Is patient readiness to change a predictor of improved glycemic control?

Defining Usual Care in Clinical Trials

Pragmatic trials occur within the complexity of real-world care delivery, and when effective, contribute to more rapid translation into practice because of their greater generalizability. Research with older adults is complex when participants have chronic conditions and multiple comorbidities. Often pragmatic trials introduce a novel intervention and try to determine whether it offers a benefit beyond the usual or routine care provided. Researchers commonly focus attention on describing the intervention, yet the comparator condition of usual or routine care can be anything but standard, reducing the effect size of the intervention and introducing threats to the overall validity of the study. The current article describes clinical trial guidelines, then illustrates the complexity of characterizing usual care for interventions addressing type 2 diabetes. The authors provide recommendations for improving description of usual care and discuss implications for gerontological nursing research. [Research in Gerontological Nursing, 13(3), 125-129.].

Revised 2005 Diabetes Quality Improvement Checklist

The initial 1992 Diabetes Quality Assurance (DQA) Checklist was developed as a tool to facilitate chart auditing for evaluating clinician adherence to the current quality-of-care standards. This article describes how the authors revised and updated the 1992 DQA Checklist to incorporate more recent evidence related to clinical interventions that improve outcome. The revised version is designed to address the 2005 American Diabetes Association Standards for Medical Care. Additional changes were made to facilitate the review process. The revised instrument has been named the Revised 2005 Diabetes Quality Improvement (DQI) Checklist to reflect the emphasis on its potential use in quality improvement activities. The 2005 DQI Checklist is an updated version of the original 1992 DQA Checklist, with additional modifications based on the 2005 American Diabetes Association Standards of Medical Care, the National Cholesterol Education Program Adult Treatment Panel III, the Seventh Joint National Committee on the Prevention, Detection and Treatment of High Blood Pressure Guidelines, and the Centers for Disease Control and Prevention Adult Immunizations Guidelines.

A Multidisciplinary Intervention Utilizing Virtual Communication Tools to Reduce Health Disparities: A Pilot Randomized Controlled Trial

Advances in technology are likely to provide new approaches to address healthcare disparities for high-risk populations. This study explores the feasibility of a new approach to health disparities research using a multidisciplinary intervention and advanced communication technology to improve patient access to care and chronic disease management. A high-risk cohort of uninsured, poorly-controlled diabetic patients was identified then randomized pre-consent with stratification by geographic region to receive either the intervention or usual care. Prior to enrollment, participants were screened for readiness to make a behavioral change. The primary outcome was the feasibility of protocol implementation, and secondary outcomes included the use of patient-centered medical home (PCMH) services and markers of chronic disease control. The intervention included a standardized needs assessment, individualized care plan, intensive management by a multidisciplinary team, including health coach-facilitated virtual visits, and the use of a cloud-based glucose monitoring system. One-hundred twenty-seven high-risk, potentially eligible participants were randomized. Sixty-one met eligibility criteria after an in-depth review. Due to limited resources and time for the pilot, we only attempted to contact 36 participants. Of these, we successfully reached 20 (32%) by phone and conducted a readiness to change screen. Ten participants screened in as ready to change and were enrolled, while the remaining 10 were not ready to change. Eight enrolled participants completed the final three-month follow-up. Intervention feasibility was demonstrated through successful implementation of 13 out of 14 health coach-facilitated virtual visits, and 100% of participants indicated that they would recommend the intervention to a friend. Protocol feasibility was demonstrated as eight of 10 participants completed the entire study protocol. At the end of the three-month intervention, participants had a median of nine total documented contacts with PCMH providers compared to four in the control group. Three intervention and two control participants had controlled diabetes (hemoglobin A1C <9%). Multidisciplinary care that utilizes health coach-facilitated virtual visits is an intervention that could increase access to intensive primary care services in a vulnerable population. The methods tested are feasible and should be tested in a pragmatic randomized controlled trial to evaluate the impact on patient-relevant outcomes across multiple chronic diseases.

Patient explanations for non-attendance at type 2 diabetes self-management education: a qualitative study

AIM

To explore reasons for non-attendance at type 2 diabetes self-management education.

