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Background and Aims: Low systolic blood pressure (SBP) levels while being treated with antihypertensives may cause hypotension-related adverse events (hrAEs), especially in the elderly, women, and frail patients. We aimed to assess the association between the occurrence of hrAEs and low SBP levels, age, sex, and polypharmacy among patients with type 2 diabetes (T2D) treated with antihypertensives.Methods: In this cohort study, we used the Groningen Initiative to ANalyse Type 2 diabetes Treatment (GIANTT) database which includes patients managed for T2D in primary care from the north of the Netherlands. Patients treated with ≥1 antihypertensive drug and ≥1 SBP measurement between 2012 and 2014 were included. The outcome was the presence of an hrAE, i.e. postural hypotension, dizziness, weakness/tiredness, and syncope in 90 days before or after the lowest recorded SBP level. Age (≥70 vs. Results: We included 21,119 patients, 49% of which were ≥70 years old, 52% were women, 57% had polypharmacy, 61% had an SBP level Conclusion: Low SBP levels in patients with T2D treated with antihypertensives is associated with an increase in hrAEs. Older patients and those with polypharmacy are particularly at risk of hrAEs. Age, sex, and polypharmacy did not modify the risk of hrAEs associated with a low SBP level.

ObjectiveTo assess trends in systolic blood pressure (SBP) thresholds at initiation of antihypertensive treatment in patients with type 2 diabetes and the impact of age and frailty on these trends.Study design and settingA repeated cross-sectional cohort study (2007–2014) using the Groningen Initiative to Analyse Type 2 diabetes Treatment database was conducted. The influence of calendar year, age or frailty and the interaction between year and age or frailty on SBP thresholds were assessed using multilevel regression analyses adjusted for potential confounders.ResultsWe included 4819 patients. The mean SBP at treatment initiation was 157 mm Hg in 2007, rising to 158 mm Hg in 2009 and decreasing to 151 mm Hg in 2014. This quadratic trend was significant (pConclusionAfter an initial rise, the observed SBP thresholds decreased over time and were not influenced by age or frailty. This is in contrast with changed guideline recommendations towards more personalised treatment during the study period and illustrates that changing prescribing practice may take considerable time. Patient-specific algorithms and tools focusing on when and when not to initiate treatment could be helpful to support personalised diabetes care.

BackgroundTo develop targeted and tailored interventions for addressing medication non-adherence, it is important to identify underlying factors.ObjectiveTo identify factors associated with non-adherence as well as subtypes of non-adherence to antihypertensive or antihyperlipidemic drugs among patients with type 2 diabetes in Indonesia.DesignAn observational multicenter cross-sectional survey.ParticipantsPatients with type 2 diabetes using either antihypertensive or antihyperlipidemic drugs in four regions in Indonesia.Main MeasuresNon-adherence and its subtypes of intentional and unintentional non-adherence were assessed using the Medication Adherence Report Scale. Necessity and concern beliefs were assessed with the Beliefs about Medicines Questionnaire. We applied binary and multinomial logistic regression to assess associations of medication beliefs, sociodemographic factors, and clinical-related factors to non-adherence and report odds ratios (OR) with 95% confidence intervals (CI).Key ResultsOf 571 participating patients (response rate 97%), 45.5% and 52.7% were non-adherent to antihypertensive and antihyperlipidemic drugs, respectively. Older age was associated with non-adherence to antihypertensive drugs (60–69 years) (OR, 5.65; 95% CI, 2.68–11.92), while higher necessity beliefs (OR, 0.92; 95% CI, 0.88–0.95) were associated with less non-adherence. Factors associated with non-adherence to antihyperlipidemic drugs were female gender (OR, 1.84; 95% CI, 1.03–3.27) and higher concern beliefs (OR, 1.10; 95% CI, 1.03–1.18), while higher necessity beliefs (OR, 0.89; 95% CI, 0.83–0.96) were associated with less non-adherence.ConclusionsThe main factors associated with non-adherence to antihypertensive and antihyperlipidemic drugs are modifiable. In general, beliefs about the necessity of the drug are important but for antihyperlipidemic drugs concerns are important as well. Healthcare providers should pay attention to identify and address medication beliefs during patient counselling.

