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Abstract Background Deprescribing of medication for cardiovascular risk factors and diabetes has been incorporated in clinical guidelines but proves to be difficult to implement in primary care. Training of healthcare providers is needed to enhance deprescribing in eligible patients. This study will examine the effects of a blended training program aimed at initiating and conducting constructive deprescribing consultations with patients. Methods A cluster-randomized trial will be conducted in which local pharmacy-general practice teams in the Netherlands will be randomized to conducting clinical medication reviews with patients as usual (control) or after receiving the CO-DEPRESCRIBE training program (intervention). People of 75 years and older using specific cardiometabolic medication (diabetes drugs, antihypertensives, statins) and eligible for a medication review will be included. The CO-DEPRESCRIBE intervention is based on previous work and applies models for patient-centered communication and shared decision making. It consists of 5 training modules with supportive tools. The primary outcome is the percentage of patients with at least 1 cardiometabolic medication deintensified. Secondary outcomes include patient involvement in decision making, healthcare provider communication skills, health/medication-related outcomes, attitudes towards deprescribing, medication regimen complexity and health-related quality of life. Additional safety and cost parameters will be collected. It is estimated that 167 patients per study arm are needed in the final intention-to-treat analysis using a mixed effects model. Taking loss to follow-up into account, 40 teams are asked to recruit 10 patients each. A baseline and 6-months follow-up assessment, a process evaluation, and a cost-effectiveness analysis will be conducted. Discussion The hypothesis is that the training program will lead to more proactive and patient-centered deprescribing of cardiometabolic medication. By a comprehensive evaluation, an increase in knowledge needed for sustainable implementation of deprescribing in primary care is expected. Trial registration The study is registered at ClinicalTrials.gov (identifier: NCT05507177).

BackgroundDeprescribing has been recommended for managing polypharmacy but deprescribing preventive medication in older patients is still uncommon. We aimed to investigate older patients’ barriers to and enablers of deprescribing cardiometabolic medication.MethodsTwo focus groups were conducted among patients ≥70 years with polypharmacy, including cardiometabolic medication. Purposive sampling through four community pharmacies was used in two regions in the Netherlands. A topic list was developed using literature and the theoretical domains framework (TDF). The meetings were audio recorded, transcribed verbatim and coded using thematic coding, attribute coding and the TDF. In addition, patients were categorized on attitudes towards medication and willingness to stop.ResultsThe meetings were attended by 17 patients and 1 caregiver (71 to 84 years). In total 15 barriers and 13 enablers were identified within four themes, partly related to beliefs, fears and experiences regarding using or stopping medication, and partly related to the relationship with the health care professional and the conditions to stop. Some patients attributed their wellbeing to their medication and were therefore unwilling to stop. Reducing cardiometabolic medication because of less strict treatment targets confused some patients and was a barrier to deprescribing. Having options to monitor clinical measurements and restart medication were enablers. Patients were only willing to stop cardiometabolic medication when this was proposed by a HCP they trusted. Patients with a positive attitude towards medication varied in their willingness to stop cardiometabolic medication. Patients with a negative attitude towards medication were generally willing to stop medication but still perceived several barriers and may consider some medication as being essential.ConclusionFears, beliefs, and experiences regarding using and stopping medication as well as trust in the HCP influence willingness to have medication deprescribed. Attitudes towards medication in general do not necessarily translate into willingness or unwillingness to stop specific medication. For deprescribing cardiometabolic medication, patient involvement when setting new treatment targets and monitoring the effects on short-term outcomes are important.

