The impact of the covid-19 pandemic on diabetes care: the perspective of healthcare providers across Europe.

AIMS: Covid-19 caused changes on the delivery of diabetes care. This study aimed to explore perceptions of healthcare providers across Europe concerning 1) the impact of covid-19 on delivery of diabetes care; 2) impact of changes in diabetes care on experienced workload; 3) experiences with video consultation in diabetes care. METHODS: Cross-sectional survey among healthcare providers in the Netherlands, United Kingdom, Turkey, Ukraine and Sweden, with a focus on primary care. RESULTS: The survey was completed by 180 healthcare providers. During the COVID-19 pandemic 57.1% of respondents provided less diabetes care and 72.8% observed a negative impact on people with diabetes. More than half of respondents (61.9%) expressed worries to some extent about getting overloaded by work. Although the vast majority considered their work meaningful (85.6%). Almost half of healthcare providers (49.4%) thought that after the pandemic video-consultation could be blended with face-to-face contact. CONCLUSIONS: Less diabetes care was delivered and a negative impact on people with diabetes was observed by healthcare providers. Despite healthcare providers' feeling overloaded, mental wellbeing seemed unaffected. Video consultations were seen as having potential. Given the remaining covid-19 risks and from the interest of proactive management of people with diabetes, these findings urge for further exploration of incorporating video consultation in diabetes care.

Patient-reported outcomes after 10-year follow-up of intensive, multifactorial treatment in individuals with screen-detected type 2 diabetes: the ADDITION-Europe trial.

AIMS: To present the longer-term impact of multifactorial treatment of type 2 diabetes on self-reported health status, diabetes-specific quality of life, and diabetes treatment satisfaction at 10-year follow up of the ADDITION-Europe trial. METHODS: The ADDITION-Europe trial enrolled 3057 individuals with screen-detected type 2 diabetes from four centres [Denmark, the UK (Cambridge and Leicester) and the Netherlands], between 2001 and 2006. Participants were randomized at general practice level to intensive treatment or to routine care . The trial ended in 2009 and a 10-year follow-up was performed at the end of 2014. We measured self-reported health status (36-item Short-Form Health Survey and EQ-5D), diabetes-specific quality of life (Audit of Diabetes-Dependent Quality of Life questionnaire), and diabetes treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire) at different time points during the study period. A mixed-effects model was applied to estimate the effect of intensive treatment (intention-to-treat analyses) on patient-reported outcome measures for each centre. Centre-specific estimates were pooled using a fixed effects meta-analysis. RESULTS: There was no difference in patient-reported outcome measures between the routine care and intensive treatment arms in this 10-year follow-up study [EQ-5D: -0.01 (95% CI -0.03, 0.01); Physical Composite Score (36-item Short-Form Health Survey): -0.27 (95% CI -1.11, 0.57), Audit of Diabetes-Dependent Quality of Life questionnaire: -0.01 (95% CI -0.11, 0.10); and Diabetes Treatment Satisfaction Questionnaire: -0.20 (95% CI -0.70, 0.29)]. CONCLUSIONS: Intensive, multifactorial treatment of individuals with screen-detected type 2 diabetes did not affect self-reported health status, diabetes-specific quality of life, or diabetes treatment satisfaction at 10-year follow-up compared to routine care.

Factors that influence the intended intensity of diabetes care in a person-centred setting.

AIMS: To assess the intended intensity of Type 2 diabetes care and the factors associated with that intensity of care after the annual monitoring visit in which a new person-centred diabetes consultation model including shared decision making was used. METHODS: We conducted an observational study in 1284 people from 47 general practices and six hospital outpatient clinics. Intensity of care (more, no/minimal change, less) was based on monitoring frequency and referral to other care providers. We used multivariable analyses to determine the factors that were independently associated with intensity of care. Care providers also reported three factors which, in their opinion, determined the intensity of care. RESULTS: After the consultation, 22.8% of people chose more intensive care, 70.6% chose no/minimal change and 6.6% chose less intensive care. Whether care became more intensive vs not/minimally changed was associated with a high educational level (odds ratio 1.65, CI 1.07 to 2.53; P=0.023), concern about illness (odds ratio 1.08; CI 1.00 to 1.17; P=0.045), goal-setting (odds ratio 6.53, CI 3.79 to 11.27; P<0.001), comorbidities (odds ratio 1.12, CI 1.00 to 1.24; P=0.041) and use of oral blood glucose lowering medication (odds ratio 0.59, CI 0.39 to 0.89; P=0.011). Less intensive care vs no/minimal change was associated with lower diabetes distress levels (odds ratio 0.87, CI 0.79 to 0.97; P=0.009). According to care providers, quality of life, lifestyle, person's preferences and motivation, glycaemic control, and self-management possibilities most frequently determined the intended care. CONCLUSIONS: In person-centred diabetes care, the intended intensity of care was associated with both disease- and person-related factors.