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.

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 5-fold 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), premixed 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. This article is protected by copyright. All rights reserved.

Background Hypoglycaemia is a common and potentially avoidable adverse event in people with type 2 diabetes (T2D). It can reduce quality of life, increase healthcare costs, and reduce treatment success. We investigated self-management issues associated with hypoglycaemia and self-identified causes of hypoglycaemia in these patients. Methods In this mixed methods study qualitative semi-structured interviews were performed, which informed a subsequent quantitative survey in T2D patients. All interviews were audio recorded, transcribed verbatim and coded independently by two coders using directed content analysis, guided by the Theoretical Domains Framework. Descriptive statistics were used to quantify the self-management issues and causes of hypoglycaemia collected in the survey for the respondents that had experienced at least one hypoglycaemic event in the past. Results Sixteen participants were interviewed, aged 59–84 years. Participants perceived difficulties in managing deviations from routine, and they sometimes lacked procedural knowledge to adjust medication, nutrition or physical activity to manage their glucose levels. Grief and loss of support due to the loss of a partner interfered with self-management and lead to hypoglycaemic events. Work ethic lead some participant to overexerting themselves, which in turn lead to hypoglycaemic events. The participants had difficulties preventing hypoglycaemic events, because they did not know the cause, suffered from impaired hypoglycaemia awareness and/or did not want to regularly measure their blood glucose. When they did recognise a cause, they identified issues with nutrition, physical activity, stress or medication. In total, 40% of respondents reported regular stress as an issue, 24% reported that they regularly overestimated their physical abilities, and 22% indicated they did not always know how to adjust their medication. Around 16% of patients could not always remember whether they took their medication, and 42% always took their medication at regular times. Among the 83 respondents with at least one hypoglycaemic event, common causes for hypoglycaemia mentioned were related to physical activity (67%), low food intake (52%), deviations from routine (35%) and emotional burden (28%). Accidental overuse of medication was reported by 10%. Conclusion People with T2D experience various issues with self-managing their glucose levels. This study underlines the importance of daily routine and being able to adjust medication in relation to more physical activity or less food intake as well as the ability to reduce and manage stress to prevent hypoglycaemic events.

Simple SummaryA growing number of colorectal cancer survivors live with type 2 diabetes, as a result of improved cancer diagnosis and treatment. These patients might have worse glycemic control after their cancer diagnosis, which may increase the risk of cardiovascular diseases. This prospective cohort study evaluated the quality of glycemic control for colorectal cancer survivors, as compared to those without cancer in Dutch primary care for diabetes. During a 10-year follow-up for 57,330 patients, there were 705 patients diagnosed with colorectal cancer. No clinically relevant difference on the probability of reaching the target HbA1c was observed between colorectal cancer survivors and patients with no history of cancer. These results showed a robust diabetes care system, implying that the glycemic control for colorectal cancer survivors can be delegated to the primary care professionals.Cancer survivors with diabetes tend to have worse glycemic control after their cancer diagnosis, which may increase the risk of cardiovascular diseases. We aimed to investigate whether glycemic control differs between colorectal cancer (CRC) survivors and those without cancer, among patients with type 2 diabetes being treated in the Dutch primary care. The Zwolle Outpatient Diabetes project Integrating Available Care database was linked with the Dutch Cancer Registry (n = 71,648, 1998-2014). The cases were those with stage 0-III CRC, and the controls were those without cancer history. The primary and secondary outcomes were the probability of reaching the glycated hemoglobin (HbA1c) target and the mean of HbA1c during follow-up, respectively. Mixed linear modeling was applied, where the status of CRC was a time-varying variable. Among the 57,330 patients included, 705 developed CRC during follow-up. The mean probability of reaching the HbA1c target during follow-up was 73% versus 74% (p = 0.157) for CRC survivors versus those without cancer, respectively. The mean HbA1c was 51.1 versus 50.8 mmol/mol (p = 0.045) among CRC survivors versus those without cancer, respectively. We observed a clinically comparable glycemic control among the CRC survivors without cancer, indicating that glycemic control for CRC survivors can be delegated to primary care professionals.

