The frequency of testing for glycated haemoglobin, HbA1c, is linked to the probability of achieving target levels in patients with suboptimally controlled diabetes mellitus

Evaluation of Cardiovascular Disease Risk in Patients with Type 2 Diabetes Mellitus Using Clinical Laboratory Markers

Background: Cardiovascular diseases (CVD) are the main cause of death in the population with diabetes mellitus. This study purposed to determine clinical laboratory markers that might be correlated with the risk of CVD in individuals with type 2 diabetes mellitus (T2DM). Methods: Using data from the Clinical Center of the University of Debrecen from 2016 to 2020, we assessed cardiovascular risk in 5593 individuals with T2DM over a five-year follow-up period. There were 347 new cases of acute myocardial infarction (AMI) and stroke during the period. Following the stratification of these individuals into two groups according to the diagnosis of these CVDs until 2020, the risk of these CVDs was assessed through the utilization of the Chi-square test and Cox proportional hazards regression. Results: The findings of the Cox proportional hazards regression model showed that the number of HbA1C measurements per year (HR = 0.46, 95% CI 0.31-0.7), decreased levels of estimated glomerular filtration rate (eGFR) (HR = 1.6, 95% CI 1.04-2.47), and elevated triglyceride levels (HR = 1.56, 95% CI 1.06-2.29) were correlated with CVD in patients with T2DM. The area under the curve (AUC) was increased from 0.557 (95% CI 0.531-0.582) to 0.628 (95% CI 0.584-0.671) after the inclusion of the laboratory variables into the model showing improved discrimination for AMI and stroke. Conclusions: These findings indicated that eGFR, triglyceride, and the number of HbA1C per year are correlated with AMI and stroke in patients with T2DM.

Effectiveness of diabetes shared care program on metabolic indicators of people with type 2 diabetes: A systematic review and meta-analysis

AIMS

To explore the effectiveness of the Taiwanese Diabetes Shared Care Program (DSCP) on improving the metabolic indicators of people with type 2 diabetes.

METHODS

Relevant studies published between January 2002 and August 2021 were retrieved from Chinese- and English-language electronic databases, including PubMed, MEDLINE, CINAHL, ProQuest, Cochrane Library, Airiti Library, and Taiwan Periodical Literature System. After screening, studies that met inclusion criteria were included in the literature review. RevMan 5.4 was employed for a meta-analysis.

RESULTS

Ten studies published between 2007 and 2021 were included in the systematic review, with nine of them contributing to the meta-analysis. In total, 1506 and 1388 participants were classified into DSCP and non-DSCP groups, respectively, for the meta-analysis. The results revealed that the DSCP significantly improved glycated hemoglobin levels (mean difference [MD]: -0.50, 95% Confidence Interval [CI]: -0.83 to -0.17) and body weights (MD: -0.83, 95% CI: -1.29 to -0.38) within 1-year follow-up. However, it did not show significant improvement in other metabolic indicators.

CONCLUSIONS

Taiwanese DSCP led to improvements in glycated hemoglobin levels and body weights among people with type 2 diabetes. This study suggests that people with diabetes and health-care institutions should consider participating in the DSCP.

The Effect of the COVID-19 Pandemic on HbA1c Testing: Prioritization of High-Risk Cases and Impact of Social Deprivation

INTRODUCTION

Studies show that the COVID-19 pandemic disproportionately affected people with diabetes and those from disadvantaged backgrounds. During the first 6 months of the UK lockdown, > 6.6 M glycated haemoglobin (HbA1c) tests were missed. We now report variability in the recovery of HbA1c testing, and its association with diabetes control and demographic characteristics.

METHODS

In a service evaluation, we examined HbA1c testing across ten UK sites (representing 9.9% of England's population) from January 2019 to December 2021. We compared monthly requests from April 2020 to those in the equivalent 2019 months. We examined effects of (i) HbA1c level, (ii) between-practice variability, and (iii) practice demographics.

