminders, enhanced health education, financial incentives, gamification strategies, community drivers, personabased modeling, and burden relief of poly pharmacy. Multi-factor solutions combine two or more of the seven approaches offering the potential for flexible interventions tailored to the individual. Discussion and Conclusion: Heart failure patients with poor adherence have increased mortality, hospitalization needs, and healthcare costs. This review highlights current singlefactor and multi-factor adherence methods. Against a backdrop of diversity of approaches, multi-factor solutions cast the widest net for positively influencing adherent behaviors. A key enabler lies in the development and leveraging of patient personas in the synthesis of successful intervention methods. Deployable solutions can also be envisioned in clinical trials where adherence tracking represents an essential component.

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Opportunities for improving use of evidence‐based therapy in patients with type 2 diabetes and cardiovascular disease

Cited by 8 publications

References 50 publications

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

<p>Moving the Dial on Heart Failure Patient Adherence Rates</p>

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