Introduction: A pharmacist-led intervention in Community Health Centers (CHCs) in Indonesia targeted at patients with type 2 diabetes non-adherent to antihypertensive drugs resulted in a significant improvement in adherence to these drugs. The aim of this study was to evaluate the process of implementation this intervention intended to improve adherence to antihypertensive drugs from both the pharmacist and the patient perspective.Methods: Using the RE-AIM (Reach, Effectiveness, Adoption, Implementation and Maintenance) framework, we conducted a focus group among pharmacists (N = 5) and a survey among patients with complete follow-up (N = 44) participating in the intervention group.Results: All pharmacists adopted the provided training and found support tools useful. The pharmacists implemented the intervention as intended (adequate intervention fidelity >69%). Factors relevant for implementation included having sufficient time and confidence, home visits for specific patients, multidisciplinary collaboration, and availability of a personal counseling room. To maintain the intervention, the need for practical guidance and support from health care authorities was mentioned. Most patients (96%) were satisfied with the information provided by the pharmacists and they believed the tailored counselling was helpful. Most patients (84%) reported that the duration of counselling was sufficient. The large majority of patients would like to receive the counselling regularly.Conclusion: Positive effects of the pharmacist-led intervention can be explained by adequate levels of reach, adoption and implementation in the participating CHCs. For successful implementation and maintenance in Indonesia or other low-and middle-income countries, sufficient training, resources, multidisciplinary collaboration, guidance and support from health care authorities are expected to be important.

AIM: To assess the effects of a targeted and tailored pharmacist-led intervention among patients with type 2 diabetes (T2DM) who are non-adherent to antihypertensive drugs.METHODS: A cluster-randomised controlled trial was conducted in 10 community health centres (CHCs) in Indonesia among T2DM patients aged ≥18 years who reported non-adherence to antihypertensive drugs according to the Medication Adherence Report Scale (MARS-5). Patients in CHCs randomised to the intervention group received a tailored intervention based on their adherence barriers (e.g., forgetfulness, lack of knowledge, lack of motivation, and/or other drug-related problems) using a simple question-based flowchart at baseline and 1-month follow-up. Patients in control CHCs received usual care. Primary outcome was the between-group difference in change in MARS-5 score from baseline to 3-months follow-up. Secondary outcomes included changes in patients' blood pressure and their medication beliefs. Differences in difference in primary and secondary outcomes between groups were assessed using General Linear Models.RESULTS: In total, 201 patients were screened for eligibility, 113 met the inclusion criteria and participated; 89 (79%) patients had complete follow-up. Forgetfulness (42%) and lack of knowledge (18%) were the most common adherence barriers identified at baseline. The intervention improved medication adherence by 4.62 points on the MARS-5 scale [95%CI: 0.93 to 8.34, p-value = 0.008]. There were no significant changes in blood pressure levels and beliefs about antihypertensive drugs.CONCLUSION: A tailored low-cost pharmacist-led intervention aimed at non-adherent T2DM patients resulted in an improvement in medication adherence to antihypertensive drugs. There were no significant changes in secondary outcomes.

