Grigory Sidorenkov

Review: Relation Between Quality-of-Care Indicators for Diabetes and Patient Outcomes: A Systematic Literature Review

The authors conducted a systematic literature review to assess whether quality indicators for diabetes care are related to patient outcomes. Twenty-four studies were included that formally tested this relationship. Quality indicators focusing on structure or processes of care were included. Descriptive analyses were conducted on the associations found, differentiating for study quality and level of analysis. Structure indicators were mostly tested in studies with weak designs, showing no associations with surrogate outcomes or mixed results. Process indicators focusing on intensification of drug treatment were significantly associated with better surrogate outcomes in three high-quality studies. Process indicators measuring numbers of tests or visits conducted showed mostly negative results in four high-quality studies on surrogate and hard outcomes. Studies performed on different levels of analysis and studies of lower quality gave similar results. For many widely used quality indicators, there is insufficient evidence that they are predictive of better patient outcomes.

A longitudinal study examining adherence to guidelines in diabetes care according to different definitions of adequacy and timeliness

Background Performance indicators assessing quality of diabetes care often look at single processes, e.g. whether an HbA1c test was conducted. Adequate care, however, consists of consecutive processes which should be taken in time (clinical pathways). We assessed quality of diabetes care by looking at single processes versus clinical pathways. In addition, we evaluated the impact of time period definitions on this quality assessment. Methodology We conducted a cohort study in 2007–2008 using the GIANTT (Groningen Initiative to Analyse type 2 diabetes Treatment) database. Proportions of patients adequately managed for HbA1c, systolic blood pressure (SBP), LDL-cholesterol (LDL-C), and albumin/creatinin ratio (ACR) were calculated for the pathway of (1) risk factor level testing, (2) treatment intensification when indicated, (3) response to treatment evaluation. Strict and wide time periods for each step were defined. Proportions of patients adequately managed regarding the overall pathway and single steps, using strict or wide time periods were compared using odds ratios (OR) with 95% confidence intervals. Findings Of 11176 patients diagnosed with type 2 diabetes, 9439 with complete follow-up were included. The majority received annual examination of HbA1c (86%) and SBP (86%), whereas this was 67% for LDL-C and 49% for ACR. Adequate management regarding the three-step pathway was observed in 73%, 53%, 46%, 41% of patients for HbA1c, SBP, LDL-C, and ACR respectively. Quality scores reduced significantly due to the second step (OR 0.43, 0.18, 0.44, 0.74), but were not much further reduced by the third step. Timely treatment evaluation occurred in 88% for HbA1c, 87% for SBP, 83% for LDL-C, and 76% for ACR. The overall score was not significantly changed by using strict time windows. Conclusion Quality estimates of glycemic, blood pressure and cholesterol management are substantially reduced when looking at clinical pathways as compared to estimates based on commonly used simple process measures.

Safety of ACE inhibitor therapies in patients with chronic kidney disease

Introduction: ACE inhibitors are first-line therapy in patients with chronic kidney disease (CKD). The main adverse effects of ACE inhibitors are hypotension, renal function impairment and hyperkalemia. Areas covered: This paper reviews evidence from clinical studies regarding adverse effects of ACE inhibitors in patients with CKD. The safety aspects of ACE inhibitors are discussed in relation to their pharmacological action, drug–drug interactions, drug–diet interaction, precautions needed in certain clinical conditions and other adverse effects. Expert opinion: The main adverse effects of ACE inhibitors follow from their interaction with renin-angiotensin-aldosterone system (RAAS)-activity and volume depletion. This interaction can be turned into clinical benefit and increase efficacy of ACE inhibitors by reduction in dietary sodium or adding diuretics. Dual RAAS-blockade is no longer advocated in patients with CKD because of the safety issues, and combination of ACE inhibitors with moderate reduction in dietary sodium intake is a better alternative. The intensified treatment regimens based on ACE inhibitors can potentially improve renoprotection, but increase the risk of adverse effects. Better strategies to address safety concerns are needed. Introduction of clinical rules and safety indicators may help clinicians to identify hazardous co-prescriptions and adverse dietary habits and can decrease the frequency of adverse effects.

Association Between Performance Measures and Glycemic Control Among Patients With Diabetes in a Community-wide Primary Care Cohort

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.

Treatment quality indicators predict short-term outcomes in patients with diabetes: a prospective cohort study using the GIANTT database

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.

Development and validation of prescribing quality indicators for patients with type 2 diabetes

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.

Prescribing Quality and Prediction of Clinical Outcomes in Patients With Type 2 Diabetes: A Prospective Cohort Study

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 …

Role of Patient and Practice Characteristics in Variance of Treatment Quality in Type 2 Diabetes between General Practices

Background Accounting 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. Methods The 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. Results Treatment 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 ACE-inhibitor/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. Discussion Considerable 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.

Is guideline-adherent prescribing associated with quality of life in patients with type 2 diabetes?

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.

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

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.