METHODS

To elicit the main themes explaining non-attendance, 15 semi-structured interviews were conducted with persons referred to, but not attending, self-management education. Systematic text condensation was applied to code and generate themes subsequently organised under individual and organisational factors.

RESULTS

Individual (illness, lack of perceived benefit) and organisational factors relating to schedule (four whole days, time of day, notification) and content (supermarket visit) were cited as reasons for non-attendance.

CONCLUSIONS

In this study, patients cited both individual and organisational factors as explaining non-attendance at type 2 diabetes self-management education. Further studies should take into account the importance of timing and of tailoring schedules and content to individuals' life situations and resources. As organisational factors are likely to vary across programmes and settings, more case studies are needed to further elucidate the dynamic relationship between individual and organisational factors to explain non-attendance at type 2 diabetes self-management education.

Evaluating Primary Care Physician Performance in Diabetes Glucose Control

This study demonstrates that it is possible to identify primary care physicians (PCPs) who perform better or worse than expected in managing diabetes. Study subjects were 14 033 adult diabetics and their 133 PCPs. Logistic regression was used to predict the odds that a patient would have uncontrolled diabetes (defined as HbA1c ≥8%) based on patient-level characteristics alone. A second model predicted diabetes control from physician-level identity and characteristics alone. A third model combined the patient- and physician-level models using hierarchical logistic regression. Physician performance is calculated from the difference between the expected and observed proportions of patients with uncontrolled diabetes. After adjusting for important patient characteristics, PCPs were identified who performed better or worse than expected in managing diabetes. This strategy can be used to characterize physician performance in other chronic conditions. This approach may lead to new insights regarding effective and ineffective treatment strategies.

The JDRF CCTN CGM TIME Trial: Timing of Initiation of continuous glucose Monitoring in Established pediatric type 1 diabetes: study protocol, recruitment and baseline characteristics

BACKGROUND

Continuous glucose monitoring (CGM) has been shown to improve glucose control in adults with type 1 diabetes. Effectiveness of CGM is directly linked with CGM adherence, which can be challenging to maintain in children and adolescents. We hypothesize that initiating CGM at the same time as starting insulin pump therapy in pump naïve children and adolescents with type 1 diabetes will result in greater CGM adherence and effectiveness compared to delaying CGM introduction by 6 months, and that this is related to greater readiness for making behaviour change at the time of pump initiation.

METHODS/DESIGN

The CGM TIME Trial is a multicenter randomized controlled trial. Eligible children and adolescents (5-18 years) with established type 1 diabetes were randomized to simultaneous initiation of pump (Medtronic Veo©) and CGM (Enlite©) or to standard pump therapy with delayed CGM introduction. Primary outcomes are CGM adherence and hemoglobin A1C at 6 and 12 months post pump initiation. Secondary outcomes include glycemic variability, stage of readiness, and other patient-reported outcomes with follow-up to 24 months. 144 (95%) of the 152 eligible patients were enrolled and randomized. Allowing for 10% withdrawals, this will provide 93% power to detect a between group difference in CGM adherence and 86% power to detect a between group difference in hemoglobin A1C. Baseline characteristics were similar between the treatment groups. Analysis of 12 month primary outcomes will begin in September 2014.

DISCUSSION

The CGM TIME Trial is the first study to examine the relationship between timing of CGM initiation, readiness for behaviour change, and subsequent CGM adherence in pump naïve children and adolescents. Its findings will advance our understanding of when and how to initiate CGM in children and adolescents with type 1 diabetes.

TRIAL REGISTRATION

ClinicalTrial.gov NCT01295788. Registered 14 February 2011.

Patient Satisfaction and it's Relation to Diabetic Control in a Primary Care Setting

Context:

Patient satisfaction is of increasing importance and recognized as an important indicator for quality of care. It is influenced by the patients, physicians and practice's characteristics. The literature on diabetes has increasingly focused on the quality of care and its measurement. The relationship between the quality of diabetes care and patient satisfaction is poorly understood and it requires further elaboration.