AIM: To explore the added value of diabetes-related genetic risk scores (GRSs) to readily available clinical variables in the prediction of glycated haemoglobin (HbA1c) levels after initiation of glucose-regulating drugs.MATERIALS AND METHODS: We conducted a cohort study in people with type 2 diabetes (T2DM) from the Groningen Initiative to Analyse Type 2 Diabetes Treatment (GIANTT) database who initiated metformin (MET) or sulphonylurea derivatives (SUs) and for whom blood samples were genotyped. The primary outcome was HbA1c level at 6 months, adjusted for baseline HbA1c. GRSs were based on single nucleotide polymorphisms linked to insulin sensitivity, β-cell activity, and T2DM risk in general. Associations were analysed using multiple linear regression to assess whether adding the GRSs increased the explained variance in a prediction model that included age, gender, diabetes duration and cardio-metabolic biomarkers.RESULTS: We included 282 patients initiating MET and 89 patients initiating SUs. In the MET prediction model, diabetes duration of >3 months when starting MET was associated with 2.7-mmol/mol higher HbA1c level. For SUs, no significant clinical predictors were identified. Addition of the GRS linked to insulin sensitivity (for MET), β-cell activity (for SUs) and T2DM risk (for both) to the models did not improve the explained variance significantly (22% without vs. 22% with GRS) for the MET and (14% without vs. 14% with GRS) for the SUs model, respectively.CONCLUSION: This study did not indicate a significant effect of GRS related to T2DM in general or to the drugs' mechanism of action for prediction of inter-individual HbA1c variability in the short term after initiation of MET or SU therapy.

Background Individualizing goals for people with type 2 diabetes may result in deintensification of medication, but a comprehensive picture of deprescribing practices is lacking. Aims To conduct a scoping review in order to assess the rates, determinants and success of implementing deprescribing of glucose‐, blood pressure‐ or lipid‐lowering medications in people with diabetes. Methods A systematic search on MEDLINE and Embase between January 2007 and January 2019 was carried out for deprescribing studies among people with diabetes. Outcomes were rates of deprescribing related to participant characteristics, the determinants and success of deprescribing, and its implementation. Critical appraisal was conducted using predefined tools. Results Fourteen studies were included; eight reported on rates, nine on determinants and six on success and implementation. Bias was high for studies on success of deprescribing. Deprescribing rates ranged from 14% to 27% in older people with low HbA1c levels, and from 16% to 19% in older people with low systolic blood pressure. Rates were not much affected by age, gender, frailty or life expectancy. Rates were higher when a reminder system was used to identify people with hypoglycaemia, which led to less overtreatment and fewer hypoglycaemic events. Most healthcare professionals accepted the concept of deprescribing but differed on when to conduct it. Deprescribing glucose‐lowering medications could be successfully conducted in 62% to 75% of participants with small rises in HbA1c. Conclusions Deprescribing of glucose‐lowering medications seems feasible and acceptable, but was not widely implemented in the covered period. Support systems may enhance deprescribing. More studies on deprescribing blood pressure‐ and lipid‐lowering medications in people with diabetes are needed.

BACKGROUND: Adherence to antihypertensive drugs in patients with diabetes is important. To support adherence, attention should be paid to the dynamic process of implementation, persistence and reinitiation of these drugs. We assessed non-adherence, non-persistence and reinitiation patterns for antihypertensive drugs in patients on oral diabetes drugs and identified pharmacy-based predictors of these processes.METHODS: We conducted a cohort study in patients on oral diabetes drugs who initiated antihypertensive drugs between 1995-2015, as registered in the IADB.nl pharmacy database. Non-adherence was defined as a medication possession ratio < 80% and non-persistence as a gap > 180 days. We defined reinitiation as the dispensing of an antihypertensive drug within one year following discontinuation. We provide descriptive statistics for different time periods and applied logistic and Cox regressions to assess associations with sociodemographic and drug-related factors.RESULTS: Of 6,669 initiators, non-adherence rates in persistent patients decreased from 11.0% in the first year to 8.5% and 7.7% in the second and third years, respectively. Non-persistence rates decreased from 18.0% in the first year to 3.7% and 2.9% in the second and third years, respectively. Of the 1,201 patients who discontinued in the first year, 22.0% reinitiated treatment within one year. Non-adherence and non-persistence rates were lower in the more recent time period. Predictors of non-adherence were secondary prevention (OR: 1.45; 95% CI: 1.10-1.93) and diuretics as initial drug class (OR: 1.37; 95% CI: 1.08-1.74). Predictors of non-persistence were female gender (HR: 1.18; 95% CI: 1.05-1.32), older age (HR: 1.33; 95% CI: 1.08-1.63) and diuretics, beta-blocking agents or calcium channel blockers as initial drug class. Longer duration of persistence was a predictor of reinitiation.CONCLUSIONS: Adherence to antihypertensive drugs in patients on oral diabetes drugs has improved over time. The first year after initiation is the most crucial with regard to non-adherence and non-persistence, and the risk groups are different for both processes. Early non-persistence is a risk factor for not reinitiating treatment.