RESULTS

In April 2020, monthly requests dropped to 7.9-18.1% of 2019 volumes. By July 2020, testing had recovered to 61.7-86.9% of 2019 levels. During April-June 2020, we observed a 5.1-fold variation in the reduction of HbA1c testing between general practices (12.4-63.8% of 2019 levels). There was evidence of limited prioritization of testing for patients with HbA1c > 86 mmol/mol during April-June 2020 (4.6% of total tests vs. 2.6% during 2019). Testing in areas with the highest social disadvantage was lower during the first lockdown (April-June 2020; trend test p < 0.001) and two subsequent periods (July-September and October-December 2020; both p < 0.001). By February 2021, testing in the highest deprivation group had a cumulative fall in testing of 34.9% of 2019 levels versus 24.6% in those in the lowest group.

CONCLUSION

Our findings highlight that the pandemic response had a major impact on diabetes monitoring and screening. Despite limited test prioritization in the > 86 mmol/mol group, this failed to acknowledge that those in the 59-86 mmol/mol group require consistent monitoring to achieve the best outcomes. Our findings provide additional evidence that those from poorer backgrounds were disproportionately disadvantaged. Healthcare services should redress this health inequality.

Assessment of the effect of the COVID-19 pandemic on UK HbA1c testing: implications for diabetes management and diagnosis

AIMS

The COVID-19 pandemic, and the focus on mitigating its effects, has disrupted diabetes healthcare services worldwide. We aimed to quantify the effect of the pandemic on diabetes diagnosis/management, using glycated haemoglobin (HbA1c) as surrogate, across six UK centres.

METHODS

Using routinely collected laboratory data, we estimated the number of missed HbA1c tests for 'diagnostic'/'screening'/'management' purposes during the COVID-19 impact period (CIP; 23 March 2020 to 30 September 2020). We examined potential impact in terms of: (1) diabetes control in people with diabetes and (2) detection of new diabetes and prediabetes cases.

RESULTS

In April 2020, HbA1c test numbers fell by ~80%. Overall, across six centres, 369 871 tests were missed during the 6.28 months of the CIP, equivalent to >6.6 million tests nationwide. We identified 79 131 missed 'monitoring' tests in people with diabetes. In those 28 564 people with suboptimal control, this delayed monitoring was associated with a 2-3 mmol/mol HbA1c increase. Overall, 149 455 'screening' and 141 285 'diagnostic' tests were also missed. Across the UK, our findings equate to 1.41 million missed/delayed diabetes monitoring tests (including 0.51 million in people with suboptimal control), 2.67 million screening tests in high-risk groups (0.48 million within the prediabetes range) and 2.52 million tests for diagnosis (0.21 million in the pre-diabetes range; ~70 000 in the diabetes range).

CONCLUSIONS

Our findings illustrate the widespread collateral impact of implementing measures to mitigate the impact of COVID-19 in people with, or being investigated for, diabetes. For people with diabetes, missed tests will result in further deterioration in diabetes control, especially in those whose HbA1c levels are already high.

HbA1c and biomarkers of diabetes mellitus in Clinical Chemistry and Laboratory Medicine: ten years after

Since its discovery in the late 1960s, HbA_1c has proven to be a major biomarker of diabetes mellitus survey and diagnosis. Other biomarkers have also been described using classical laboratory methods or more innovative, non-invasive ones. All biomarkers of diabetes, including the historical glucose assay, have well-controlled strengths and limitations, determining their indications in clinical use. They all request high quality preanalytical and analytical methodologies, necessitating a strict evaluation of their performances by external quality control assessment trials. Specific requirements are needed for point-of-care testing technologies. This general overview, which describes how old and new tools of diabetes mellitus biological survey have evolved over the last decade, has been built through the prism of papers published in Clinical Chemistry and Laboratory Medicine during this period.