Theory-based diabetes self-management education with pre-selection of participants: a randomized controlled trial with 2.5 years' follow-up (ELDES Study).

AIMS: To evaluate the (cost-)effectiveness of Beyond Good Intentions (BGI), a 12-week group-based, nurse-led self-management programme, in terms of cardiovascular risk factors, self-management and quality of life, after 2.5 years of follow-up in pre-selected individuals with known Type 2 diabetes of up to 5 years' duration. METHODS: A parallel randomized controlled trial comparing BGI with usual care, based on a self-management screening questionnaire, was conducted in 43 general practices after pre-selection of participants. After 2.5 years of follow-up, the between-group changes in the abovementioned variables were assessed using analysis of covariance. RESULTS: A total of 108 participants (BGI group, n =56; control group, n =52) were included. Changes over time in BMI (-0.4 vs -0.5 kg/m2 ) were similar in the two groups. Median HbA1c [BGI group 47 mmol/mol (6.5%); control group: 49 mmol/mol (6.6%)] and mean systolic blood pressure (BGI group: 132±13 mmHg; control group: 133±14 mmHg) were well controlled at baseline and no intervention effect was found. LDL cholesterol levels decreased from 2.4 to 2.2 mmol/l in the control group and remained stable at 2.6 mmol/l in the intervention group (P=0.032). No intervention effect was found for self-management or quality of life. CONCLUSION: In contrast to the first BGI study, we did not observe significant effects of the BGI intervention, despite pre-selection of individuals. In diabetes populations with target levels for HbA1c , systolic blood pressure and LDL cholesterol, no further beneficial effects can be expected from self-management programmes with regard to biomedical factors and quality of life.

The effectiveness of an emotion-focused educational programme in reducing diabetes distress in adults with Type 2 diabetes mellitus (VEMOFIT): a cluster randomized controlled trial.

AIMS: To evaluate the effectiveness of a brief, value-based emotion-focused educational programme (VEMOFIT) in Malay adults with Type 2 diabetes mellitus compared with a programme of active listening to participants' emotional experiences, social support and their opinion on the health clinic diabetes care services (attention control). METHODS: Malay adults with severe diabetes distress [Diabetes Distress Scale (DDS-17) mean score ≥ 3] were included. VEMOFIT consisted of four biweekly group sessions, a booster session after 3 months and a follow-up 6 months post intervention. The attention control programme consisted of three sessions over the same period. Outcomes included diabetes distress, depressive symptoms, self-efficacy and disease control. Required total sample size was 165. RESULTS: Participants (n = 124) were randomized to either VEMOFIT (n = 53) or the attention control programme (n = 71). Participants had a mean (sd) age of 55.7 (9.7) years, median diabetes duration of 7.0 (8.0) years and mean HbA1c level of 82 mmol/mol (9.7%). The mean DDS-17 level decreased significantly in both the VEMOFIT and the attention control programmes (3.4 to 2.9 vs. 3.1 to 2.7, respectively). The adjusted between-group DDS-17 difference was not significant [-0.01, 95% confidence interval (CI) -0.38, 0.35]. The proportion of individuals with severe diabetes distress decreased in both groups, from 89% to 47% vs. 69% to 39% (odds ratio 0.88; 95% CI 0.26, 2.90). Other outcomes did not differ between groups. CONCLUSIONS: Both interventions decreased diabetes distress significantly. The theory-based VEMOFIT programme was not superior to the attention control programme. The latter approach is a simpler way to decrease severe diabetes distress (Trial registration: NCT02730078; NMRR-15-1144-24803).