Aims:

The aim of this study is to Identify the underlying factors influencing patient's satisfaction with the diabetes care, to assess whether comprehensive diabetes management that provided in diabetic clinic improves satisfaction and glycemic control.

Settings and Design:

Cross-sectional study Family Medicine and Diabetic Clinics at King Abdul-Aziz Medical City.

Materials and Methods:

A total of 230 type two diabetic patients attending their follow-up were requested to fill the questionnaire. The questionnaire identified patients, doctors and practice related factors.

Statistical Analysis Used:

SPSS 16 with appropriate statistical test.

Results:

The response rate was 85%. Mean hemoglobin A1c (HbA1c) level was 0.087 ± 0.020. Around half of the patients were having high satisfaction rate of (>60%). Doctor's communication ranked the highest satisfaction level among other factors. However, no association between satisfaction with other patient's characteristics and HbA1c.

Conclusions:

Physicians play a major role in promoting higher level of satisfaction by good communication with their patients. More efforts are needed to improve certain aspects of diabetic care such as: Patient's education and periodic physical examination. Although the present study did not show any association between satisfaction and important outcome like HbA1c, more studies are needed to explore such complex relationship. To obtain more significant results a bigger sample size might be needed.

Glycemic control among patients with type 2 diabetes who initiate basal insulin: a retrospective cohort study

OBJECTIVE

To examine changes in glycemic control for patients with type 2 diabetes mellitus (T2DM) after initiation of basal insulin and factors associated with improved glycemic control.

METHODS

An analysis of retrospective medical records of patients with T2DM was examined using Humedica's electronic medical records database (January 2007-August 2012). Patients with T2DM, initiating basal insulin, age ≥ 21 years, with a recorded HbA1c test in both the 1 year prior and the 2 years post-initiation were included. A multivariate regression examined factors associated with changes in glycemic control. Logistic regressions examined factors associated with improvements or worsening of glycemic control, compared to relatively unchanged glycemic control.

RESULTS

Many (14,457) individuals met the inclusion-exclusion criteria. Multivariate analyses revealed that older age (p < 0.0001), residence in the Western region of the US (vs South) (p < 0.0001), Medicare insurance vs Medicaid or being uninsured (p = 0.0138), and higher household income (p = 0.0065) were associated with improved glycemic control. Patients diagnosed with comorbid peripheral vascular disease (p = 0.0072), cancer (p = 0.0019), obesity (p = 0.0002), moderate (p = 0.0103), and severe chronic kidney disease (p < 0.0001), or end-stage renal disease (p = 0.0075) in the pre-period were found to have significantly improved glycemic control in the post-period. Use of prandial insulin (p = 0.0087), pre-mix insulin (p = 0.0003) in the pre-period, a higher pre-period HbA1c score (p < 0.0001), and longer duration between pre-period and post-period HbA1c testing (p < 0.0001) were significantly associated with higher HbA1c levels in the post-period.

LIMITATIONS

Analyses rely on electronic medical records which cannot capture patient healthcare utilization occurring outside of the data capture system. Analyses do not control for insulin dosage or type of basal insulin prescribed.

CONCLUSIONS

Among patients with T2DM treated with basal insulin, a number of factors may influence glycemic outcomes. These findings suggest a role for a more personalized approach to the treatment of patients with T2DM.

Predictors of effects of lifestyle intervention on diabetes mellitus type 2 patients

The main aim of the study was to identify predictors of the effects of lifestyle intervention on diabetes mellitus type 2 patients by means of multivariate analysis. Data from a previously published randomised clinical trial, which compared the effects of a rehabilitation programme including standardised education and physical training sessions in the municipality's health care centre with the same duration of individual counseling in the diabetes outpatient clinic, were used. Data from 143 diabetes patients were analysed. The merged lifestyle intervention resulted in statistically significant improvements in patients' systolic blood pressure, waist circumference, exercise capacity, glycaemic control, and some aspects of general health-related quality of life. The linear multivariate regression models explained 45% to 80% of the variance in these improvements. The baseline outcomes in accordance to the logic of the regression to the mean phenomenon were the only statistically significant and robust predictors in all regression models. These results are important from a clinical point of view as they highlight the more urgent need for and better outcomes following lifestyle intervention for those patients who have worse general and disease-specific health.