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.

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.

AimsThe aim of this study was to describe and compare treatment modifications and discontinuation, adherence levels and response to treatment in patients with type 2 diabetes initiating on low-dose vs. standard-dose statin treatment.MethodsA 2-year follow-up cohort study was performed using data from the Groningen Initiative to Analyse Type 2 Diabetes Treatment (GIANTT) database in patients with type 2 diabetes initiating statin treatment between January 2007 and December 2012. First, we determined whether there were differences in treatment modifications and discontinuation after statin initiation between patients starting on a low-dose vs. standard-dose. Second, we looked at differences in adherence and LDL-cholesterol response after 2 years follow-up between these groups.ResultsAround 22% of patients initiated statin treatment on a dose lower than recommended. More than half of them remained on a low dose during a 2-year follow-up period, whereas less than 15% received a dose increase. Of the patients initiating on standard-dose, also more than half remained on the same treatment during this period, whereas 8% received a dose decrease without subsequent increase. Over 25% of patients starting on low-dose or standard-dose treatment discontinued treatment, often within the first 180 days after initiation or after a first treatment change. Patients on low-dose treatment had lower adherence levels and were less likely to have adequate LDL-cholesterol response compared with patients on standard-dose after 2 years follow-up.ConclusionsCurrent patterns of statin treatment in patients with type 2 diabetes are suboptimal, with discontinuation, inadequate adherence levels and lack of treatment intensification seen in those who had inadequate LDL-cholesterol response after 2 years of follow-up. Patients starting on low-dose had more treatment modifications, discontinuation and adherence problems as compared with those starting on standard-dose treatment, which calls for a closer look at the rationale of starting patients on low-dose statin treatment.

BACKGROUND: Guideline-adherent prescribing for treatment of multiple risk factors in type 2 diabetes (T2D) patients is expected to improve clinical outcomes. However, the relationship to Health-Related Quality of Life (HRQoL) is not straightforward since guideline-adherent prescribing can increase medication burden.OBJECTIVES: To test whether guideline-adherent prescribing and disease-specific medication burden are associated with HRQoL in patients with T2D.METHODS: Cross-sectional study including 1,044 T2D patients from the e-VitaDM/ZODIAC study in 2012 in the Netherlands. Data from the diabetes visit, such as laboratory and physical examinations and prescribed medication, and from two HRQoL questionnaires, the EuroQol 5 Dimensions 3 Levels (EQ5D-3L) and the World Health Organization Well-Being Index (WHO-5) were collected. Twenty indicators assessing prescribing of recommended glucose lowering drugs, statins, antihypertensives and renin-angiotensin-aldosterone system (RAAS)-inhibitors and potentially inappropriate drugs from a validated diabetes indicator set were included. Disease-specific medication burden was assessed using a modified version of the Medication Regimen Complexity Index (MRCI). Associations were tested with regression models, adjusting for age, gender, diabetes duration, comorbidity, body mass index and smoking.RESULTS: The mean MRCI was 7.1, the median EQ5D-3L-score was 0.86 and the mean WHO-5 score was 72. Seven indicators included too few patients and were excluded from the analysis. The remaining thirteen indicators focusing on recommended start, intensification, current and preferred use of glucose lowering drugs, statins, antihypertensives, RAAS inhibitors, and on inappropriate prescribing of glibenclamide and dual RAAS blockade were not significantly associated with HRQoL. Finally, also the MRCI was not associated with HRQoL.CONCLUSIONS: We found no evidence for associations between guideline-adherent prescribing or disease-specific medication burden and HRQoL in T2D patients. This gives no rise to refrain from prescribing intensive treatment in T2D patients as recommended, but the interpretation of these results is limited by the cross-sectional study design and the selection of patients included in some indicators.

