Prescription Sequence Symmetry Analysis (PSSA) to assess prescribing cascades: a step-by-step guide.

Prescribing cascades occur when patients are prescribed medication to treat the adverse drug reaction of previously prescribed medication. Prescription sequence symmetry analysis (PSSA) can be used to assess the association between two medications in prescription or dispensing databases and thus the potential occurrence of prescribing cascades. In this article, a step-by-step guide is presented for conducting PSSA to assess prescribing cascades. We describe considerations for medication data collection and setting time periods for relevant parameters, including washout window, exposure window, continued exposure interval and blackout period. With two examples, we illustrate the impact of changes in these parameters on the strengths of associations observed. Given the impact seen, we recommend that researchers clearly specify and explain all considerations regarding medication included and time windows set when studying prescribing cascades with PSSA, and conduct subgroup and sensitivity analyses.

Sex disparities in medication prescribing amongst patients with type 2 diabetes mellitus managed in primary care.

BACKGROUND: Sex differences in clinical outcomes have been observed for patients with type 2 diabetes mellitus (T2DM). These could be related to sex disparities in treatment. OBJECTIVES: To determine whether there are sex disparities in medication prescribing amongst patients with T2DM. METHODS: A cohort study was conducted using the Groningen Initiative to ANalyze Type 2 diabetes Treatment (GIANTT) database, which includes data from primary care patients with T2DM from the north of the Netherlands. Data on demographics, physical examinations, laboratory measurements and prescribing were extracted. A set of validated prescribing quality indicators assessing the prevalence, start, intensification and safety of glucose-, lipid-, blood pressure- and albuminuria-lowering medication was applied for the calendar year 2019. Univariate logistic regression analyses were conducted. RESULTS: We included 10,456 patients (47% females). Females were less often treated with metformin (81.7% vs. 86.5%; OR 0.70, 95% CI 0.61-0.80), and were less often prescribed a renin-angiotensin-aldosterone inhibitor (RAAS-i) when treated with multiple blood pressure-lowering medicines (81.9% vs. 89.3%; OR 0.55, 95% CI 0.46-0.64) or when having albuminuria (74.7% vs. 82.1%; OR 0.64, 95% CI 0.49-0.85) than males. Statin treatment was less frequently started (19.7% vs. 24.7%; OR 0.75, 95% CI 0.58-0.96) and prescribed (58.7% vs. 63.9%; OR 0.80, 95% CI 0.73-0.89) in females. There were no differences in starting and intensifying glucose-, blood pressure- and albuminuria-lowering medication. CONCLUSIONS: Sex disparities in medication prescribing amongst T2DM patients were seen, including less starting with statins and potential undertreatment with RAAS-i in females. Such disparities may partly explain higher excess risks for cardiovascular and renal complications associated with diabetes observed in females.

Sex disparities in treatment patterns after metformin initiation among patients with type 2 diabetes mellitus.

PURPOSE: To assess sex differences in treatment patterns after metformin initiation among type 2 diabetes mellitus (T2D) patients. METHODS: A cohort study was conducted using the Groningen Initiative to ANalyze Type 2 diabetes Treatment (GIANTT) primary care database. Patients aged ≥18 years initiating metformin were followed 2-5 years. Markov modeling was conducted to estimate treatment transition rates and calculate adjusted hazard ratios (aHR) with 95% confidence intervals (CI) comparing men with women adjusted for age, HbA1c level at initiation, and cardiovascular disease history. Kaplan-Meier analyses and Cox proportional-hazards models were used to determine the time to and likelihood of getting treatment intensification. HbA1c levels at initiation and intensification were compared using Mann-Whitney U tests. RESULTS: In total, 11 508 metformin initiators were included (50.1% women). The most common transition after initiation was a dose increase (probability women 0.52, men 0.59, no significant difference). Women were more likely than men to switch to any other non-insulin hypoglycemic agent after initiation (aHR 1.66; 95% CI 1.31-2.12), after dose increase (aHR 1.48; 95% CI 1.10-1.98) and after dose decrease (aHR 2.64; 95% CI 1.28-5.46). Time to intensification was longer, time to switching was shorter, and HbA1c levels at initiation and intensification were lower for women than men. CONCLUSIONS: Sex disparities were observed in treatment transitions after metformin initiation. Women more often switched treatment than men, which suggest that prescribers acknowledge more tolerance or other problems for metformin in women. Men intensified treatment earlier and at higher HbA1c levels, indicative of a higher need for treatment intensification.