Monitoring modifiable cardiovascular risk in type 2 diabetes care in general practice: the use of an aggregated z-score

BACKGROUND

Because many patients in usual care reach the diabetes treatment goals, it may be more efficacious to focus quality improvement efforts on those general practice populations requiring additional support. We therefore developed a tool based on a composite end point considering blood pressure, lipids, and glycaemia.

METHODS

We created an aggregated z(A)-score, calculated as the average of 3 z-scores testing whether the mean practice values of hemoglobin A1c, low density lipoprotein cholesterol, and systolic blood pressure are significantly higher than the corresponding ADA-target (respectively 7%, 100 mg/dL, and 130 mm Hg). This score was used with 100 general practitioners who participated in a Quality Improvement Program. We defined the cut-off value (COV) to determine "Practices Requiring Support" (z(A) <COV) using a receiver's operating characteristics curve with the mean practice CHD risk as gold standard. To further test the z-score validity, we calculated the correlation coefficient between the z-score and the mean practice CHD risk and the improvement in the z-score after the Quality Improvement Program.

RESULTS

The COV was -1.22 and was valid to discriminate between practices at higher risk from practices at lower CHD risk (24% +/- 4% vs. 19% +/- 4%). The correlation coefficient was -0.515 (P = 0.001). The average z-score increased from -1.21 +/- 0.97 at baseline to 0.49 +/- 1.01 after the intervention (P < 0.001).

CONCLUSION

This scoring system is useful to picture practice populations with diabetes who are at high cardiovascular risk because of modifiable risk factors. Although the unadjusted z-score cannot be used to compare physicians, this technique can be used to evaluate improvement efforts over time.

Factors associated with glycemic control after an inpatient program

In this study, we investigated the factors predicting poor glycemic control after an inpatient program. Using the hospital database from April 1999 to May 2003, we retrospectively identified patients with type 2 diabetes mellitus and hemoglobin A(1C) (HbA(1C)) of at least 8.0% at the time of admission for an inpatient program. In the primary analysis, factors potentially related to poor glycemic control (HbA(1C) > or =7.0%) at 6 months after admission were investigated. Stepwise multivariate regression analysis identified the duration of diabetes (odds ratio, 2.43; 95% confidence interval [CI], 1.54-3.82; P < .001), period from the first attendance at our hospital until admission (odds ratio, 1.60; 95% CI, 1.01-2.54; P = .047), and number of admissions (odds ratio, 2.28; 95% CI, 1.36-3.82; P = .002) as predictors of poor glycemic control. In the secondary analysis, factors related to poor glycemic response (an absolute decrease of HbA(1C) by <1.5% from the baseline) at 6 months after admission were investigated. Stepwise multivariate regression analysis identified the duration of diabetes (odds ratio, 2.17; 95% CI, 1.19-3.93; P = .011), period from the first attendance at our hospital until admission (odds ratio, 2.17; 95% CI, 1.43-3.29; P < .001), treatment of diabetes at discharge (oral hypoglycemic agents: odds ratio, 2.52; 95% CI, 1.15-5.51; P = .021; insulin: odds ratio, 4.44; 95% CI, 1.96-10.07; P < .001), baseline HbA(1C) (odds ratio, 0.44; 95% CI, 0.37-0.53; P < .001), and addition of new medications (odds ratio, 0.41; 95% CI, 0.27-0.62; P < .001) as predictors of poor glycemic control.

Patient factors associated with hemoglobin A1C change with pioglitazone as adjunctive therapy in type 2 Diabetes Mellitus

Objective

To identify patient factors associated with change in hemoglobin A1C (A1C) with adjunct pioglitazone therapy in routine clinical practice.