Prescribing quality indicators (PQIs) are used to assess whether patients are treated according to guideline recommendations. To ensure that the use of PQIs leads to improved patient outcomes, their predictive validity needs to be assessed (1). We assessed whether newly developed PQIs for diabetes care are associated with better intermediate cardiometabolic and renal outcomes in patients with type 2 diabetes. Special focus was on clinical action indicators that consider patients with elevated risk factor levels to be receiving adequate treatment when treatment is either started or intensified or when they return to control (2). Such indicators are considered more meaningful and fair than presently used indicators that focus on current medication use or on achieving risk factor control (3). A cohort study was conducted using data from the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database, including medical records data of 26,321 patients with type 2 diabetes in primary care. Eleven previously …

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.

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.

Objective: The aim was to assess demographic and clinical factors as predictors of short (6 months) and long term (18 months) HbA1c levels in diabetes patients initiating metformin treatment. Research design and methods: We conducted a cohort study including type 2 diabetes patients who received their first metformin prescription between 2007 and 2013 in the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database. The primary outcome was HbA1c level at follow-up adjusted for baseline HbA1c; the secondary outcome was failing to achieve the target HbA1c level of 53 mmol/mol. Associations were analyzed by linear and logistic regression. Multiple imputation was used for missing data. Additional analyses stratified by dose and adherence level were conducted. Results: The cohort included 6050 patients initiating metformin. Baseline HbA1c at target consistently predicted better HbA1c outcomes. Longer diabetes duration and lower total cholesterol level at baseline were predictors for higher HbA1c levels at 6 months. At 18 months, cholesterol level was not a predictor. Longer diabetes duration was also associated with not achieving the target HbA1c at follow-up. The association for longer diabetes duration was especially seen in patients starting on low dose treatment. No consistent associations were found for comorbidity and comedication. Conclusions: Diabetes duration was a relevant predictor of HbA1c levels after 6 and 18 months of follow-up in patients initiating metformin treatment. Given the study design, no causal inference can be made. Our study suggests that prompt treatment intensification may be needed in patients who have a longer diabetes duration at treatment initiation.

Objective To assess whether quality indicators for treatment of cardiovascular and renal risk factors are associated with short-term outcomes in patients with diabetes.Design A prospective cohort study using linear regression adjusting for confounders.Setting The GIANTT database (Groningen Initiative to Analyse Type 2 Diabetes Treatment) containing data from primary care medical records from The Netherlands.Participants 15 453 patients with type 2 diabetes mellitus diagnosed before 1 January 2008. Mean age 66.5 years, 47.5% men.Exposure Quality indicators assessing current treatment (CT) status or treatment intensification (TI) for patients with diabetes with elevated cardiovascular or renal risk factors.Main outcome measures Low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP), and albumin: creatinine ratio (ACR) before and after assessment of treatment quality.Results Use of lipid-lowering drugs was associated with better LDL-C levels (-0.41 mmol/litre; 95% CI -0.48 to -0.34). Use of blood pressure-lowering drugs and use of renin-angiotensin system inhibitors in patients with elevated risk factor levels was not associated with better SBP and ACR outcomes, respectively. TI was also associated with better LDL-C (-0.82 mmol/litre; CI -0.93 to -0.71) in patients with elevated LDL-C levels, and with better SBP (-1.26 mm Hg; CI -2.28 to -0.24) in patients with two elevated SBP levels. Intensification of albuminuria-lowering treatment showed a tendency towards better ACR (-2.47 mmol/mg; CI -5.32 to 0.39) in patients with elevated ACR levels.Conclusions Quality indicators of TI were predictive of better short-term cardiovascular and renal outcomes, whereas indicators assessing CT status showed association only with better LDL-C outcome.

