Factors influencing hospitalization or emergency department visits and mortality in type 2 diabetes following the onset of new cardiovascular diagnoses in a population-based study.

BACKGROUND: Individuals with type 2 diabetes (T2D) are at increased risk of developing cardiovascular disease (CVD) which necessitates monitoring of risk factors and appropriate pharmacotherapy. This study aimed to identify factors predicting emergency department visits, hospitalizations, and mortality among T2D patients after being newly diagnosed with CVD. METHODS: In a retrospective observational study conducted in Region Halland, individuals aged > 40 years with T2D diagnosed between 2011 and 2019, and a new diagnosis of CVD between 2016 and 2019, were followed for one year from the date of CVD diagnosis. The first encounter for CVD diagnosis was categorized as inpatient-, outpatient-, primary-, or emergency department care. Follow-up included laboratory tests, blood pressure, pharmacotherapies, and healthcare utilization. Hazard ratios (HR) in two Cox regression analyses determined relative risks for emergency visits/hospitalization and mortality, adjusting for age, sex, glucose regulation, lipid levels, kidney function, blood pressure, pharmacotherapy, and healthcare utilization. RESULTS: The study included a total of 1759 T2D individuals who received a new CVD diagnosis, with 67% diagnosed during inpatient care. The average hospitalization stay was 6.5 days, and primary care follow-up averaged 10.1 visits. Patients with CVD diagnosed in primary care had a HR 0.52 (confidence interval [CI] 0.35-0.77) for emergency department visits/hospitalization, but age had a HR 1.02 (CI 1.00-1.03). Pharmacotherapy with insulin, DPP4-inhibitors, aldosterone antagonists, and beta-blockers had a raised HR. Highest mortality risk was observed when CVD was diagnosed inpatient care, systolic blood pressure < 100 mm Hg and elevated HbA1c. Age had a HR 1.05 (CI 1.03-1.08), eGFR < 30 ml/min HR 1.46 (CI 1.01-2.11), and LDL-Cholesterol > 2,5 h 1.46 (CI 1.01-2.11) and associated with increased mortality risk. Pharmacotherapy with metformin had a HR 0.41 (CI 0.28-0.62), statins a HR 0.39 (CI 0.27-0.57), and a primary care follow-up < 30 days a HR 0.53 (CI 0.37-0.77) and associated with lower mortality risk. CONCLUSIONS: T2D individuals who had a new diagnosis of CVD were predominantly diagnosed when hospitalized, while follow-up typically occurred in primary care. Identifying factors that predict risks of mortality and hospitalization should be a focus of follow-up care, underscoring the critical role of primary care in the effective management of T2D and CVD.

Patterns and Predictors Associated With Long-Term Glycemic Control in Pediatric and Young Adult Patients with Type 1 Diabetes.

BACKGROUND: The development of diabetes technology is rapid and requires education and resources to be successfully implemented in diabetes care management. METHOD: In an observational study, we evaluated the use of advanced diabetes technology, resource utilization, and glycemic control. The study population was 725 individuals with type 1 diabetes (T1D) living in Region Halland, Sweden. The study cohort was followed for 7 years between 2013 and 2019. RESULTS: Children aged 0 to 17 years were associated with significantly better glucose control than young adults aged 18 to 25 years. The mean HbA1c in children and young adults was 53 mmol/mol (7.0%) compared to 61 mmol/mol (7.7%) (P < .0001), respectively. Comorbidities such as attention deficit hyperactivity disorder (ADHD), autism, and coelic disease were associated with higher HbA1c. All groups, regardless of age and comorbidity, showed a positive effect on glucose control after visiting a dietitian or psychologist. Differences were found between the age groups in terms of more use of advanced diabetes technology and more frequent visits to a physician in children compared to young adults. CONCLUSIONS: More frequent visits to physicians, and a visit to dietitians, and psychologists were associated with improved glucose control in individuals with T1D 0 to 25 years. Increased resources, including access to more advanced technologies, may be required in young adults with T1D.

Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study.