Attitudes towards deprescribing and patient-related factors associated with willingness to stop medication among older patients with type 2 diabetes (T2D) in Indonesia: a cross-sectional survey study.

BACKGROUND: Deprescribing of preventive medication is recommended in older patients with polypharmacy, including people with type 2 diabetes (T2D). It seems that many patients in low-middle-income countries are not willing to have their medicines deprescribed. This study aims to assess attitudes of Indonesian patients with T2D towards deprescribing in general and regarding specific cardiometabolic medicines, and factors influencing their willingness to stop medicines. METHODS: Primary care patients with T2D of ≥60 years in Indonesia completed the revised Patients' Attitudes Towards Deprescribing (rPATD) questionnaire. Attitudes in general and for cardiometabolic medicines were reported descriptively. Proportions of patients willing to stop one or more medicines when recommended by different healthcare professionals were compared with Chi-square test. Multiple regression analysis was used to analyse the influence between patient-related factors and the willingness to stop medicines. RESULTS: The survey was completed by 196 participants (median age 69 years, 73% female). The percentages willing to stop medicines were 69, 67, and 41%, when the general practitioner (GP), the specialist, or the pharmacist initiates the process (p-value < 0.001). Higher perceived burden of medicines (p-value = 0.03) and less concerns about stopping (p-value < 0.001) were associated with a higher willingness to stop medicines if proposed by the GP. Patients using multiple glucose-regulating medicines were less willing to stop (p-value = 0.02). Using complementary or alternative medicines was not associated with the willingness to stop. If proposed by their pharmacist, patients without substantial education were more willing to stop than educated patients. CONCLUSIONS: Only two-thirds of older people with T2D in Indonesia were willing to stop one or more of their medicines if the GP or specialist recommended this, and even less when the pharmacist proposed this. Attention should be given to concerns about stopping specific medicines, especially among patients using multiple glucose-lowering medicines, who may be more eligible but were less willing to accept deprescribing.

Identifying patients at increased risk of hypoglycaemia in primary care: Development of a machine learning-based screening tool.

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.

Precision medicine approaches for diabetic kidney disease: opportunities and challenges.

The prevalence of end-stage kidney disease (ESKD) continuously increases worldwide. The increasing prevalence parallels the growth in the number of people with diabetes, which is the leading cause of ESKD. Early diagnosis of chronic kidney disease (CKD) in patients with diabetes and appropriate intervention is important to delay the progression of kidney function decline and prevent ESKD. Rate of CKD progression and response to treatment varies among patients with diabetes, highlighting the need to tailor individual treatment. In this review, we describe recent advances and areas for future studies with respect to precision medicine in diabetic kidney disease (DKD). DKD is a multi-factorial disease that is subject in part to genetic heritability, but is also influenced by various exogenous mediators, such as environmental or dietary factors. Genetic testing so far has limited utility to facilitate early diagnosis, classify progression or evaluate response to therapy. Various biomarker-based approaches are currently explored to identify patients at high risk of ESKD and to facilitate decision-making for targeted therapy. These studies have led to discovery and validation of a couple of inflammatory proteins such as circulating tumour necrosis factor receptors, which are strong predictors of kidney disease progression. Moreover, risk and drug-response scores based on multiple biomarkers are developed to predict kidney disease progression and long-term drug efficacy. These findings, if implemented in clinical practice, will pave the way to move from a one-size-fits-all to a one-fit-for-everyone approach.

Effectiveness of a targeted and tailored pharmacist-led intervention to improve adherence to antihypertensive drugs among patients with type 2 diabetes in Indonesia: A cluster randomised controlled trial.

AIM: To assess the effects of a targeted and tailored pharmacist-led intervention among patients with type 2 diabetes (T2DM) who are nonadherent 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 nonadherence 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 (eg, 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-month 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, and 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 nonadherent T2DM patients resulted in an improvement in medication adherence to antihypertensive drugs. There were no significant changes in secondary outcomes.

Trends in polypharmacy and potentially inappropriate medication (PIM) in older and middle-aged people treated for diabetes.