Aim: To develop and pilot an algorithm to select older people for different types of medication review based on their case complexity.Methods: Experts rated complexity of patient cases through a Delphi-consensus method. The case characteristics were included in a regression model predicting complexity to develop a criteria-based algorithm. The algorithm was piloted in four community pharmacies with 38 patients of high and low complexity. Pharmacists conducted medication reviews according to their personal judgment and rated the patients' complexity. Time needed for reviewing and number of interventions (proposed and implemented) were assessed. Feasibility was evaluated with in-depth interviews.Results: We developed the algorithm with 75 cases proceeding from patients in average 79 years old and using 10 prescribed medications. The regression model (adjusted R2 = 0.726, P < 0.0001) resulted in the following criteria for the algorithm: “number of medications” × 1 + “number of prescribers” × 3 + “recent fall incident” × 7 + “does not collect own medication” × 4. The pharmacists performed advanced medication reviews with all patients. The time needed to perform the medication review did not differ significantly according to case complexity (76.9 min for high complexity; 66.1 min for low complexity). Agreement between the algorithm scores and the pharmacists' ratings on complexity degree was slight to moderate (Kappa 0.16–0.42). The pharmacists had mixed opinions about the feasibility of applying the algorithm, particularly regarding the criterion “fall incidents.”Conclusion: We developed an algorithm with four criteria that distinguished between high and low complexity patients as rated by experts. Additional validation steps are needed before implementation.

Introduction: In primary care, identifying patients with type 2 diabetes (T2D) who are at increased risk of hypoglycaemia is important for the prevention of hypoglycaemic events. We aimed to develop a screening tool based on machine learning to identify such patients using routinely available demographic and medication data. Methods: We used a cohort study design and the Groningen Initiative to ANalyse Type 2 diabetes Treatment (GIANTT) medical record database to develop models for hypoglycaemia risk. The first hypoglycaemic event in the observation period (2007–2013) was the outcome. Demographic and medication data were used as predictor variables to train machine learning models. The performance of the models was compared with a model using additional clinical data using fivefold cross validation with the area under the receiver operator characteristic curve (AUC) as a metric. Results: We included 13,876 T2D patients. The best performing model including only demographic and medication data was logistic regression with least absolute shrinkage and selection operator, with an AUC of 0.71. Ten variables were included (odds ratio): male gender (0.997), age (0.990), total drug count (1.012), glucose‐lowering drug count (1.039), sulfonylurea use (1.62), insulin use (1.769), pre‐mixed insulin use (1.109), insulin count (1.827), insulin duration (1.193), and antidepressant use (1.05). The proposed model obtained a similar performance to the model using additional clinical data. Conclusion: Using demographic and medication data, a model for identifying patients at increased risk of hypoglycaemia was developed using machine learning. This model can be used as a tool in primary care to screen for patients with T2D who may need additional attention to prevent or reduce hypoglycaemic events. [ABSTRACT FROM AUTHOR]

Aim: To assess the potential of supervised machine‐learning techniques to identify clinical variables for predicting short‐term and long‐term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). Materials and methods: We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow‐up of 2 years. Short‐ and long‐term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty‐four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine‐learning algorithms was compared for the prediction of short‐ and long‐term responses and the area under the receiver‐operating characteristic curve (AUC) was used to assess the performance of the prediction models. Results: The elastic net regularization‐based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short‐ and long‐term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78–0.83) and 0.81 (95% CI 0.79–0.84), respectively, and outperformed the other machine‐learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65–0.73) and 0.72 (95% CI 0.66–0.75) was obtained for predicting short‐term and long‐term response, respectively. Conclusions: Machine‐learning algorithm performed well in the prediction of an individual's short‐term and long‐term HbA1c response using baseline clinical variables. [ABSTRACT FROM AUTHOR]

Aims: To identify subgroups of patients with type 2 diabetes mellitus (T2DM) following distinct trajectories of HbA1c after insulin initiation and explore underlying differences in clinical characteristics. Materials and methods: A cohort study was conducted in patients with T2DM initiating insulin in 2007–2013 with a follow‐up of 2 to 4 years. Data were collected from the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database. The primary outcome was subgroups with different trajectories of HbA1c patterns after insulin initiation, as identified by latent class growth modeling. Differences between subgroups were tested using one‐way ANOVA, Kruskal‐Wallis or chi‐square tests, where appropriate. Results: From 1459 patients, three subgroups with distinct HbA1c patterns were identified. Group 1 (8%) initially showed a moderate decrease followed by an increase in HbA1c 2 years later, despite receiving more comedication. Group 2 (84%) showed a stable decrease. Group 3 (8%) had a high initial level of HbA1c and a rapid decline within the first year, followed by a slow increase thereafter. Group 1 patients were on average 6–7 years younger than patients in groups 2 and 3 and were more likely to receive sulfonylureas than Group 3 patients. Group 3 patients had a shorter diabetes duration and were less well‐controlled for HbA1c, systolic blood pressure and LDL‐cholesterol at insulin initiation. Conclusions: Most patients showed a stable HbA1c response, but one out of six patients showed either a poor response, or a rapid initial response only after insulin initiation. Response patterns were associated with age, diabetes duration and risk‐factor controls at the time of insulin initiation. [ABSTRACT FROM AUTHOR]