[From annual check-up to annual appraisal: personalised diabetes care]. / Van jaarcontrole naar jaargesprek.

Diabetes care is shifting from disease management to personalised care. Internationally, diabetes care providers are advised to integrate the patient's preferences, wishes and possibilities into diabetes care in order to improve its efficiency. The Dutch Diabetes Federation has developed a specifically patient-centred conversation model that can be systematically applied. At an annual appraisal, the physician and the patient make decisions on the treatment goals to set, and on the treatment and professional support needed to achieve these goals. In this way person-centred and efficient care may become reality. The first results of a pilot study are promising. Currently the applicability and added value of the model are being tested on a large scale. The model is more broadly applicable, which means this could be a new perspective for everyone with a chronic disease.

Case-finding for cognitive impairment among people with Type 2 diabetes in primary care using the Test Your Memory and Self-Administered Gerocognitive Examination questionnaires: the Cog-ID study.

AIM: To evaluate two cognitive tests for case-finding for cognitive impairment in older patients with Type 2 diabetes. METHODS: Of 1243 invited patients with Type 2 diabetes, aged ≥70 years, 228 participated in a prospective cohort study. Exclusion criteria were: diagnosis of dementia; previous investigation at a memory clinic; and inability to write or read. Patients first filled out two self-administered cognitive tests (Test Your Memory and Self-Administered Gerocognitive Examination). Secondly, a general practitioner, blinded to Test Your Memory and Self-Administered Gerocognitive Examination scores, performed a structured evaluation using the Mini-Mental State Examination. Subsequently, patients suspected of cognitive impairment (on either the cognitive tests or general practitioner evaluation) and a random sample of 30% of patients not suspected of cognitive impairment were evaluated at a memory clinic. Diagnostic accuracy and area under the curve were determined for the Test Your Memory, Self-Administered Gerocognitive Examination and general practitioner evaluation compared with a memory clinic evaluation to detect cognitive impairment (mild cognitive impairment or dementia). RESULTS: A total of 44 participants were diagnosed with cognitive impairment. The Test Your Memory and Self-Administered Gerocognitive Examination questionnaires had negative predictive values of 81 and 85%, respectively. Positive predictive values were 39 and 40%, respectively. The general practitioner evaluation had a negative predictive value of 83% and positive predictive value of 64%. The area under the curve was ~0.70 for all tests. CONCLUSIONS: Both the tests evaluated in the present study can easily be used in case-finding strategies for cognitive impairment in patients with Type 2 diabetes in primary care. The Self-Administered Gerocognitive Examination had the best diagnostic accuracy and therefore we would have a slight preference for this test. Applying the Self-Administered Gerocognitive Examination would considerably reduce the number of patients in whom the general practitioner needs to evaluate cognitive functioning to tailor diabetes treatment.

Effectiveness of tailored support for people with Type 2 diabetes after a first acute coronary event: a multicentre randomized controlled trial (the Diacourse-ACE study).

AIMS: To evaluate the effectiveness of a tailored, supportive intervention strategy in influencing diabetes-related distress, health status, well-being and clinical outcomes in people with Type 2 diabetes shortly after a first acute coronary event. METHODS: People with Type 2 diabetes and a recent first acute coronary event (n = 201) were randomized to the intervention group (three home visits by a diabetes nurse) or the attention control group (one telephone consultation). Outcomes were measured after discharge (baseline) and at 5 months (follow-up) using validated questionnaires for diabetes-related distress (Problem Areas in Diabetes), well-being (WHO Well-Being Index) and health status (Euroqol 5 Dimensions; Euroqol Visual Analogue Scale). ancova was used to analyse change-over-time differences between groups. RESULTS: Follow-up data were available for 81 participants in the intervention group (66.0 ± 9.3 years, 76% male) and 80 in the control group (65.6 ± 9.4 years, 75% male) participants. Mean diabetes-related distress was low after hospital discharge (intervention group: 8.2 ± 10.1; control group: 9.2 ± 12.4) and did not change after 5 months (intervention group: 9.2 ± 12.4; control group: 9.0 ± 11.2). Baseline well-being was less favourable but improved significantly in the intervention group (baseline: 58.5 ± 28.0; follow-up: 65.5 ± 23.7; P = 0.005), but not in the control group (baseline: 57.5 ± 25.2; follow-up: 59.6 ± 24.4; P = 0.481). Health status also improved in the intervention group (baseline: 69.9 ± 17.3; follow-up: 76.8 ± 15.6; P < 0.001) but not in the control group (baseline: 68.6 ± 15.9; follow-up: 69.9 ± 16.7; P = 0.470). A significant group effect was found for health status (F = 7.9; P = 0.006). CONCLUSIONS: Although the intervention had no effect on diabetes-related distress, this might be at least partially attributable to very low levels of diabetes-related distress at baseline. Interestingly, health status scores and well-being, which were less favourable at baseline, both improved after the tailored support intervention.