Economic Hardships from COVID-19 and its Association with Socioeconomic Factors and Diabetes Management Indicators among Diabetic Residents: A Cross-Sectional Study

Aims

The three objectives of this study were to determine the economic hardships of COVID-19 pandemic, their socio-economic predictors, and their association with diabetes management indicators in three cities in a middle-income country.

Methods

A community-based cross-sectional survey of 309 people with diabetes aged 34–85 was carried out in 10 communities during July and August 2020. Face-to-face surveys were conducted by trained community physicians. Economic hardship was assessed by income loss and “financial toxicity” during the COVID-19 pandemic, where financial toxicity was defined as experiencing economic difficulties in accessing diabetes management resources. Indicators of diabetes management was assessed by blood glucose and Hemoglobin A1c (HbA1c) monitoring frequency.

Results

Among all respondents, 38.5% reported having income loss, and 15.5% experiencing financial toxicity during the pandemic. Younger and self-employed people living suburban areas were more likely to experience income loss. Similarly, suburban area residency and lower household income were associated with financial toxicity. Patients with financial toxicity were less likely to monitor HbA1c in the past three months (OR = 0.20; 95% CI, 0.07–0.48).

Conclusion

Diabetes management as indicated by less frequent HbA1c monitoring was associated with experiencing COVID-19 related financial toxicity. Our findings identified vulnerable groups in need of additional support for diabetes management.

Variability in Test Interval Is Linked to Glycated Haemoglobin (HbA1c) Trajectory over Time

AIMS

We previously showed that the glycated haemoglobin (HbA1c) testing frequency links to diabetes control. Here, we examine the effect of variability in test interval, adjusted for the frequency, on change in HbA1c (ΔHbA1c). Materials & Methods. HbA1c results were collected on 83,872 people with HbA1c results at baseline and 5 years (±3 months) later and ≥6 tests during this period. We calculated the standard deviation (SD) of test interval for each individual and examined the link between deciles of SD of the test interval and ΔHbA1c level, stratified by baseline HbA1c.

RESULTS

In general, less variability in testing frequency (more consistent monitoring) was associated with better diabetes control. This was most evident with moderately raised baseline HbA1c levels (7.0-9.0% (54-75 mmol/mol)). For example, in those with a starting HbA1c of 7.0-7.5% (54-58 mmol/mol), the lowest SD decile was associated with little change in HbA1c over 5 years, while for those with the highest decile, HbA1c rose by 0.4-0.6% (4-6 mmol/mol; p < 0.0001). Multivariate analysis showed that the association was independent of the age/sex/hospital site. Subanalysis suggested that the effect was most pronounced in those aged <65 years with baseline HbA1c of 7.0-7.5% (54-58 mmol/mol). We observed a 6.7-fold variation in the proportion of people in the top-three SD deciles across general practices.

CONCLUSIONS

These findings indicate that the consistency of testing interval, not the just number of tests/year, is important in maintaining diabetes control, especially in those with moderately raised HbA1c levels. Systems to improve regularity of HbA1c testing are therefore needed, especially given the impact of COVID-19 on diabetes monitoring.

Can we check serum lithium levels less often without compromising patient safety?

BACKGROUND

Lithium is viewed as the first-line long-term treatment for prevention of relapse in people with bipolar disorder.

AIMS

This study examined factors associated with the likelihood of maintaining serum lithium levels within the recommended range and explored whether the monitoring interval could be extended in some cases.

METHOD

We included 46 555 lithium rest requests in 3371 individuals over 7 years from three UK centres. Using lithium results in four categories (<0.4 mmol/L; 0.40-0.79 mmol/L; 0.80-0.99 mmol/L; ≥1.0 mmol/L), we determined the proportion of instances where lithium results remained stable or switched category on subsequent testing, considering the effects of age, duration of lithium therapy and testing history.