Aims: To assess variability in systolic blood pressure (SBP) and albuminuria (urinary albumin creatinine ratio [UACR]) responses in patients with type 2 diabetes mellitus initiating renin angiotensin aldosterone system (RAAS) inhibition, and to assess the association of response variability with cardiovascular outcomes. Material and Methods: We performed an observational cohort study in patients with type 2 diabetes who started RAAS inhibition between 2007 and 2013 (n = 1600). Patients were identified from general practices in the Netherlands. Individual response in SBP and UACR was assessed during 15 months’ follow‐up. Patients were categorized as: good responders (∆SBP <0 mm Hg and ∆UACR <0%); intermediate responders (∆SBP <0 mm Hg and ∆UACR >0% or ∆SBP >0 mm Hg and ∆UACR <0%); or poor responders (∆SBP >0 mm Hg and ∆UACR >0%). Multivariable Cox regression was performed to test the association between initial RAAS inhibition response and subsequent cardiovascular outcomes. Results: After starting RAAS inhibition, the mean SBP change was −13.2 mm Hg and the median UACR was −36.6%, with large between‐individual variability, both in SBP [5th to 95th percentile: 48.5‐20] and UACR [5th to 95th percentile: −87.6 to 171.4]. In all, 812 patients (51%) were good responders, 353 (22%) had a good SBP but poor UACR response, 268 (17%) had a good UACR but poor SBP response, and 167 patients (10%) were poor responders. Good responders had a lower risk of cardiovascular events than poor responders (hazard ratio 0.51, 95% confidence interval 0.30‐0.86; P = .012). Conclusions: SBP and UACR response after RAAS inhibition initiation varied between and within individual patients with type 2 diabetes treated in primary care. Poor responders had the highest risk of cardiovascular events, therefore, more efforts are needed to develop personalized treatment plans for these patients. [ABSTRACT FROM AUTHOR]

Aim: Quality indicators are used to measure whether healthcare professionals act according to guidelines, but few indicators focus on the quality of pharmacotherapy for diabetes. The aim of this study was to develop and validate a set of prescribing quality indicators (PQIs) for type 2 diabetes in primary care, and to apply this set in practice. To take into account the stepwise treatment of chronic disease, clinical action indicators were specifically considered.Methods: Potential PQIs were derived from clinical practice guidelines and evaluated using the RAND/UCLA Appropriateness Method, a modified Delphi panel. Thereafter, the feasibility of calculating the PQIs was tested in two large Dutch primary care databases including >80 000 diabetes patients in 2012.Results: 32 PQIs focusing on treatment with glucose, lipid, blood pressure and albuminuria lowering drugs, and on vaccination, medication safety and adherence were assessed by ten experts. After the Delphi panel, the final list of twenty PQIs was tested for feasibility. All PQIs definitions were feasible for measuring the quality of medication treatment using these databases. Indicator scores ranged from 18.8% to 90.8% for PQIs focusing on current medication use, clinical action and medication choice, and from 2.1% to 37.2% for PQIs focusing on medication safety.Discussion and Conclusions: Twenty PQIs focusing on treatment with glucose, lipid, blood pressure and albuminuria lowering drugs, and on medication safety in type 2 diabetes were developed, considered valid and operationally feasible. Results showed room for improvement, especially in initiation and intensification of treatment as measured with clinical action indicators. [ABSTRACT FROM AUTHOR]