Diabetes-related distress over the course of illness: results from the Diacourse study.

AIMS: To investigate the relationship between diabetes duration and diabetes-related distress and to examine the impact of micro- and macrovascular complications and blood glucose-lowering treatment on this relationship. METHODS: We conducted a cross-sectional study in people with Type 2 diabetes who participated in the Dutch Diacourse study (n = 590) and completed the Problem Areas in Diabetes questionnaire. Data on diabetes duration, micro- and macrovascular complications and blood glucose-lowering treatment were collected. Multiple linear regression analysis was used to investigate the association between diabetes duration and diabetes-related distress, and to examine whether complications and treatment could explain this association. RESULTS: A significant linear and quadratic association between diabetes duration and diabetes-related distress was found (duration: ß = 0.27, P = 0.005; duration(2): ß = -0.21, P = 0.030). The association between duration and distress could be explained by microvascular complications and insulin treatment, which were both more often present in people with a longer diabetes duration, and were associated with higher levels of diabetes-related distress (ß = 0.20, P < 0.001 and ß = 0.16, P = 0.006 respectively). Duration, age, gender, complications and treatment together explained 13.1% of the variance in distress. CONCLUSIONS: Diabetes duration was associated with diabetes-related distress. This association can be explained largely by the presence of diabetes-related microvascular complications and insulin treatment. Healthcare providers should focus on distress in people with Type 2 diabetes in different stages over the course of illness, especially when complications are present or when people are on insulin treatment. As well as diabetes duration, complications and blood glucose-lowering treatment, diabetes-related distress is likely to be influenced by many other factors.

Cost-effectiveness of intensive multifactorial treatment compared with routine care for individuals with screen-detected Type 2 diabetes: analysis of the ADDITION-UK cluster-randomized controlled trial.

AIMS: To examine the short- and long-term cost-effectiveness of intensive multifactorial treatment compared with routine care among people with screen-detected Type 2 diabetes. METHODS: Cost-utility analysis in ADDITION-UK, a cluster-randomized controlled trial of early intensive treatment in people with screen-detected diabetes in 69 UK general practices. Unit treatment costs and utility decrement data were taken from published literature. Accumulated costs and quality-adjusted life years (QALYs) were calculated using ADDITION-UK data from 1 to 5 years (short-term analysis, n = 1024); trial data were extrapolated to 30 years using the UKPDS outcomes model (version 1.3) (long-term analysis; n = 999). All costs were transformed to the UK 2009/10 price level. RESULTS: Adjusted incremental costs to the NHS were £285, £935, £1190 and £1745 over a 1-, 5-, 10- and 30-year time horizon, respectively (discounted at 3.5%). Adjusted incremental QALYs were 0.0000, - 0.0040, 0.0140 and 0.0465 over the same time horizons. Point estimate incremental cost-effectiveness ratios (ICERs) suggested that the intervention was not cost-effective although the ratio improved over time: the ICER over 10 years was £82,250, falling to £37,500 over 30 years. The ICER fell below £30 000 only when the intervention cost was below £631 per patient: we estimated the cost at £981. CONCLUSION: Given conventional thresholds of cost-effectiveness, the intensive treatment delivered in ADDITION was not cost-effective compared with routine care for individuals with screen-detected diabetes in the UK. The intervention may be cost-effective if it can be delivered at reduced cost.