Background: Performance measures are used for assessing quality of care. Higher performance shown by these measures is expected to reflect better care, but little is known whether they predict better patient outcomes.Objective: To assess the predictive value of performance measures of glucose management on glycemic control, and evaluate the impact of patient characteristics on this association.Research Design: Cohort study (2007-2009).Subjects: A total of 15,454 type 2 diabetes patients (mean age, 66.5 y; 48% male) from the GIANTT cohort.Measures: We included performance measures assessing frequency of HbA1c monitoring, glucose-lowering treatment status, and treatment intensification. Associations between performance and glycemic control were tested using multivariate linear regression adjusted for confounding, reporting estimated differences in HbA1c with 95% confidence intervals (CI). Impact of patient characteristics was examined through interactions.Results: Annual HbA1c monitoring was associated with better glycemic control when compared with no such monitoring (HbA1c -0.29%; 95% CI -0.37, -0.22). This association lost significance in patients with lower baseline HbA1c, older age, and without macrovascular comorbidity. Treatment status was associated with better glycemic control only in patients with elevated baseline HbA1c. Treatment intensification after elevated HbA1c levels was associated with better glycemic control compared with no intensification (HbA1c -0.21; 95% CI -0.26, -0.16).Conclusions: Performance measures of annual HbA1c monitoring and of treatment intensification did predict better patient outcomes, whereas the measure of treatment status did not. Predictive value of annual monitoring and of treatment status varied across patient characteristics, and it should be used with caution when patient characteristics cannot be taken into account.

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

BackgroundAccounting for justifiable variance is important for fair comparisons of treatment quality. The variance between general practices in treatment quality of type 2 diabetes (T2DM) patients may be attributed to the underlying patient population and practice characteristics. The objective of this study is to describe the between practice differences in treatment, and identify patient and practice level characteristics that may explain these differences.MethodsThe data of 24,607 T2DM patients from 183 general practices in the Netherlands were used. Treatment variance was assessed in a cross- sectional manner for: glucose- lowering drugs/metformin, lipid- lowering drugs/statins, blood pressure- lowering drugs/ACE- inhibitor or ARB. Patient characteristics tested were age, gender, diabetes duration, comorbidity, comedication. Practice characteristics were number of T2DM patients, practice type, diabetes assistant available. Multilevel logistic regression was used to examine the between practice variance in treatment and the effect of characteristics on this variance.ResultsTreatment rates varied considerably between practices (IQR 9.5-13.9). The variance at practice level was 7.5% for glucose- lowering drugs, 3.6% for metformin, 3.1% for lipid- lowering drugs, 10.3% for statins, 8.6% for blood pressure- lowering drugs, and 3.9% for ACEinhibitor/ARB. Patient and practice characteristics explained 19.0%, 7.5%, 20%, 6%, 9.9%, and 13.4% of the variance respectively. Age, multiple chronic drugs, and >= 3 glucose- lowering drugs were the most relevant patient characteristics. Number of T2DM patients per practice was the most relevant practice characteristic.DiscussionConsiderable differences exist between practices in treatment rates. Patients' age was identified as characteristic that may account for justifiable differences in especially lipid- lowering treatment. Other patient or practice characteristics either do not explain or do not justify the differences.