Background: Routine primary care data are increasingly being used for evaluation and research purposes but there are concerns about the completeness and accuracy of diagnoses and events captured in such databases. We evaluated how well patients with major cardiovascular disease (CVD) can be identified using primary care morbidity data and drug prescriptions.Methods: The study was conducted using data from 17,230 diabetes patients of the GIANTT database and Dutch Hospital Data register. To estimate the accuracy of the different measures, we analyzed the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to hospitalizations and/or records with a diagnosis indicating major CVD, including ischaemic heart diseases and cerebrovascular events.Results: Using primary care morbidity data, 43% of major CVD hospitalizations could be identified. Adding drug prescriptions to the search increased the sensitivity up to 94%. A proxy of at least one prescription of either a platelet aggregation inhibitor, vitamin k antagonist or nitrate could identify 85% of patients with a history of major CVD recorded in primary care, with an NPV of 97%. Using the same proxy, 57% of incident major CVD recorded in primary or hospital care could be identified, with an NPV of 99%.Conclusions: A substantial proportion of major CVD hospitalizations was not recorded in primary care morbidity data. Drug prescriptions can be used in addition to diagnosis codes to identify more patients with major CVD, and also to identify patients without a history of major CVD. [ABSTRACT FROM AUTHOR]

OBJECTIVE To assess whether after the introduction of diabetes performance measures decreases in undertreatment correspond with increases in overtreatment for blood pressure (BP) and glycemic control in different patient age groups. RESEARCH DESIGN AND METHODS We conducted a cohort study using data from the Groningen Initiative to Analyse Type 2 Diabetes Treatment (GIANTT) database. General practices were included when data were available from 1 year before to at least 1 year after the introduction of diabetes performance measures. Included patients had a confirmed diagnosis of type 2 diabetes. Potential overtreatment was defined as prescribing maximum treatment or a treatment intensification to patients with a sustained low-risk factor level. Potential undertreatment was defined as a lack of treatment intensification in patients with a sustained high-risk factor level. Percentages of over- and undertreated patients at baseline were compared with those in subsequent years, and stratified analyses were performed for different patient age groups. RESULTS For BP, undertreatment significantly decreased from 61 to 57% in the first year after the introduction of performance measures. In patients >75 years of age, undertreatment decreased from 65 to ~61%. Overtreatment was relatively stable (~16%). For glycemic control, undertreatment significantly increased from 49 to 53%, and overtreatment remained relatively stable (~7%). CONCLUSIONS The improvement of BP undertreatment after introduction of the performance measures did not correspond with an increase in overtreatment. The performance measures appeared to have little impact on improving glucose-regulating treatment. The trends did not differ among patient age groups. [ABSTRACT FROM AUTHOR]

Abstract: Background: Statins are lipid-lowering drugs that reduce the risk of cardiovascular events in patients with diabetes. Objectives: The objective of this study was to determine whether statin treatment for primary prevention in newly diagnosed type 2 diabetes is cost-effective, taking nonadherence, baseline risk, and age into account. Methods: A cost-effectiveness analysis was performed by using a Markov model with a time horizon of 10 years. The baseline 10-year cardiovascular risk was estimated in a Dutch population of primary prevention patients with newly diagnosed diabetes from the Groningen Initiative to Analyse Type 2 Diabetes Treatment (GIANTT) database, using the United Kingdom Prospective Diabetes Study risk engine. Statin adherence was measured as pill days covered in the IADB.nl pharmacy research database. Cost-effectiveness was measured in costs per quality-adjusted life-year (QALY) from the health care payers’ perspective. Results: For an average patient aged 60 years, the base case, statin treatment was highly cost-effective at €2245 per QALY. Favorable cost-effectiveness was robust in sensitivity analysis. Differences in age and 10-year cardiovascular risk showed large differences in cost-effectiveness from almost €100,000 per QALY to almost being cost saving. Treating all patients younger than 45 years at diabetes diagnosis was not cost-effective (weighted cost-effectiveness of almost €60,000 per QALY). Conclusions: Despite the nonadherence levels observed in actual practice, statin treatment is cost-effective for primary prevention in patients newly diagnosed with type 2 diabetes. Because of large differences in cost-effectiveness according to different risk and age groups, the efficiency of the treatment could be increased by targeting patients with relatively higher cardiovascular risk and higher ages. [Copyright &y& Elsevier]