The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus.

OBJECTIVE: Various cardiovascular prediction models have been developed for patients with type 2 diabetes. Their predictive performance in new patients is mostly not investigated. This study aims to quantify the predictive performance of all cardiovascular prediction models developed specifically for diabetes patients. DESIGN AND METHODS: Follow-up data of 453, 1174 and 584 type 2 diabetes patients without pre-existing cardiovascular disease (CVD) in the EPIC-NL, EPIC-Potsdam and Secondary Manifestations of ARTerial disease cohorts, respectively, were used to validate 10 prediction models to estimate risk of CVD or coronary heart disease (CHD). Discrimination was assessed by the c-statistic for time-to-event data. Calibration was assessed by calibration plots, the Hosmer-Lemeshow goodness-of-fit statistic and expected to observed ratios. RESULTS: There was a large variation in performance of CVD and CHD scores between different cohorts. Discrimination was moderate for all 10 prediction models, with c-statistics ranging from 0.54 (95% CI 0.46 to 0.63) to 0.76 (95% CI 0.67 to 0.84). Calibration of the original models was poor. After simple recalibration to the disease incidence of the target populations, predicted and observed risks were close. Expected to observed ratios of the recalibrated models ranged from 1.06 (95% CI 0.81 to 1.40) to 1.55 (95% CI 0.95 to 2.54), mainly driven by an overestimation of risk in high-risk patients. CONCLUSIONS: All 10 evaluated models had a comparable and moderate discriminative ability. The recalibrated, but not the original, prediction models provided accurate risk estimates. These models can assist clinicians in identifying type 2 diabetes patients who are at low or high risk of developing CVD.

Effectiveness and cost-effectiveness of 3-monthly versus 6-monthly monitoring of well-controlled type 2 diabetes patients: a pragmatic randomised controlled patient-preference equivalence trial in primary care (EFFIMODI study).

AIM: To investigate effectiveness and cost-effectiveness of 6-monthly monitoring compared with 3-monthly monitoring of well-controlled type 2 diabetes patients in primary care. METHODS: A pragmatic randomised controlled patient-preference equivalence trial was performed. From April 2009 to August 2010, 2215 patients from 233 general practitioners across the Netherlands were included. Patients were eligible if between 40- and 80-years-old, diagnosed with type 2 diabetes for more than a year, treated by their general practitioner, not on insulin treatment and well-controlled during the last year (HbA1c ≤ 58 mmol/mol, systolic blood pressure ≤ 145 mmHg and total cholesterol ≤ 5.2 mmol/l). Patients without a strong preference for their monitoring frequency were randomised to 3-monthly or 6-monthly monitoring. Follow-up was 18 months. The primary outcome is the percentage of patients remaining under: HbA1c ≤ 58 mmol/mol, systolic blood pressure ≤ 145 mmHg and total cholesterol ≤ 5.2 mmol/l. Equivalence was assumed if the two-sided 95% confidence interval (CI) was between -5 and 5%. Cost-effectiveness was determined using a cost-minimisation analysis. RESULTS: In the 3-monthly group 69.5% remained under good cardiometabolic control, versus 69.8% in the 6-monthly group (difference: 0.3%; 95%CI: -6.2-6.7%). All secondary outcomes were equivalent for 3-monthly and 6-monthly monitoring, except the systolic blood pressure target, physical activity and antihypertensive drug use. Six-monthly monitoring was €387 (£333) cheaper per patient compared to 3-monthly monitoring during the study period. CONCLUSIONS: Patients with good cardiometabolic control and without preference for their monitoring frequency can visit the primary care physician less often. The cost-savings can be considerable.

Does early intensive multifactorial therapy reduce modelled cardiovascular risk in individuals with screen-detected diabetes? Results from the ADDITION-Europe cluster randomized trial.