AIMS: Polypharmacy is common in people with diabetes and is associated with the use of potentially inappropriate medication (PIM). This study aimed to assess trends in the prevalence of polypharmacy and PIM in older and middle-aged people with diabetes. METHODS: A repeated cross-sectional study using the University Groningen IADB.nl prescription database was conducted. All people aged 45 years and over who were treated for diabetes registered in the period 2012-2016 were included. Polypharmacy was assessed for three age groups. PIMs were assessed using Beers criteria for people ≥65 years old, and PRescribing Optimally in Middle-aged People's Treatments (PROMPT) criteria for 45-64 years old. Chi-square tests and regression analysis were applied. RESULTS: The prevalence of polypharmacy increased significantly in all age groups in the study period. In 2016, the prevalence of polypharmacy was 36.9% in patients aged 45-54 years, 50.3% in those aged 55-64 years, and 66.2% in those aged ≥65 years. The prevalence of older people with at least one PIM decreased by 3.1%, while in the middle-aged group this prevalence increased by 0.9% from 2012 to 2016. The most common PIMs in both age groups were the use of long-term high-dose proton pump inhibitors, benzodiazepines and strong opioids without laxatives. Of those, only benzodiazepines showed a decreasing trend. CONCLUSIONS: Polypharmacy increased in older and middle-aged people with diabetes. While the prevalence of PIM decreased over time in older age, this trend was not observed in middle-aged people with diabetes. Efforts are needed to decrease the use of PIMs in populations already burdened with many drugs, notably at middle age.

Trends in HbA1c thresholds for initiation of hypoglycemic agents: Impact of changed recommendations for older and frail patients.

AIMS: Less strict glycated hemoglobin (HbA1c ) thresholds have been recommended in older and/or frail type 2 diabetes (T2D) patients than in younger and less frail patients for initiating hypoglycemic agents since 2011. We aimed to assess trends in HbA1c thresholds at initiation of a first hypoglycemic agent(s) in T2D patients and the influence of age and frailty on these trends. MATERIALS AND METHODS: The groningen initiative to analyze type 2 diabetes treatment (GIANTT) database was used, which includes primary care T2D patients from the north of the Netherlands. Patients initiating a first non-insulin hypoglycemic agent(s) between 2008 and 2014 with an HbA1c measurement within 120 days before initiation were included. The influence of calendar year, age, or frailty and the interaction between calendar year and age or frailty were assessed using multilevel regression analyses adjusted for confounders. RESULTS: We included 4588 patients. The mean HbA1c threshold at treatment initiation was 7.4% up to 2010, decreasing to 7.1% in 2011 and increasing to 7.4% in 2014. This quadratic change over the years was significant (P < 0.001). Patients aged 60 to 79 initiated treatments at lower HbA1c and patients of different frailty at similar HbA1c levels. The interaction between year and age or frailty was not significant (P > 0.05). CONCLUSIONS: HbA1c thresholds at initiation of a first hypoglycemic agent(s) changed significantly over time, showing a decrease after 2010 and an increase after 2012. The HbA1c threshold at initiation was not influenced by age or frailty, which is in contrast with recommendations for more personalized treatment.

Older people's attitudes towards deprescribing cardiometabolic medication.

BACKGROUND: Overtreatment with cardiometabolic medication in older patients can lead to major adverse events. Timely deprescribing of these medications is therefore essential. Self-reported willingness to stop medication is usually high among older people, still overtreatment with cardiometabolic medication is common and deprescribing is rarely initiated. An important barrier for deprescribing reported by general practitioners is the patients' unwillingness to stop the medication. More insights are needed into the influence of patients' characteristics on their attitudes towards deprescribing and differences in these attitudes between cardiometabolic medication groups. METHODS: A survey in older people using cardiometabolic medication using the revised Patients' Attitudes Towards Deprescribing (rPATD) questionnaire was performed. Participants completed the general rPATD and an adapted version for four medication groups. Linear and ordinal logistic regression were used to assess the influence of age, sex, therapeutic area and number of medications used on the patients' general attitudes towards deprescribing. Univariate analysis was used to compare differences in deprescribing attitudes towards sulfonylureas, insulins, antihypertensive medication and statins. RESULTS: Overall, 314 out of 1143 invited participants completed the survey (median age 76 years, 54% female). Most participants (80%) were satisfied with their medication and willing to stop medications if their doctor said it was possible (88%). Age, sex and therapeutic area had no influence on the general attitudes towards deprescribing. Taking more than ten medicines was significantly associated with a higher perceived medication burden. Antihypertensive medication and insulin were considered more appropriate than statins, and insulin was considered more appropriate than sulfonylureas not favouring deprescribing. CONCLUSIONS: The majority of older people using cardiometabolic medication are willing to stop one of their medicines if their doctor said it was possible. Health care providers should take into account that patients perceive some of their medication as more appropriate than other medication when discussing deprescribing.