Background Failure of diagnosing and treatment of albuminuria play a role in morbidity and mortality in type 2 diabetes (T2DM). We evaluated guideline adherence and factors associated with albuminuria screening and treatment in T2DM patients in primary care. Methods Guidelines recommend annual measurement of albuminuria and, if increased, treatment with renin–angiotensin–aldosterone system (RAAS) blockers. We performed a cohort study of T2DM patients managed by 182 Dutch general practitioners (GPs; Groningen Initiative to Analyse Type 2 diabetes Treatment database), and evaluated guideline adherence in the years 2007–2009. We assessed whether demographic, clinical, organizational or provider factors determined guideline adherence with multilevel analyses. Results Data were available for 14 120 T2DM patients [47.6% male, mean age 67.3 ± 11.7 years, median diabetes duration 6 (IQR: 3–10) years]. The albumin–creatinine ratio (ACR) was measured in 45.2% in 2007, 57.4% in 2008 and 56.8% in 2009. Only 23.7% of all patients were measured every year and 21.4% were never measured. The ACR was more often measured in patients <75 years, with a previous ACR measurement, using anti-diabetic medication, and receiving additional care by a diabetes support facility. RAAS treatment was prescribed to 78.4% of patients with prevalent micro/macroalbuminuria, 66.5% with incident micro/macroalbuminuria, 59.3% with normoalbuminuria and 52.1% of those without ACR measurements. In those not treated with RAAS blockers, it was initiated in 14.3, 12.3, 3.0 and 2.3%, respectively. The presence of micro/macroalbuminuria, higher blood pressure, incidence of cardiovascular events and treatment with antihypertensive medication were the determinants of RAAS-treatment initiation. Conclusions Guideline implementation regarding the management of albuminuria in T2DM patients in primary care should be further improved. [ABSTRACT FROM PUBLISHER]

Abstract: Results of studies conducted 10–20 years ago show the prominence of commercial information sources in the adoption process of new drugs. Over the past decade, there has been a growing emphasis on practicing evidence-based medicine in drug prescribing. This raises the question whether professional information sources currently counterbalance the influence of commercial information sources in the adoption process. The aim of this study was to identify determinants influencing the adoption of a new drug class, the angiotensin II receptor blockers (ARBs), by general practitioners (GPs) in The Netherlands. A retrospective study was conducted to assess prevalent ARB prescribing for hypertensive patients using the Integrated Primary Care Information (IPCI) database. We conducted a survey among all GPs who participated in the IPCI project in 2003 to assess their exposure to commercial and professional information sources, perceived benefits and risks of ARBs, perceived influences of the professional network, and general characteristics. Multilevel logistic regression was applied to identify determinants of ARB adoption while adjusting for patient characteristics. Data were obtained from 70 GPs and 9470 treated hypertensive patients. A total of 1093 patients received ARBs (12%). GPs who reported frequent use of commercial information sources were more likely to prescribe ARBs routinely in preference to other antihypertensives, whereas GPs who used a prescribing decision support system and those who were involved in pharmacotherapy education were less likely to prescribe ARBs. Other factors that were associated with higher levels of ARB adoption included a more positive perception of ARBs regarding their effectiveness in lowering blood pressure, and working in single-handed practices or in rural areas. Aside from determinants related to the patient population, adoption of a new drug class among Dutch GPs is still determined more by their reliance on promotional information than by their use of professional information sources. [Copyright &y& Elsevier]