AIMS: Little is known about the long-term effects of intensive multifactorial treatment early in the diabetes disease trajectory. In the absence of long-term data on hard outcomes, we described change in 10-year modelled cardiovascular risk in the 5 years following diagnosis, and quantified the impact of intensive treatment on 10-year modelled cardiovascular risk at 5 years. METHODS: In a pragmatic, cluster-randomized, parallel-group trial in Denmark, the Netherlands and the UK, 3057 people with screen-detected Type 2 diabetes were randomized by general practice to receive (1) routine care of diabetes according to national guidelines (1379 patients) or (2) intensive multifactorial target-driven management (1678 patients). Ten-year modelled cardiovascular disease risk was calculated at baseline and 5 years using the UK Prospective Diabetes Study Risk Engine (version 3ß). RESULTS: Among 2101 individuals with complete data at follow up (73.4%), 10-year modelled cardiovascular disease risk was 27.3% (sd 13.9) at baseline and 21.3% (sd 13.8) at 5-year follow-up (intensive treatment group difference -6.9, sd 9.0; routine care group difference -5.0, sd 12.2). Modelled 10-year cardiovascular disease risk was lower in the intensive treatment group compared with the routine care group at 5 years, after adjustment for baseline cardiovascular disease risk and clustering (-2.0; 95% CI -3.1 to -0.9). CONCLUSIONS: Despite increasing age and diabetes duration, there was a decline in modelled cardiovascular disease risk in the 5 years following diagnosis. Compared with routine care, 10-year modelled cardiovascular disease risk was lower in the intensive treatment group at 5 years. Our results suggest that patients benefit from intensive treatment early in the diabetes disease trajectory, where the rate of cardiovascular disease risk progression may be slowed.

Does early intensive multifactorial treatment reduce total cardiovascular burden in individuals with screen-detected diabetes? Findings from the ADDITION-Europe cluster-randomized trial.

AIMS: To describe the total cardiovascular burden (cardiovascular morbidity or mortality, revascularization or non-traumatic amputation) in individuals with screen-detected diabetes in the ADDITION-Europe trial and to quantify the impact of the intervention on multiple cardiovascular events over 5 years. METHODS: In a pragmatic, cluster-randomized, parallel-group trial in four centres (Denmark; Cambridge, UK; the Netherlands; and Leicester, UK), 343 general practices were randomized to screening plus routine care (n = 1379 patients), or screening and promotion of target-driven, intensive treatment of multiple risk factors (n = 1678). We estimated the effect of the intervention on multiple cardiovascular events after diagnosis of diabetes using the Wei, Lin and Weissfeld method. RESULTS: Over 5.3 years, 167 individuals had exactly one cardiovascular event, 53 exactly two events, and 18 three or more events. The incidence rates (95% CI) of first events and any event per 1000 person-years were 14.6 (12.8-16.6) and 20.4 (18.2-22.6), respectively. There were non-significant reductions in the risk of a first (hazard ratio 0.83, 95% CI 0.65-1.05) and second primary endpoint (hazard ratio 0.70, 95% CI 0.43-1.12). The overall average hazard ratio for any event was 0.77 (95% CI 0.58-1.02). CONCLUSIONS: Early intensive multifactorial treatment was not associated with a significant reduction in total cardiovascular burden at 5 years. Focusing on first events in cardiovascular disease prevention trials underestimates the total cardiovascular burden to patients and the health service.

High prevalence of previously unknown heart failure and left ventricular dysfunction in patients with type 2 diabetes.

AIMS/HYPOTHESIS: The aim of this study was to assess the prevalence of (unknown) heart failure and left ventricular dysfunction in older patients with type 2 diabetes. METHODS: In total, 605 patients aged 60 years or over with type 2 diabetes in the south west of the Netherlands participated in this cross-sectional study (response rate 48.7%), including 24 with a cardiologist-confirmed diagnosis of heart failure. Between February 2009 and March 2010, the patients without known heart failure underwent a standardised diagnostic work-up, including medical history, physical examination, ECG and echocardiography. An expert panel used the criteria of the European Society of Cardiology to diagnose heart failure. RESULTS: Of the 581 patients studied, 161 (27.7%; 95% CI 24.1%, 31.4%) were found to have previously unknown heart failure: 28 (4.8%; 95% CI 3.1%, 6.6%) with reduced ejection fraction, and 133 (22.9%; 95% CI 19.5%, 26.3%) with preserved ejection fraction. The prevalence of heart failure increased steeply with age. Heart failure with preserved ejection fraction was more common in women. Left ventricular dysfunction was diagnosed in 150 patients (25.8%; 95% CI 22.3%, 29.4%); 146 (25.1%; 95% CI 21.6%, 28.7%) had diastolic dysfunction. CONCLUSIONS/INTERPRETATION: This is the first epidemiological study that provides exact prevalence estimates of (previously unknown) heart failure and left ventricular dysfunction in a representative sample of patients with type 2 diabetes. Previously unknown heart failure and left ventricular dysfunction are highly prevalent. Physicians should pay special attention to 'unmasking' these patients.