Purpose Lareb Intensive Monitoring, LIM, is a non-interventional observational cohort method which follows first-time users of certain drugs during a certain period of time and collects information about adverse drug reactions, ADRs. In order for LIM to be a useful pharmacovigilance tool, it is important to know whether the LIM population is comparable to the whole population using the drug. The aim of this study is to compare the LIM diabetes population with an external diabetes reference population on characteristics that may influence the patient’s susceptibility for ADRs. Methods In this study, a LIM diabetes population was compared to a reference diabetes population derived from The Groningen Initiative to ANalyse Type 2 diabetes Treatment project. Comparisons were made regarding age, gender, body mass index and polypharmacy, as well as diabetes medication used and disease/treatment duration. Results LIM patients were more often men (58.5% vs 50.8%) and in general younger (59.1 vs 64.7 years) and healthier, by that meaning they had a higher percentage of de novo treated patients (55.5% vs 53.2%), a shorter diabetes treatment duration (3.7 vs 5.5 years) and used less co-medication than patients in the reference population. Conclusions This study shows that diabetes patients participating in a web-based intensive monitoring system differ from a reference population. The observed differences might lead to an underestimation of ADRs, but it is not clear whether this would also influence the type or time-course of the reported ADRs. When interpreting results from LIM studies, one should take these differences into account. Copyright © 2012 John Wiley & Sons, Ltd. key words—pharmacovigilance; adverse drug reaction; intensive monitoring; web; diabetes population; pharmacoepidemiology

ObjectivesTo assess whether patients' willingness to add a blood pressure-lowering drug and the importance they attach to specific treatment characteristics differ among age groups in patients with type 2 diabetes.Materials and MethodsPatients being prescribed at least an oral glucose-lowering and a blood pressure-lowering drug completed a questionnaire including a discrete choice experiment. This experiment contained choice sets with hypothetical blood pressure-lowering drugs and a no additional drug alternative, which differed in their characteristics (i.e. effects and intake moments). Differences in willingness to add a drug were compared between patients = 75 years (aged) using Pearson chi(2)-tests. Multinomial logit models were used to assess and compare the importance attached to the characteristics.ResultsOf the 161 patients who completed the questionnaire, 151 (72%) could be included in the analyses (mean age 68 years; 42% female). Aged patients were less willing to add a drug than non-aged patients (67% versus 84% respectively; P = 0.017). In both age groups, the effect on blood pressure was most important for choosing a drug, followed by the risk of adverse drug events and the risk of death. The effect on limitations due to stroke was only significant in the non-aged group. The effect on blood pressure was slightly more important in the non-aged than the aged group (P = 0.043).ConclusionsAged patients appear less willing to add a preventive drug than non-aged patients. The importance attached to various treatment characteristics does not seem to differ much among age groups.

Objective:To determine the association between adherence, dose and low-density lipoprotein (LDL) cholesterol response in patients with type 2 diabetes initiating statin treatment.Research design and methods:This cohort study was performed using data for 2007–2012 from the Groningen Initiative to Analyse Type 2 Diabetes Treatment (GIANTT) database. The association between adherence to a standard-dose statin and LDL cholesterol response was assessed using linear regression, adjusting for covariates. The effect of low-dose versus standard-dose was assessed in a propensity-score matched cohort. Adherence rates, defined as the proportion of days covered (PDC), were estimated between statin initiation and LDL outcome measurement.Main outcome measure:LDL cholesterol level at follow-up.Results:The effect of adherence on LDL cholesterol response, measured in 2160 patients, was dependent on the baseline LDL cholesterol level. For patients with a baseline LDL cholesterol of 3.7 mmol/l and an adherence rate of 80%, a 40% reduction in LDL cholesterol was predicted. In the matched sample of 1144 patients, the treatment dose showed a difference in impact on the outcome for adherence rates higher than 50%. It was estimated that a patient with a baseline LDL cholesterol of 3.7 mmol/l will need an adherence rate of at least 76% on low-dose and 63% on standard-dose treatment to reach the LDL cholesterol target of 2.5 mmol/l.Limitations:Adherence was measured as the PDC, which is known to overestimate actual adherence. Also, we were not able to adjust for lifestyle factors.Conclusions:We determined the concurrent effect of treatment adherence and dose on LDL cholesterol outcomes. Given the adherence levels seen in clinical practice, diabetes patients initiating statin treatment are at high risk of not reaching the recommended cholesterol target, especially when they start on a low-dose statin.