Objective: To investigate whether physicians' attitudes towards treatment guidelines for primary and secondary care differ between teaching and nonteaching hospitals shortly before and 4 years after the guidelines' introduction. Methods: Possible barriers and facilitators of joint treatment guidelines were obtained by selfadministered questionnaires twice during the study period. Questionnaires were distributed among all internists and cardiologists in the Groningen region of The Netherlands. Results: Physicians from teaching and nonteaching hospitals differed in attitude regarding the content and usefulness of the guidelines. Physicians from nonteaching hospitals more often believed that the guidelines are too restrictive (64% vs. 18%) and too rigid to apply to individual patients (14% vs. 6%) and that they oversimplify medical practice (79% vs. 35%). Physicians from teaching hospitals more often agreed that good recommendations for first-choice drugs had been made (76% vs. 50%) and that these guidelines are a convenient source of advice (94% vs. 57%), can facilitate communication with general practitioners (94% vs. 71%), and can improve the quality of pharmacotherapeutic care (88% vs. 43%). Four years later, a larger proportion of physicians from both hospital settings had a negative attitude towards the usefulness of the guidelines, but the difference in attitude between teaching and nonteaching hospitals remained the same. Conclusion: Physicians from nonteaching hospitals were less positive about the usefulness of joint treatment guidelines than physicians from teaching hospitals were. Results from studies on the implementation of guidelines in teaching hospitals can therefore not be transferred to nonteaching settings. [ABSTRACT FROM AUTHOR]

We assessed sex differences across the life span in the lipid profile of type 2 diabetes (T2D) patients treated and not treated with statins. We used the Groningen Initiative to ANalyze Type 2 diabetes Treatment database, which includes T2D patients from the north of the Netherlands. Patients with a full lipid profile determined between 2010 and 2012 were included. We excluded patients treated with other lipid-lowering drugs than statins. Sex differences in low- and high-density lipoprotein cholesterol (LDL-c and HDL-c) and triglyceride (TG) levels across 11 age groups stratified by statin treatment were assessed using linear regression. We included 26,849 patients (51% women, 55% treated with statins). Without statins, women had significantly lower LDL-c levels than men before the age of 45 years, similar levels between 45 and 49 years, and higher levels thereafter. With statins, similar LDL-c levels were shown up to the age of 55, and higher levels in women thereafter. Women had significantly higher HDL-c levels than men, regardless of age or statin treatment. Men had significantly higher TG levels up to the age of 55 and 60, depending on whether they did not take or took statins, respectively, and similar levels thereafter. When managing cardiovascular risk in patients with T2D, attention is needed for the menopausal status of women and for TG levels in younger men. [ABSTRACT FROM AUTHOR]

Abstract: Background: Low rates of treatment modification in patients with insufficiently controlled risk factors are common in type 2 diabetes. Although adherence problems are often mentioned in surveys as a reason for not intensifying treatment, observational studies have shown inconclusive results. Objective: To assess how medication adherence affects treatment modifications for hypertension and hyperglycemia in patients with type 2 diabetes. Methods: This was a cohort study of 11,268 primary care patients with type 2 diabetes in the Netherlands. Inclusion criteria were diagnosis before 2007, ≥1 prescription to antihypertensive or glucose-regulating medication in the preceding 6 months, and a systolic blood pressure level ≥140 mm Hg or glycosylated hemoglobin ≥7% in 2007. Patients on maximal treatment were excluded. Treatment modifications as observed from prescriptions were classified as none, dose increase, dose decrease, class switch, class addition, or class discontinuation. Refill adherence was assessed as medication possession ratio or length of last gap between refills. We performed multilevel multinomial regression analysis to test for associations. Results: We included 4980 diabetic patients with elevated blood pressure and 2945 diabetic patients with elevated glycosylated hemoglobin levels. Patients with lower adherence for antihypertensive drugs were more likely to have those medications discontinued (odds ratio [OR] for every 10% lower medication possession ratio =1.22; 95% CI, 1.11–1.33) or the dose decreased (OR = 1.14; CI 1.01–1.28). For glucose-regulating medication, dose increases (OR = 0.92; 95% CI, 0.85–0.98) and medication additions (OR = 0.90; 95% CI, 0.82–0.99) were less likely in patients with lower adherence levels. Conclusions: Low adherence inhibits the intensification of glucose-regulating but not antihypertensive medication in type 2 diabetic patients with insufficiently controlled risk factors in the Netherlands. Adherence problems may lead to diminished or even discontinued antihypertensive treatment. [Copyright &y& Elsevier]