Effectiveness of peer-led self-management coaching for patients recently diagnosed with Type 2 diabetes mellitus in primary care: a randomized controlled trial.

AIMS: To study the effectiveness of a peer-led self-management coaching intervention in recently diagnosed patients with Type 2 diabetes. METHODS: Randomized controlled trial of recently diagnosed patients with Type 2 diabetes from 54 participating general practices. The intervention group received three home visits by an experienced peer (expert patient) who adhered to the recommended treatment and lifestyle guidelines. Together with their expert patient, participants set feasible goals and these were evaluated in the next visit. Participants in the control group received care as usual. At baseline, 3 months and 6 months post-intervention, participants completed a questionnaire measuring changes in self-efficacy, coping, physical activity, dietary habits, psychological well-being, depressive symptoms and diabetes related distress. RESULTS: In total, 327 patients were eligible for inclusion in the study of which 133 consented to participate. In participating patients, self-efficacy, coping and saturated fat intake improved significantly over time. Analyses of participants with low self-efficacy at baseline (25th percentile: 44) revealed a significant time × group difference, F = 3.71; P = 0.03. Participants who reported low psychological well-being at baseline increased substantially throughout the study (F = 23.84; P < 0.01) but no significant time × group differences were found. CONCLUSIONS: A peer-led self-management coaching programme for recently diagnosed patients with Type 2 diabetes improved self-efficacy of patients experiencing low self-efficacy shortly after diagnosis.

Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review.

CONTEXT: A recent overview of all CVD models applicable to diabetes patients is not available. OBJECTIVE: To review the primary prevention studies that focused on the development, validation and impact assessment of a cardiovascular risk model, scores or rules that can be applied to patients with type 2 diabetes. DESIGN: Systematic review. DATA SOURCES: Medline was searched from 1966 to 1 April 2011. STUDY SELECTION: A study was eligible when it described the development, validation or impact assessment of a model that was constructed to predict the occurrence of cardiovascular disease in people with type 2 diabetes, or when the model was designed for use in the general population but included diabetes as a predictor. DATA EXTRACTION: A standardized form was sued to extract all data of the CVD models. RESULTS: 45 prediction models were identified, of which 12 were specifically developed for patients with type 2 diabetes. Only 31% of the risk scores has been externally validated in a diabetes population, with an area under the curve ranging from 0.61 to 0.86 and 0.59 to 0.80 for models developed in a diabetes population and in the general population, respectively. Only one risk score has been studied for its effect on patient management and outcomes. 10% of the risk scores are advocated in national diabetes guidelines. CONCLUSION: Many cardiovascular risk scores are available that can be applied to patients with type 2 diabetes. A minority of these risk scores has been validated and tested for its predictive accuracy, with only a few showing a discriminative value of ≥0.80. The impact of applying these risk scores in clinical practice is almost completely unknown, but their use is recommended in various national guidelines.

Screening for type 2 diabetes. Lessons from the ADDITION-Europe study.

