Real-world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes

The experience of blood glucose monitoring in people with type 2 diabetes mellitus (T2DM)

BACKGROUND

Finger prick blood glucose (BG) monitoring remains a mainstay of management in people with type 2 diabetes (T2DM) who take sulphonylurea (SU) drugs or insulin. We recently examined patient experience of BG monitoring in people with type 1 diabetes (T1DM). There has not been any recent comprehensive assessment of the performance of BG monitoring strips or the patient experience of BG strips in people with T2DM in the UK.

METHODS

An online self-reported questionnaire containing 44 questions, prepared following consultation with clinicians and patients, was circulated to people with T2DM. 186 responders provided completed responses (25.5% return rate). Fixed responses were coded numerically (eg not confident = 0 fairly confident = 1).

RESULTS

Of responders, 84% were treated with insulin in addition to other agents. 75% reported having had an HbA1c check in the previous 6 months. For those with reported HbA1c ≥ 65 mmol/mol, a majority of people (70%) were concerned or really concerned about the shorter term consequences of running a high HbA1c This contrasted with those who did not know their recent HbA1c, of whom only 33% were concerned/really concerned and those with HbA1c <65 mmol/mol of whom 35% were concerned. Regarding BG monitoring/insulin adjustment, only 25% of responders reported having sufficient information with 13% believing that the accuracy and precision of their BG metre was being independently checked. Only 9% recalled discussing BG metre accuracy when their latest metre was provided and only 7% were aware of the International Standardisation Organisation (ISO) standards for BG metres. 77% did not recall discussing BG metre performance with a healthcare professional.

CONCLUSION

The group surveyed comprised engaged people with T2DM but even within this group there was significant variation in (a) awareness of shorter term risks, (b) confidence in their ability to implement appropriate insulin dosage (c) awareness of the limitations of BG monitoring technology. There is clearly an area where changes in education/support would benefit many.

Investigation of polycystic ovarian syndrome: variation in practice and impact on the speed of diagnosis

Objective

Accurate diagnosis of polycystic ovarian syndrome (PCOS) enables clinical interventions/cardiometabolic risk factor management. Diagnosis can take over 2 years and multiple clinician contacts. We examined patterns of PCOS-associated biochemical investigations following initial consultation prior to pelvic ultrasound scan (USS).

Methods

We determined in 206 women (i) the range of different biochemical test panels used in the diagnosis of PCOS in primary/secondary care prior to USS relative to national guidance in the UK and (ii) the relation between testing patterns and time to USS to highlight potential delays introduced by inappropriate testing.

Results

In these 206 women, 47 different test combinations were requested at initial venepuncture; only 7 (3%) had the test panel suggested in UK guidance (follicle-stimulating hormone/luteinizing hormone/testosterone/sex hormone-binding globulin/prolactin). The number of tests performed prior to USS varied from one test to all seven tests. There was an inverse relation between the number of biochemistry tests requested at initial venepuncture episode and 'time to scan'. Those who had <3 tests had a significantly longer time from first request to USS (median 70 days) than those with 3-7 tests (median 40 days; P = 0.002). One venepuncture episode prior to USS was associated with shorter 'time to scan' (median 29 days) than those with 2-4 episodes (median 255 days; P < 0.001).

Conclusion

There was no identifiable pattern to biochemical investigations requested as part of the initial diagnostic evaluation in women with suspected PCOS. We recommend standardization of the initial biochemical panel of analytes for PCOS workup, with incorporation into hospital/general practice ordering software systems.

Applying Parkes Grid Method to Evaluate Impact of Variation in Blood Glucose Monitoring (BGM) Strip Accuracy Performance in Type 1 Diabetes Highlights the Potential for Amplification of Imprecision With Less Accurate BGM Strips

BACKGROUND

The National Health Service spends £170 million on blood glucose monitoring (BGM) strips each year and there are pressures to use cheaper less accurate strips. Technology is also being used to increase test frequency with less focus on accuracy.Previous modeling/real-world data analysis highlighted that actual blood glucose variability can be more than twice blood glucose meter reported variability (BGMV). We applied those results to the Parkes error grid to highlight potential clinical impact.

METHOD

BGMV is defined as the percent of deviation from reference that contains 95% of results. Four categories were modeled: laboratory (<5%), high accuracy strips (<10%), ISO 2013 (<15%), and ISO 2003 (<20%) (includes some strips still used).The Parkes error grid model with its associated category of risk including "alter clinical decision" and "affect clinical outcomes" was used, with the profile of frequency of expected results fitted into each BGM accuracy category.