RESULTS

For tests within the recommended range (0.40-0.99 mmol/L categories), 84.5% of subsequent tests remained within this range. Overall, 3 monthly testing was associated with 90% of lithium results remaining within range, compared with 85% at 6 monthly intervals. In cases where the lithium level in the previous 12 months was on target (0.40-0.79 mmol/L; British National Formulary/National Institute for Health and Care Excellence criteria), 90% remained within the target range at 6 months. Neither age nor duration of lithium therapy had any significant effect on lithium level stability. Levels within the 0.80-0.99 mmol/L category were linked to a higher probability of moving to the ≥1.0 mmol/L category (10%) compared with those in the 0.4-0.79 mmol/L group (2%), irrespective of testing frequency.

CONCLUSION

We propose that for those who achieve 12 months of lithium tests within the 0.40-0.79 mmol/L range, the interval between tests could increase to 6 months, irrespective of age. Where lithium levels are 0.80-0.99 mmol/L, the test interval should remain at 3 months. This could reduce lithium test numbers by 15% and costs by ~$0.4 m p.a.

Effects of Patient-Initiated Visits on Patient Satisfaction and Clinical Outcomes in a Type 1 Diabetes Outpatient Clinic: A 2-Year Randomized Controlled Study

OBJECTIVE

We investigated the effects of replacing regular outpatient follow-up through prescheduled visits with patient-initiated visits on patient satisfaction and clinical variables of type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

A 24-month randomized controlled trial in which adults with T1D were allocated to either patient-initiated unlimited access to outpatient visits or usual care through regular prescheduled visits. The primary outcome was seven patient-reported experience measures of patient satisfaction focused on benefit of consultation and accessibility of the outpatient clinic. Secondary outcomes included clinical variables of diabetes and use of staff resources.

RESULTS

We enrolled 357 outpatients (intervention, n = 178; control, n = 179). After 24 months, participants in the intervention group experienced more benefit from consultations compared with baseline within groups (P < 0.05) and fewer unnecessary visits compared with control subjects (P < 0.05). Patient needs covered and satisfaction with the outpatient clinic were high and unchanged in both groups, and accessibility was increased (three questions, all P < 0.05). A calculated seven-item patient satisfaction sum score favored the intervention group over control subjects (P < 0.001). There were no significant changes in glycated hemoglobin (HbA1c), LDL, blood pressure, and complication status. The mean number of outpatient visits over 24 months (± SD) was lower in the intervention group compared with control subjects (4.4 ± 2.8 vs. 6.3 ± 2.7; P < 0.001), while the number of telephone contacts was higher (3.1 ±3.4 vs. 2.5 ± 3.2; P < 0.001).

CONCLUSIONS

Patient satisfaction remained high or improved with patient-initiated on-demand use of the diabetes outpatient clinic, with no decline in the quality of diabetes care, and a reduction in the use of staff resources.

Clinical Inertia and 2-Year Glycaemic Trajectories in Patients with Non-Newly Diagnosed Type 2 Diabetes Mellitus in Primary Care: A Retrospective Cohort Study

OBJECTIVE

To analyse diabetes treatment, treatment change and self-management behaviours in association with 2-year glycaemic trajectories in patients with non-newly diagnosed type 2 diabetes mellitus in Chinese primary care.

METHODS

This was an observational, multi-centre, longitudinal, retrospective cohort study. Clinical data of 4690 subjects were extracted from electronic medical records, including serial glycated haemoglobin A_1c (HbA_1c) measurements, antidiabetic medication records and compliance to exercise, diet, medications and self-monitoring of blood glucose (SMBG). Patterns of longitudinal HbA1c trajectories were identified using the percentage of HbA_1c measurements <7.5% from the second available HbA_1c measurement. Clinical relevance of the clusters was assessed through multivariable analysis.