BACKGROUND: There is a strong need to improve medication adherence (MA) for individuals with hypertension in order to reduce long-term hospitalization costs. We believe this can be achieved through an artificial intelligence agent that helps the patient in understanding key individual adherence risk factors and designing an appropriate intervention plan. The incidence of hypertension in Sweden is estimated at approximately 27%. Although blood pressure control has increased in Sweden, barely half of the treated patients achieved adequate blood pressure levels. It is a major risk factor for coronary heart disease and stroke as well as heart failure. MA is a key factor for good clinical outcomes in persons with hypertension. OBJECTIVE: The overall aim of this study is to design, develop, test, and evaluate an adaptive digital intervention called iMedA, delivered via a mobile app to improve MA, self-care management, and blood pressure control for persons with hypertension. METHODS: The study design is an interrupted time series. We will collect data on a daily basis, 14 days before, during 6 months of delivering digital interventions through the mobile app, and 14 days after. The effect will be analyzed using segmented regression analysis. The participants will be recruited in Region Halland, Sweden. The design of the digital interventions follows the just-in-time adaptive intervention framework. The primary (distal) outcome is MA, and the secondary outcome is blood pressure. The design of the digital intervention is developed based on a needs assessment process including a systematic review, focus group interviews, and a pilot study, before conducting the longitudinal interrupted time series study. RESULTS: The focus groups of persons with hypertension have been conducted to perform the needs assessment in a Swedish context. The design and development of digital interventions are in progress, and the interventions are planned to be ready in November 2020. Then, the 2-week pilot study for usability evaluation will start, and the interrupted time series study, which we plan to start in February 2021, will follow it. CONCLUSIONS: We hypothesize that iMedA will improve medication adherence and self-care management. This study could illustrate how self-care management tools can be an additional (digital) treatment support to a clinical one without increasing burden on health care staff. TRIAL REGISTRATION: ClinicalTrials.gov NCT04413500; https://clinicaltrials.gov/ct2/show/NCT04413500. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24494.

Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation.

BACKGROUND AND PURPOSE: Patients' adherence to medication is a complex, multidimensional phenomenon. Dispensation data and electronic health records are used to approximate medication-taking through refill adherence. In-depth discussions on the adverse effects of data quality and computational differences are rare. The purpose of this article is to evaluate the impact of common pitfalls when computing medication adherence using electronic health records. PROCEDURES: We point out common pitfalls associated with the data and operationalization of adherence measures. We provide operational definitions of refill adherence and conduct experiments to determine the effect of the pitfalls on adherence estimations. We performed statistical significance testing on the impact of common pitfalls using a baseline scenario as reference. FINDINGS: Slight changes in definition can significantly skew refill adherence estimates. Pickup patterns cause significant disagreement between measures and the commonly used proportion of days covered. Common data related issues had a small but statistically significant (p < 0.05) impact on population-level and significant effect on individual cases. CONCLUSION: Data-related issues encountered in real-world administrative databases, which affect various operational definitions of refill adherence differently, can significantly skew refill adherence values, leading to false conclusions about adherence, particularly when estimating adherence for individuals.

Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data.

BACKGROUND AND PURPOSE: Low adherence to medication in chronic disease patients leads to increased morbidity, mortality, and healthcare costs. The widespread adoption of electronic prescription and dispensation records allows a more comprehensive overview of medication utilization. In combination with electronic health records (EHR), such data provides new opportunities for identifying patients at risk of nonadherence and provide more targeted and effective interventions. The purpose of this article is to study the predictability of medication adherence for a cohort of hypertensive patients, focusing on healthcare utilization factors under various predictive scenarios. Furthermore, we discover common proportion of days covered patterns (PDC-patterns) for patients with index prescriptions and simulate medication-taking behaviours that might explain observed patterns. PROCEDURES: We predict refill adherence focusing on factors of healthcare utilization, such as visits, prescription information and demographics of patient and prescriber. We train models with machine learning algorithms, using four different data splits: stratified random, patient, temporal forward prediction with and without index patients. We extract frequent, two-year long PDC-patterns using K-means clustering and investigate five simple models of medication-taking that can generate such PDC-patterns. FINDINGS: Model performance varies between data splits (AUC test set: 0.77-0.89). Including historical information increases the performance slightly in most cases (approx. 1-2% absolute AUC uplift). Models show low predictive performance (AUC test set: 0.56-0.66) on index-prescriptions and patients with sudden drops in PDC (Recall: 0.58-0.63). We find 21 distinct two-year PDC-patterns, ranging from good adherence to intermittent gaps and early discontinuation in the first or second year. Simulations show that observed PDC-patterns can only be explained by specific medication consumption behaviours. CONCLUSIONS: Prediction models developed using EHR exhibit bias towards patients with high healthcare utilization. Even though actual medication-taking is not observable, consumption patterns may not be as arbitrary, provided that medication refilling and consumption is linked.