RESULTS

Applying to single readings, almost all strip accuracy ranges derived in a controlled setting fell within the category: clinically accurate/no effect on outcomes areas.However modeling the possible blood glucose distribution in more detail, 30.6% of longer term results of the strips with current ISO accuracy would fall into the "alter clinical action" category. For previous ISO strips, this rose to 44.1%, and for the latest higher accuracy strips, this fell to 12.8%.

CONCLUSION

There is a minimum standard of accuracy needed to ensure that clinical outcomes are not put at risk. This study highlights the potential for amplification of imprecision with less accurate BGM strips.

People with Type Diabetes Mellitus (T1DM) self-reported views on their own condition management reveal links to potentially improved outcomes and potential areas for service improvement

BACKGROUND

The self-management of type 1 diabetes (T1DM) has moved forward in many areas over the last 40 years. Our study asked people with T1DM what is their experience of blood glucose (BG) monitoring day to day and how this influences decisions about insulin dosing.

METHODS

An on-line self-reported questionnaire containing 44 questions prepared after consultation with clinicians and patients was circulated to people with T1DM 116 responders provided completed responses. Fixed responses were allocated specific values (e.g. not confident = 0 fairly confident = 1). Multivariate regression analysis was carried out. Only those 5 factors with p-value <0.05 were retained.

RESULTS

59% of respondents were >50 years old and 66% had diabetes for >20 years, with 63% of patients reporting HbA1c results ≤8% or 64 mmol/mol. Findings included; 75% used only 1 m; 56% had used the same meter for ≥3 years; 10% had tried flash monitors; 47% were concerned about current BG level; 85% were concerned about long-term impact of higher BG. 72% of respondents keep BG level high to avoid hypoglycaemia; 25% used ≥7 mmol/L as pre-meal BG target to calculate dose; 65% were concerned they might be over/under-dosing; 83% did not discuss accuracy when choosing meter. However 85% were confident in their meter's performance. The factors that linked to LOWER HbA1c included LESS units of basal insulin (p < 0.001), HIGHER number of daily BG tests (p = 0.008), LOWER bedtime blood glucose (p = 0.009), HIGHER patient's concern over long-term impact of high BG (BG) (p < 0.009 but LOWER patient's concern over current BG values (p = 0.009). The final statistical model could explain 41% of the observed variation in HbA1c.

CONCLUSION

Many people still run their BG high to avoid hypoglycaemia. Concern about the longer-term consequences of suboptimal glycaemic control was associated with a lower HbA1c and is an area to explore in the future when considering how to help people with T1DM.

Real-world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes

AIMS/HYPOTHESIS

Our aim was to quantify the impact of Blood Glucose Monitoring Strips variability (BGMSV) at GP practice level on the variability of reported glycated haemoglobin (HbA1cV) levels.

METHODS

Overall GP Practice BGMSV and HbA1cV were calculated from the quantity of main types of BGMS being prescribed combined with the published accuracy, as % results within ±% bands from reference value for the selected strip type. The regression coefficient between the BGMSV and HbA1cV was calculated. To allow for the aggregation of estimated three tests/day over 13 weeks (ie, 300 samples) of actual Blood Glucose (BG) values up to the HbA1c, we multiplied HbA1cV coefficient by √300 to estimate an empirical value for impact of BGMSV on BGV.

RESULTS

Four thousand five hundred and twenty-four practice years with 159 700 T1DM patient years where accuracy data were available for more than 80% of strips prescribed were included, with overall BGMSV 6.5% and HbA1c mean of 66.9 mmol/mol (8.3%) with variability of 13 mmol/mol equal to 19% of the mean. At a GP practice level, BGMSV and HbA1cV as % of mean HbA1c (in other words, the spread of HbA1c) were closely related with a regression coefficient of 0.176, P < 0.001. Thus, greater variability in the BGMS at a GP practice level resulted in a greater spread of HbA1C readings in T1DM patients. Applying this factor for BGMS to the national ISO accepted standard where 95% results must be ≤±15% from reference, revealed that for BG, 95% results would be ≤±45% from the reference value. Thus, the variation in BG is three times that of the BGMS. For a patient with BG target @10 mmol/L using the worst performing ISO standard strips, on 1/20 occasions (average 1/week) actual blood glucose value could be >±4.5 mmol/L from target, compared with the best performing BGMS with BG >±2.2 mmol/L from reference on 1/20 occasions.

CONCLUSION

Use of more variable/less accurate BGMS is associated both theoretically and in practice with a larger variability in measured BG and HbA1c, with implications for patient confidence in their day-to-day monitoring experience.