AIMS: To describe and compare attendance rates and the proportions of people identified with Type 2 diabetes mellitus in people with previously unknown diabetes who participated in screening programmes undertaken in general practice in the UK, Denmark and the Netherlands as part of the ADDITION-Europe study. METHODS: In Cambridge, routine computer data searches were conducted to identify individuals aged 40-69 years at high risk of Type 2 diabetes using the Cambridge Diabetes Risk Score. In Denmark, the Danish Diabetes Risk Score was mailed to individuals aged 40-69 years, or completed by patients visiting their general practitice. In the Netherlands, the Hoorn Symptom Risk Questionnaire was mailed to individuals aged 50-69 years. In these three centres, high-risk individuals were invited to attend subsequent steps in the screening programme, including random blood glucose, HbA(1c) , fasting blood glucose and/or oral glucose tolerance test. In Leicester, eligible people aged 40-69 years were invited directly for an oral glucose tolerance test. In all centres, Type 2 diabetes was defined according to World Health Organization 1999 diagnostic criteria. RESULTS: Attendance rates ranged from 20.2% (oral glucose tolerance test in Leicester without pre-stratification) to 95.1% (random blood glucose in opportunistic screening in Denmark in high-risk people). The percentage of people with newly detected Type 2 diabetes from the target population ranged from 0.33% (Leicester) to 1.09% (the Netherlands). CONCLUSIONS: Screening for Type 2 diabetes was acceptable and feasible, but relatively few participants were diagnosed in all participating centres. Different strategies may be required to increase initial attendance and ensure completion of screening programmes.

Non-fasting lipids and risk of cardiovascular disease in patients with diabetes mellitus.

AIMS/HYPOTHESIS: The aim of this study was to examine the effect of postprandial time on the associations and predictive value of non-fasting lipid levels and cardiovascular disease risk in participants with diabetes. METHODS: This study was conducted among 1,337 participants with diabetes from the Dutch and German (Potsdam) contributions to the European Prospective Investigation into Cancer and Nutrition. At baseline, total cholesterol, LDL- and HDL-cholesterol and triacylglycerol concentrations were measured and the ratio of total cholesterol/HDL-cholesterol was calculated. Participants were followed for incidence of cardiovascular disease. RESULTS: Lipid concentrations changed minimally with increasing postprandial time, except for triacylglycerol which was elevated just after a meal and declined over time (1.86 at 0.1 h to 1.33 at >6 h, p for trend <0.001). During a mean follow-up of 8 years, 116 cardiovascular events were documented. After adjustment for potential confounders, triacylglycerol (HR for third tertile compared with first tertile (HR(t)3(to)1), 1.73 [95% CI 1.04, 2.87]), HDL-cholesterol (HR(t)3(to)1, 0.41 [95% CI 0.23, 0.72]) and total cholesterol/HDL-cholesterol ratio (HR(t)3(to)1, 1.65 [95% CI 0.95, 2.85]) were associated with cardiovascular disease, independent of postprandial time. Cardiovascular disease risk prediction using the UK Prospective Diabetes Study risk engine was not affected by postprandial time. CONCLUSIONS/INTERPRETATION: Postprandial time did not affect associations between lipid concentrations and cardiovascular disease risk in patients with diabetes, nor did it influence prediction of cardiovascular disease. Therefore, it may not be necessary to use fasting blood samples to determine lipid concentrations for cardiovascular disease risk prediction in patients with diabetes.

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes.

AIMS/HYPOTHESIS: Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. The methods used in these validation studies were diverse, however, and sometimes insufficient. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes. METHODS: The cohort included 1,622 patients with type 2 diabetes. During a mean follow-up of 8 years, patients were followed for incidence of CHD and cardiovascular disease (CVD). Discrimination and calibration were assessed for 4, 5, 6 and 8 year risk. Discrimination was examined using the c-statistic and calibration by visually inspecting calibration plots and calculating the Hosmer-Lemeshow χ(2) statistic. RESULTS: The UKPDS risk engine showed moderate to poor discrimination for both CHD and CVD (c-statistic of 0.66 for both 5 year CHD and CVD risks), and an overestimation of the risk (224% and 112%). The calibration of the UKPDS risk engine was slightly better for patients with type 2 diabetes who had been diagnosed with diabetes more than 10 years ago compared with patients diagnosed more recently, particularly for 4 and 5 year predicted CVD and CHD risks. Discrimination for these periods was still moderate to poor. CONCLUSIONS/INTERPRETATION: We observed that the UKPDS risk engine overestimates CHD and CVD risk. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be updated.