RESULTS

Approximately half of the participants demonstrated good glycaemic control; of these, 34.5% demonstrated stable, good control, and 13.7% demonstrated relatively good control. About 16.2% demonstrated moderate control, and 35.6% demonstrated poor control. From the good to poor control groups, the percentage of subjects treated with insulin at baseline and during the follow-up period increased gradually, while the percentage of subjects adhering to exercise, diet, medications and SMBG decreased gradually. Compared with baseline, the adherence to exercise, diet, medications and SMBG improved significantly. Approximately 50% and 26% of subjects in the two poorest control groups, respectively, experienced treatment changes. After multivariable adjustments, baseline HbA_1c ≥7.5%, HbA_1c change ≥-0.5% from baseline to visit 1, insulin treatment, treatment change, poor adherence to diet, exercise, SMBG during the follow-up period and HbA_1c measurements <3 per year were significantly associated with poorer glycaemic control.

CONCLUSION

We identified four longitudinal HbA_1c trajectories in patients with non-newly diagnosed type 2 diabetes. Even if baseline HbA_1c is suboptimal, aggressive treatment changes, good adherence during the follow-up period, ≥3 HbA_1c measurements per year and reducing HbA_1c levels to a certain extent by the first follow-up visit were important for good, stable, long-term glycaemic control.

What methods are being used to create an evidence base on the use of laboratory tests to monitor long-term conditions in primary care? A scoping review

BACKGROUND

Studies have shown unwarranted variation in test ordering among GP practices and regions, which may lead to patient harm and increased health care costs. There is currently no robust evidence base to inform guidelines on monitoring long-term conditions.

OBJECTIVES

To map the extent and nature of research that provides evidence on the use of laboratory tests to monitor long-term conditions in primary care, and to identify gaps in existing research.

METHODS

We performed a scoping review-a relatively new approach for mapping research evidence across broad topics-using data abstraction forms and charting data according to a scoping framework. We searched CINAHL, EMBASE and MEDLINE to April 2019. We included studies that aimed to optimize the use of laboratory tests and determine costs, patient harm or variation related to testing in a primary care population with long-term conditions.

RESULTS

Ninety-four studies were included. Forty percent aimed to describe variation in test ordering and 36% to investigate test performance. Renal function tests (35%), HbA1c (23%) and lipids (17%) were the most studied laboratory tests. Most studies applied a cohort design using routinely collected health care data (49%). We found gaps in research on strategies to optimize test use to improve patient outcomes, optimal testing intervals and patient harms caused by over-testing.

CONCLUSIONS

Future research needs to address these gaps in evidence. High-level evidence is missing, i.e. randomized controlled trials comparing one monitoring strategy to another or quasi-experimental designs such as interrupted time series analysis if trials are not feasible.

Sociodemographic factors associated with HbA1c variability in type 2 diabetes: a prospective exploratory cohort study

BACKGROUND

The associations between sociodemographic factors and HbA1c variability in type 2 diabetes are not yet established. Examining group differences in HbA1c variability may help identify patient characteristics related to diabetes management. The present study examined differences in baseline HbA1c and HbA1c variability between groups with regard to sex, level of education, civil status, age, and BMI, in a sample of individuals with type 2 diabetes.

METHODS

The study was a prospective exploratory cohort study. Differences in HbA1c variability between sociodemographic groups were analyzed in 158 individuals. HbA1c variability was assessed as the standard deviation (SD) and coefficient of variation (CV) over five measured points, and a questionnaire was used to assess sociodemographic factors.

RESULTS

The results showed significantly higher HbA1c variability in men compared to women (mean difference 1.44 mmol/mol [95% CI: 0.58 to 2.31]), and significantly higher HbA1c variability in individuals with a BMI characterized as obese compared to individuals with a BMI characterized as normal weight (mean difference 1.56 mmol/mol [95% CI: 0.25 to 2.88]). There were no significant associations between HbA1c variability and civil status or education.

CONCLUSIONS

Men and individuals with obesity may be more vulnerable to future diabetic complications than other groups, since they have greater long-term glycemic variability.