Type 2 diabetes patients' views on prevention of hypoglycaemia - a mixed methods study investigating self-management issues and self-identified causes of hypoglycaemia.

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.

Modifiable Factors Associated with Non-adherence to Antihypertensive or Antihyperlipidemic Drugs Are Dissimilar: a Multicenter Study Among Patients with Diabetes in Indonesia.

BACKGROUND: To develop targeted and tailored interventions for addressing medication non-adherence, it is important to identify underlying factors. OBJECTIVE: To 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. DESIGN: An observational multicenter cross-sectional survey. PARTICIPANTS: Patients with type 2 diabetes using either antihypertensive or antihyperlipidemic drugs in four regions in Indonesia. MAIN MEASURES: Non-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 RESULTS: Of 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. CONCLUSIONS: The 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.

No significant association of type 2 diabetes-related genetic risk scores with glycated haemoglobin levels after initiating metformin or sulphonylurea derivatives.

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, ß-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), ß-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.

Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms.

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.

Adverse drug event patterns experienced by patients with diabetes: A diary study in primary care.

PURPOSE: Little is known about adverse drug events (ADEs) experienced over time during chronic drug use. The purpose of this study was to assess ADE patterns experienced by patients with diabetes. METHODS: Patients who received an oral glucose-lowering drug completed a daily diary for 13 weeks. The diary asked for experienced symptoms and whether patients related these symptoms to any drug they used. Summaries of Product Characteristics were used to check whether the ADEs were known adverse drug reactions (ADRs) of the drugs used. Patterns of weekly occurring ADEs were assessed with descriptive statistics. RESULTS: We included 78 patients. Almost half of them reported at least one ADE (N = 36; 46%). In total, 80 ADEs were reported. Of these ADEs, 71 (90%) were known ADRs. ADEs lasted less than 1 week in 27 cases (34%) and between 2 and 12 weeks in 15 cases (19%). The remaining ADEs fluctuated (16 cases; 20%) or persisted (22 cases; 28%) during the entire study period. CONCLUSIONS: ADEs experienced by patients with diabetes can fluctuate or persist over long periods of drug use.

HbA1c response after insulin initiation in patients with type 2 diabetes mellitus in real life practice: Identifying distinct subgroups.

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.

Individual variability in response to renin angiotensin aldosterone system inhibition predicts cardiovascular outcome in patients with type 2 diabetes: A primary care cohort study.

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.

Views of general practice staff about the use of a patient-oriented treatment decision aid in shared decision making for patients with type 2 diabetes: A mixed-methods study.

BACKGROUND: Decision aids can be used to support shared decision making (SDM). A patient-oriented treatment decision aid (DA) was developed for type 2 diabetes but its use by general practice staff appeared to be limited. OBJECTIVES: To explore views of practice staff towards SDM and the DA. DESIGN: A mixed-methods study within the Dutch PORTDA-diab trial. SETTING AND PARTICIPANTS: Included were 17 practices with staff members who were responsible for routine diabetes care and had worked with the DA, and 209 of their patients. METHODS: Interviews were conducted focusing on applicability, usefulness and feasibility of the DA. Interviews were tape-recorded, transcribed verbatim and subjected to content analysis for identifying and classifying views. Patient-reported data about the use of the DA were collected. Associations between specific views and use of the DA were tested using Pearson point-biserial correlation. RESULTS: The majority of practice staff expressed positive views towards SDM, which was associated with making more use of the DA. Most of the staff expressed that the DA stimulated a two-way conversation. By using the DA, several became aware of their paternalistic approach. Some staff experienced a conflict with the content of the DA, which was associated with making less use of the DA. CONCLUSIONS: The DA was considered useful by practice staff to support SDM. A positive view towards SDM was a facilitator, whereas experiencing a conflict with the content of the DA was a barrier for making use of the DA.