Methods

This was a retrospective analysis of adult type 2 diabetes mellitus patients in a health maintenance organization setting who were newly-initiated on pioglitazone between January 2002 and December 2005. Eligible patients were receiving at least one other oral antihyperglycemic medication prior to initiating pioglitazone and maintained a stable dose of pioglitazone for 90 days. Data on eligible patients’ characteristics, pharmacy purchases, comorbidities, and A1C measurement 90 days prior to the pioglitazone purchase date (baseline) and 90 days after achieving a stable dose (follow-up) were obtained from electronic records. Multivariate regression modeling was used to assess factors independently associated with: 1) absolute change in A1C, 2) achieving a ≥1 percentage point decrease in A1C, and 3) achieving an A1C<7%.

Results

Baseline and follow-up A1Cs were available for 128 patients. At baseline, mean age was 65 years, 38% were female, mean A1C was 8.4%, and 74% had an A1C>8%. At follow-up, the mean A1C change was -1.2 percentage points (interquartile range= -0.4, -2.1), 59% achieved a ≥1 unit decrease in A1C, and 44% achieved an A1C<7%. Independent predictors in all models were baseline A1C and time (in days) between baseline and follow-up A1C measurements (p<0.05).

Conclusions

Adjunct pioglitazone therapy in routine clinical practice was associated with clinically meaningful reductions in A1C levels. Patients with higher baseline A1C achieved the greatest absolute reduction in A1C but were less likely to achieve levels <7%.

Can we predict future improvement in glycaemic control?

AIMS

To determine the factors responsible for poor glycaemic control in diabetes and whether any such factors are associated with likely improvement in glycaemic control.

METHODS

A prospective cohort study of 130 diabetic patients with poor glycaemic control (HbA(1c )> or = 10.0%) with 1-year follow-up in a teaching hospital Diabetes Clinic. Changes in HbA(1c) were measured after 1 year.

RESULTS

Poor glycaemic control was attributed to one of 15 possible causes. Those cases due to recent diagnosis of diabetes, inadequate treatment with diet, oral glucose-lowering agents or insulin, exacerbation of co-existent medical problems, recent stressful life-events and missed clinic appointments were all associated with significant improvement in HbA(1c) at 12 months. Patients with low mood or alcohol excess, inadequate blood glucose monitoring, poor exercise/sedentary lifestyle, refusal to take tablets or underdosing and refusal to take insulin at all or to increase the dose were all associated with continuing poor glycaemic control at 12 months. The patients were divided almost equally between the two groups.

CONCLUSIONS

In patients with poor glycaemic control, it is possible by simple features identified at clinic to predict which individuals are likely to show improvement in control and which will not. These findings have not been reported previously and suggest that about half of individuals with poor control will improve within our current diabetes clinic practice. Additional strategies will be required to address those individuals who are not likely to respond.

Factors influencing disease self-management among veterans with diabetes and poor glycemic control

SPECIFIC AIM

Although the Department of Veterans Affairs (VA) has made significant organizational changes to improve diabetes care, diabetes self-management has received limited attention. The purpose of this study is to assess factors influencing diabetes self-management among veterans with poorly controlled diabetes.

METHODS

Surveys were mailed to patients with type 2 diabetes and a HbA1c of 8% or greater who attended 1 of 2 VA Medical Centers in Washington State (n = 1,286). Validated survey instruments assessed readiness to change, self-efficacy, provider advice, and diabetes self-care practices.

RESULTS

Our response rate was 56% (n = 717). Most respondents reported appropriate advice from physicians regarding physical activity, nutrition, and glucose monitoring (73%, 92%, and 98%, respectively), but many were not ready to change self-management behaviors. Forty-five percent reported non-adherence to medications, 42% ate a high-fat diet, and only 28% obtained either moderate or vigorous physical activity. The mean self-efficacy score for diabetes self-care was low and half of the sample reported readiness to change nutrition (52%) or physical activity (51%). Individuals with higher self-efficacy scores were more likely to adhere to medications, follow a diabetic meal plan, eat a lower fat diet, have higher levels of physical activity, and monitor their blood sugars (P < .001 for all).

CONCLUSIONS

Although veterans with poor diabetes control receive appropriate medical advice, many were not sufficiently confident or motivated to make and maintain self-management changes. Targeted patient-centered interventions may need to emphasize increasing self-efficacy and readiness to change to further improve VA diabetes outcomes.